{"id":12072,"date":"2026-06-25T10:41:31","date_gmt":"2026-06-25T15:41:31","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/annual\/?page_id=12072"},"modified":"2026-08-24T08:55:07","modified_gmt":"2026-08-24T13:55:07","slug":"plenaries-keynotes","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/annual\/plenaries-keynotes\/","title":{"rendered":"Plenaries &amp; Keynotes"},"content":{"rendered":"<!--themify_builder_content-->\n<div id=\"themify_builder_content-12072\" data-postid=\"12072\" class=\"themify_builder_content themify_builder_content-12072 themify_builder tf_clear\">\n                    <div  data-zoom-bg=\"desktop\" data-css_id=\"1gup925\" data-lazy=\"1\" class=\"module_row themify_builder_row fullwidth_row_container tb_1gup925 tb_first tf_w\">\n            <span  class=\"builder_row_cover tf_abs\" data-lazy=\"1\"><\/span>            <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_cjnk925 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_1cxl925   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h1 class=\"page-title\" style=\"text-align: left\">Plenaries &amp; Keynotes<\/h1>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module menu -->\n<div  class=\"module module-menu tb_fa9j925  mobile-menu-slide\" data-menu-style=\"mobile-menu-slide\" data-menu-breakpoint=\"0\" data-element-id=\"tb_fa9j925\" data-lazy=\"1\">\n        \n    <div class=\"module-menu-container\"><ul id=\"menu-plenaries\" class=\"ui tf_clearfix nav tf_rel tf_scrollbar  tb_default_color shadow\"><li id=\"menu-item-12264\" class=\"menu-item-custom-12264 menu-item menu-item-type-custom menu-item-object-custom menu-item-12264\"><a href=\"#plenaries\">Plenaries<\/a><\/li>\n<li id=\"menu-item-12265\" class=\"menu-item-custom-12265 menu-item menu-item-type-custom menu-item-object-custom menu-item-12265\"><a href=\"#keynotes\">Keynotes<\/a><\/li>\n<\/ul><\/div>        <style>\n            .mobile-menu-module[data-module=\"tb_fa9j925\"]{\n                right:-300px            }\n        <\/style>\n    <\/div>\n<!-- \/module menu -->\n        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"plenaries\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-plenaries tb_iuqe919 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_04bz919 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_o16c919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h2>Plenaries<\/h2>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module text -->\n<div  class=\"module module-text tb_suvx919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><strong>SUNDAY, November 1, 9:30-10:45AM<\/strong><\/h3>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"jordan\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-jordan tb_d432919 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_kvec919 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_7ohl919\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-1 tb_vmtl919 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_rhs7919 image-top bordered  tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" width=\"293\" height=\"293\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Michael-Jordan.jpg\" class=\"wp-post-image wp-image-12082\" title=\"MICHAEL JORDAN\" alt=\"Michael Jordan\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Michael-Jordan.jpg 293w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Michael-Jordan-150x150.jpg 150w\" sizes=\"auto, (max-width: 293px) 100vw, 293px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    MICHAEL JORDAN                            <\/h3>\n                        <div class=\"image-caption tb_text_wrap\">\n            Inria Paris and University of California, Berkeley        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-3 tb_c38f919 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_6e0n919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <div id=\"ctl00_MainContent_SubmissionPreview1_divTitle\" class=\"SubmissionTitle\">\n<h4>A Collectivist, Economic Perspective on AI<\/h4>\n<\/div>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_wsct919 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-wsct919-0\" class=\"tb_title_accordion\" aria-controls=\"acc-wsct919-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Presentation and Michael Jordan Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-wsct919-0-content\" data-id=\"acc-wsct919-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_083f919\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_avrn919 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_6s5x919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before.\u00a0 The word &#8220;intelligence&#8221; is being used as a North Star for the development of this technology, with human cognition viewed as a baseline.\u00a0 This view neglects the fact that humans are social animals, and that much of our intelligence is social and cultural in origin.\u00a0 Thus, a broader framing is to consider the system level, where the agents in the system, be they computers or humans, are active, they are cooperative, and they wish to obtain value from their participation in learning-based systems.\u00a0 Agents may supply data and other resources to the system only if it is in their interest to do so, and they may be honest and cooperative only if it is in their interest to do so.\u00a0 Critically, intelligence inheres as much in the overall system as it does in individual agents.\u00a0 In the overall system, computation of equilibria replaces computation of optima.\u00a0 This is a perspective that is familiar in economics, although without the focus on learning algorithms.\u00a0 A key challenge is thus to bring (micro)economic concepts into contact with foundational issues in the computing, statistical, and decision-making sciences.\u00a0 I&#8217;ll discuss some concrete examples of problems and solutions at this tripartite interface.<\/p>\n<h4>About Michael Jordan<\/h4>\n<p>Michael I. Jordan is a researcher at Inria Paris and Professor Emeritus at the University of California, Berkeley.\u00a0 His research interests bridge the computational, statistical, cognitive, biological and social sciences.\u00a0 Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering, a member of the American Academy of Arts and Sciences, a Foreign Member of the Royal Society, and a Foreign Member of the Chinese Academy of Sciences.\u00a0 He was a winner of a BBVA Foundation Frontiers of Knowledge Award in 2025 and was the inaugural winner of the World Laureates Association (WLA) Prize in 2022.\u00a0 He was a Plenary Lecturer at the International Congress of Mathematicians in 2018.\u00a0 He has received the Ulf Grenander Prize from the American Mathematical Society, the IEEE John von Neumann Medal, the IJCAI Research Excellence Award, the David E.\u00a0 Rumelhart Prize, and the ACM\/AAAI Allen Newell Award.\u00a0<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_oaj2919 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_j2jv919 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_j8qu919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><strong>MONDAY, November 2, 9:45-10:45AM<\/strong><\/h3>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"smilowitz\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-smilowitz tb_zjc1919 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_kp1p919 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_h082919 image-top bordered  tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Karen-Smilowitz-194x173.jpg\" width=\"194\" height=\"173\" class=\"wp-post-image wp-image-12085\" title=\"Karen Smilowitz \" alt=\"Karen Smilowitz \">    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    Karen Smilowitz                             <\/h3>\n                        <div class=\"image-caption tb_text_wrap\">\n            Northwestern University        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_coty919 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_75cm919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <div id=\"ctl00_MainContent_SubmissionPreview1_divTitle\" class=\"SubmissionTitle\">\n<h4>The Use of Optimization Models in Public Sector Decision-making<\/h4>\n<\/div>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_jpwf919 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-jpwf919-0\" class=\"tb_title_accordion\" aria-controls=\"acc-jpwf919-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Presentation and Karen Smilowitz Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-jpwf919-0-content\" data-id=\"acc-jpwf919-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_wl3c919\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_peu8919 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_vgo4919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Operations research methods have been used to improve access to education for decades.\u00a0 The talk will focus specifically on connections between evolving issues in public education and advances in optimization, computing and geographic information systems.\u00a0 The talk will present examples of how operations research has impacted access to education.\u00a0 To illustrate the impact of such work, we present a case study from a research-practice partnership focused on district redesign to address access to education.\u00a0 \u00a0The talk will also reflect on challenges related to the use of optimization models in public sector decision-making.<\/p>\n<h4>About Karen Smilowitz<\/h4>\n<p>Dr. Karen Smilowitz is Associate Provost for Undergraduate Education at Northwestern University. \u00a0 She holds the James N. and Margie M. Krebs Professorship in Industrial Engineering and Management Science, with a joint appointment in the Operations group at the Kellogg School of Management.\u00a0 Dr. Smilowitz is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and nonprofit applications.\u00a0 She has been instrumental in promoting the use of operations research within the humanitarian and nonpro\ufb01t sectors through the Woodrow Wilson International Center for Scholars, the American Association for the Advancement of Science, and the National Academy of Engineering, as well as various media outlets.\u00a0 She received a CAREER award from the National Science Foundation and a Sloan Industry Studies Fellowship.\u00a0 She is an INFORMS Fellow and recently served as Editor-in-Chief of Transportation Science, the flagship journal of the INFORMS Transportation Science and Logistics Society.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_9mx4919 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_d05f919 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_uhiy919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><strong>TUESDAY, November 3, 9:45-10:45AM<\/strong><\/h3>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_yc5p919 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_bhhg919 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_o9ob919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><strong>WEDNESDAY, November 4, 11AM-12Noon<\/strong><\/h3>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"lodi\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-lodi tb_8tfr919 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_irrm919 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_rd4s919\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-1 tb_sfmz919 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_b0a0919 image-top bordered  tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" width=\"770\" height=\"860\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Andrea-Lodi.jpg\" class=\"wp-post-image wp-image-12088\" title=\"ANDREA LODI\" alt=\"Andrea Lodi\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Andrea-Lodi.jpg 770w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Andrea-Lodi-269x300.jpg 269w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Andrea-Lodi-768x858.jpg 768w\" sizes=\"auto, (max-width: 770px) 100vw, 770px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    ANDREA LODI                            <\/h3>\n                        <div class=\"image-caption tb_text_wrap\">\n            Cornell Tech        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-3 tb_020u919 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_0plr919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4>Three Ideas toward GPU-Enhanced Mixed-Integer Optimization<\/h4>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_jswm919 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-jswm919-0\" class=\"tb_title_accordion\" aria-controls=\"acc-jswm919-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Presentation and Andrea Lodi  Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-jswm919-0-content\" data-id=\"acc-jswm919-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_4s0q919\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_zp9l919 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_3ydp919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>The possibility of using GPUs to enhance the parallelization of Mixed-Integer Optimization algorithms has been discussed for several years, but the development of new approaches has been slower than anticipated. This is due to several factors, including the technological burden of moving data between CPUs, where the computation has traditionally been performed, and GPUs, as well as a more fundamental question: which algorithmic components of branch and bound should be executed on GPUs, and how should they be redesigned to exploit massive parallelism?<\/p>\n<p>The recent rediscovery of first-order methods for linear programming (LP), and the subsequent efficient implementations of the primal-dual hybrid gradient algorithm, have renewed interest in the topic. This has led software vendors to add first-order methods for LP to their algorithmic arsenal and experiment with branch-and-bound versions based on them, while GPU hardware vendors have started developing their own optimization solvers. These developments raise a broader question: should GPUs simply be used to accelerate existing Mixed-Integer Optimization algorithms, or should we rethink the algorithms themselves to better match the hardware?<\/p>\n<p>Within this exciting context, we propose three ideas that could contribute to the development of effective GPU-enhanced exact algorithms for Mixed-Integer Optimization. First, we show how to effectively batch similar LPs associated with important branch-and-bound components, such as strong branching and bound tightening, and solve them in parallel on GPUs. Second, we extend the batching idea to the branch-and-bound tree itself, processing multiple tree nodes in parallel on GPUs rather than following the traditional one-node-at-a-time computation. Third, we observe that this approach is not restricted to first-order methods for solving linear or convex relaxations: lightweight neural proxies, such as graph neural networks, can be designed to provide valid bounds and replace more expensive relaxation solvers while preserving the exactness of branch and bound.<\/p>\n<p>Together, these three ideas suggest that fully exploiting GPUs for Mixed-Integer Optimization may require more than accelerating individual components of existing solvers. It may require rethinking the architecture of exact algorithms around massive parallelism, while preserving the mathematical guarantees that make them exact.<\/p>\n<h4><strong>About Andrea Lodi<\/strong><\/h4>\n<p>Andrea Lodi is an Andrew H. and Ann R. Tisch Professor at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion. He is a member of both the Operations Research and Information Engineering and the Computer Science fields at Cornell University. Before joining Cornell, he was a Herman Goldstine Fellow at the IBM Mathematical Sciences Department, NY in 2005\u20132006, full professor of Operations Research at DEI, University of Bologna 2007-2015, and Canada Excellence Research Chair in \u201cData Science for Real-time Decision Making\u201d at Polytechnique Montr\u00e9al 2015-2022. His main research interests are in Mixed-Integer Linear and Nonlinear Programming and Data-driven Optimization, and his work has received several recognitions including the IBM and Google faculty awards. Andrea is the recipient of the INFORMS Optimization Society 2021 Farkas Prize and has been elected an INFORMS Fellow in 2023. Andrea has been the principal investigator of scientific projects (often involving industrial partners) for Italy, European Union, Canada, and USA. In the period 2006-2021, he was a consultant of the IBM CPLEX research and development team, developing CPLEX, one of the leading software for Mixed-Integer Optimization.\u00a0<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"keynotes\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-keynotes tb_5sms919 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_ksx0919 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_tm12919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h2>Keynotes<\/h2>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module text -->\n<div  class=\"module module-text tb_rkq5919   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><strong>SUNDAY, November 1, 5:45-6:35PM<\/strong><\/h3>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"wang\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-wang tb_1fzp918 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_jspz918 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_98z2918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-1 tb_euoa918 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_cayf918 image-top bordered  tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/mengdi-wang-194x204.png\" width=\"194\" height=\"204\" class=\"wp-post-image wp-image-12096\" title=\"MENGDI WANG\" alt=\"Mengdi Wang\">    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    MENGDI WANG                            <\/h3>\n                        <div class=\"image-caption tb_text_wrap\">\n            Princeton University        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-3 tb_xbg2918 last\">\n                    <!-- module fancy heading -->\n<div  class=\"module module-fancy-heading tb_fc4z918 \" data-lazy=\"1\">\n        <h3 class=\"fancy-heading\">\n    <span class=\"main-head tf_block\">\n                                <\/span>\n\n    \n    <span class=\"sub-head tf_block tf_rel\">\n                    OMEGA RHO DISTINGUISHED LECTURE            <\/span>\n    <\/h3>\n<\/div>\n<!-- \/module fancy heading -->\n<!-- module text -->\n<div  class=\"module module-text tb_unik918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4><strong>LabOS: The AI-XR Co-Scientist That Sees and Works With Humans<\/strong><\/h4>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_673c918 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-673c918-0\" class=\"tb_title_accordion\" aria-controls=\"acc-673c918-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Presentation and Mengdi Wang Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-673c918-0-content\" data-id=\"acc-673c918-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_c1nz918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_ysf2918 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_fl1h918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Modern science advances fastest when thought meets action. LabOS represents the first AI co-scientist that unites computational reasoning with physical experimentation through multimodal perception, self-evolving agents, and Extended-Reality(XR)-enabled human-AI collaboration. By connecting multi-model AI agents, smart glasses, and robots, LabOS allows AI to see what scientists see, understand experimental context, and assist in real-time execution. Across applications &#8212; from cancer immunotherapy target discovery to stem-cell engineering and material science &#8212; LabOS shows that AI can move beyond computational design to participation, turning the laboratory into an intelligent, collaborative environment where human and machine discovery evolve together.<\/p>\n<h4>About Mengdi Wang<\/h4>\n<p>Mengdi Wang is Co-Director of Princeton AI for Accelerated Invention, and Professor of the Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning at Princeton University. She is also affiliated with the Department of Computer Science, Omenn-Darling Bioengineering Institute, and Princeton Language+Intelligence. She was a visiting research scientist at Google DeepMind, IAS and Simons Institute on Theoretical Computer Science. Her research focuses on machine learning, reinforcement learning, generative AI, large language models, and AI for science. Mengdi received her PhD in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2013, where she was affiliated with the Laboratory for Information and Decision Systems and advised by Dimitri P. Bertsekas. Before that, she got her bachelor degree from the Department of Automation, Tsinghua University. Mengdi received the Young Researcher Prize in Continuous Optimization of the Mathematical Optimization Society in 2016 (awarded once every three years), the Princeton SEAS Innovation Award in 2016, the NSF Career Award in 2017, the Google Faculty Award in 2017, and the MIT Tech Review 35-Under-35 Innovation Award (China region) in 2018, WAIC YunFan Award 2022, American Automatic Control Council&#8217;s Donald Eckman Award 2024 for &#8220;extraordinary contributions to the intersection of control, dynamical systems, machine learning and information theory&#8221;. She serves as a Program Chair for ICLR 2023 and Senior AC for Neurips, ICML, COLT, associate editor for Harvard Data Science Review, Operations Research. Research supported by NSF, AFOSR, NIH, ONR, Google, Microsoft C3.ai, FinUP, RVAC Medicines, MURI, GenMab.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"dwork\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-dwork tb_n81n918 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_5npj918 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_i0o2918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-1 tb_ehu4918 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_p68d918 image-top bordered  tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Cynthia-Dwork-818x1024-194x220.jpg\" width=\"194\" height=\"220\" class=\"wp-post-image wp-image-12105\" title=\"Cynthia Dwork\" alt=\"Harvard University\">    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    Cynthia Dwork                            <\/h3>\n                        <div class=\"image-caption tb_text_wrap\">\n            Harvard University        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-3 tb_5vp7918 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_x2we918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4>Indistinguishability: The Stuff of Magic<\/h4>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_sj78918 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-sj78918-0\" class=\"tb_title_accordion\" aria-controls=\"acc-sj78918-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Presentation and Cynthia Dwork Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-sj78918-0-content\" data-id=\"acc-sj78918-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_e6yg918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_tabe918 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_fga6918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>CS theory has given us many magical concepts, such as zero-knowledge proofs, pseudo-randomness, privacy-preserving data analysis, and public-key cryptography. These, and many others, share a common thread: their very definitions are based on indistinguishability, a core concept in complexity theory. Indistinguishability is a form of impossibility: encryptions of zero cannot be distinguished from encryptions of one; outputs of an analysis operating on a dataset D cannot be distinguished from outputs of an analysis operating on D+Me (or D+You). It is remarkable that this negative, complexity-based notion has proven so powerful as an instrument of positive algorithmic construction. We will survey several flavors of indistinguishability and their applications, and conclude with thoughts on the role indistinguishability can play in addressing pressing questions of values in modern computer systems.<\/p>\n<h4><strong>About Cynthia Dwork<\/strong><\/h4>\n<p>Cynthia Dwork, Gordon McKay Professor of Computer Science at Harvard, and Affiliated Faculty at Harvard Law School and Department of Statistics, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation through her invention of Differential Privacy. She has also made seminal contributions in cryptography and distributed computing, and she spearheaded the field of algorithmic fairness.\u00a0 Her honors include the US National Medal of Science, the Japan Prize, the Hamming Medal, and the Dijkstra, G\u00f6del, Knuth, and Kanellakis Awards. She is a member of the US National Academy of Sciences and the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences and the American Philosophical Society.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"vanroy\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-vanroy tb_zifp918 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_vkz0918 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_iq5u918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-1 tb_bqox918 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_4597918 image-top bordered  tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Van-Roy-731x1024-194x211.jpg\" width=\"194\" height=\"211\" class=\"wp-post-image wp-image-12141\" title=\"Benjamin Van Roy \" alt=\"Benjamin Van Roy \">    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    Benjamin Van Roy                             <\/h3>\n                        <div class=\"image-caption tb_text_wrap\">\n            Stanford University        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-3 tb_fcmk918 last\">\n                    <!-- module fancy heading -->\n<div  class=\"module module-fancy-heading tb_gvca18 \" data-lazy=\"1\">\n        <h3 class=\"fancy-heading\">\n    <span class=\"main-head tf_block\">\n                                <\/span>\n\n    \n    <span class=\"sub-head tf_block tf_rel\">\n                    Morse Lectureship            <\/span>\n    <\/h3>\n<\/div>\n<!-- \/module fancy heading -->\n<!-- module text -->\n<div  class=\"module module-text tb_buhi287   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4>Aligning Superintelligence<\/h4>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_m99f918 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-m99f918-0\" class=\"tb_title_accordion\" aria-controls=\"acc-m99f918-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Benjamin Van Roy Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-m99f918-0-content\" data-id=\"acc-m99f918-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_6yh2918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_4y2g918 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_o9kf918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>\u00a0<\/p>\n<h4>About Benjamin Van Roy<\/h4>\n<p>Benjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His research focuses on reinforcement learning and alignment. Beyond academia, he founded the Efficient Agent Team \u2013 DeepMind&#8217;s first US-based research team (now part of Google) \u2013 and Enuvis (acquired by SiRF\/Qualcomm). He has also led research programs at Morgan Stanley and Unica (acquired by IBM). He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT, where his doctoral research was advised by John N. Tsitsiklis.<\/p>\n<p>He is a Fellow of INFORMS and IEEE and has served on the editorial boards of Machine Learning, Mathematics of Operations Research, for which he edited the Learning Theory Area, Operations Research, for which he edited the Financial Engineering Area, the INFORMS Journal on Optimization, and Foundations and Trends in Machine Learning. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master&#8217;s Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, the Management Science and Engineering Department&#8217;s Graduate Teaching Award, and the INFORMS Frederick W. Lanchester Prize.<\/p>\n<p>He has graduated dozens of doctoral students, who have gone on to careers in academia (Carnegie Mellon, Columbia, Cornell, MIT, Northwestern, Rice, Stanford, USC), technology (Adobe, Amazon, DeepMind, Meta, Microsoft, Netflix, OpenAI, Spotify, Tesla, xAI), and finance (Citadel, DE Shaw, Goldman Sachs, Jane Street, Morgan Stanley, Two Sigma).<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_idkl918 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_wbrm918 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_hwx5918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><strong>MONDAY, nOVEMBER 2, 5:45-6:35PM<\/strong><\/h3>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"microsoft\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-microsoft tb_xzz9918 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_0k69918 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_imzv918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-1 tb_r7im918 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_d0d3918 image-top   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/08\/Edelman-194x187.jpg\" width=\"194\" height=\"187\" class=\"wp-post-image wp-image-12971\" title=\"KONSTANTINA MELLOU\" alt=\"Microsoft\">    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h4 class=\"image-title\">\n                                    KONSTANTINA MELLOU                            <\/h4>\n                        <div class=\"image-caption tb_text_wrap\">\n            Microsoft        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-3 tb_p06v918 last\">\n                    <!-- module fancy heading -->\n<div  class=\"module module-fancy-heading tb_uob0764 \" data-lazy=\"1\">\n        <h3 class=\"fancy-heading\">\n    <span class=\"main-head tf_block\">\n                                <\/span>\n\n    \n    <span class=\"sub-head tf_block tf_rel\">\n                    2026 INFORMS franz edelman award REPRISE            <\/span>\n    <\/h3>\n<\/div>\n<!-- \/module fancy heading -->\n<!-- module text -->\n<div  class=\"module module-text tb_0ypp918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4>Microsoft Cloud Supply Chain: Democratizing Hyperscale Optimization for Cloud Fulfillment\u00a0<\/h4>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_jzbj918 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-jzbj918-0\" class=\"tb_title_accordion\" aria-controls=\"acc-jzbj918-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Reprise Presentation and About Microsoft<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-jzbj918-0-content\" data-id=\"acc-jzbj918-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_z5db918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_mrr5918 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_4v1a918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Microsoft transformed Cloud Supply Chain with the Intelligent Fulfillment Service (IFS), a breakthrough platform that combines machine learning, mathematical optimization, and generative AI. By automating global shipment planning, IFS cuts cycle times in half and delivers tens to hundreds of millions of dollars in annual savings while helping mitigate tariff exposure. Its large-language-model-powered assistant, built on the pioneering OptiGuide framework, brings real-time explainability and scenario exploration to planners, significantly reducing the fulfillment team&#8217;s workload by compressing decision cycles from days to minutes.<\/p>\n<p>Microsoft creates platforms and tools powered by AI to deliver innovative solutions that meet the evolving needs of our customers. The technology company is committed to making AI available broadly and doing so responsibly, with a mission to empower every person and every organization on the planet to achieve more.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"romeromorales\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-romeromorales tb_hy9x918 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_ig19918 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_wgq7918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-1 tb_0qtc918 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_zxje918 image-top bordered  tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Dolores-Romero-Morales-808x1024-194x196.jpg\" width=\"194\" height=\"196\" class=\"wp-post-image wp-image-12160\" title=\"Dolores Romero Morales  \" alt=\"Dolores Romero Morales \">    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    Dolores Romero Morales                              <\/h3>\n                        <div class=\"image-caption tb_text_wrap\">\n            Copenhagen Business School        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-3 tb_fld1918 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_eifo918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4>Optimization Models for Group-Level Explainability in Machine Learning<\/h4>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_jqgh918 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-jqgh918-0\" class=\"tb_title_accordion\" aria-controls=\"acc-jqgh918-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Presentation and Dolores Romero Morales Bio  <\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-jqgh918-0-content\" data-id=\"acc-jqgh918-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_iaj1918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_3mys918 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_hhij918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>State-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms have become ubiquitous across industries due to their high predictive performance. However, despite their widespread deployment, these models are often criticized for their lack of transparency and accountability. Their \u201cblack-box\u201d nature obscures the reasoning behind decisions, limiting trust and hindering their use in critical, data-driven decision-making processes. Moreover, algorithmic decisions can perpetuate or amplify societal biases, leading to unfair and discriminatory outcomes. These concerns are especially pressing in high-stakes domains such as healthcare, criminal justice, credit scoring, and public benefits, where algorithmic decisions can significantly affect individuals\u2019 lives.<\/p>\n<p>In Explainable Artificial Intelligence (XAI), local explanations seek to shed light on the prediction of a machine learning model at a given instance. Popular methodologies include LIME, which builds an interpretable surrogate model that approximates predictions around the instance, and SHAP, which attributes the difference between the instance prediction and a baseline prediction to individual features. While such explanations are valuable to the individuals affected by a prediction, they provide limited information to modelers and decision-makers seeking to understand model behavior across many individuals. This raises broader questions concerning which features drive explanations across groups of instances, whether explanations are consistent and fair across individuals, and how individuals with similar explanations can be identified and represented collectively. This presentation shows how Operations Research provides a natural framework for addressing these questions and moving from individual explanations toward explainability at the group level.<\/p>\n<h4>About Dolores Romero Morales<\/h4>\n<p>Dolores Romero Morales is a Professor in Operations Research at Copenhagen Business School. Her areas of expertise include Data Science, Supply Chain Optimization and Revenue Management. In Data Science she investigates explainability\/interpretability, fairness and visualization matters. In Supply Chain Optimization she works on environmental issues and robustness. In Revenue Management she works on large-scale network models. Her work has appeared in a variety of leading scholarly journals, including European Journal of Operational Research, Management Science, Mathematical Programming and Operations Research. She has received various distinctions, such as the SEIO Medal for an outstanding contribution to the Operations Research discipline, and since June 2025, she is President-Elect of EURO &#8211; The Association of European Operational Research Societies.<\/p>\n<p>Dolores has received funding from the EU as well as national research councils to conduct her research. She has worked with and advised various companies on these topics, including IBM, SAS, KLM and Radisson Edwardian Hotels, as a result of which these companies managed to improve some of their practices. SAS named her an Honorary SAS Fellow and member of the SAS Academic Advisory Board. Currently, she is a member of the Editorial Board of the International Journal of Production Research, and an Associate Editor of Journal of the Operational Research Society, the INFORMS Journal on Data Science, and TOP-Transactions in Operations Research.<\/p>\n<p>Dolores joined Copenhagen Business School in 2014. Prior to coming to Copenhagen Business School, she was a Full Professor at University of Oxford (2003-2014) and an Assistant Professor at Maastricht University (2000-2003). She has a BSc and an MSc in Mathematics from Universidad de Sevilla and a PhD in Operations Research from Erasmus University Rotterdam.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"belcak\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-belcak tb_8r95918 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_j61s918 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_mn3c918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col-full tb_4vgc918 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_tytm918 image-top bordered  tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/photo-186x186.jpeg\" width=\"186\" height=\"186\" class=\"wp-post-image wp-image-12273\" title=\"Peter Belcak \" alt=\"NVIDIA\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/photo-186x186.jpeg 186w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/photo-300x300.jpeg 300w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/photo-150x150.jpeg 150w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/photo.jpeg 399w\" sizes=\"auto, (max-width: 186px) 100vw, 186px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    Peter Belcak                             <\/h3>\n                        <div class=\"image-caption tb_text_wrap\">\n            NVIDIA        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <\/div>\n                <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-2 tb_0e7t918 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_5s9s918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4><strong>TBA<\/strong><\/h4>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_hcg4918 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-hcg4918-0\" class=\"tb_title_accordion\" aria-controls=\"acc-hcg4918-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Peter Belcak Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-hcg4918-0-content\" data-id=\"acc-hcg4918-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_ttm0918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_7tuy918 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_r9ob918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4>About Peter Belcak<\/h4>\n<p>Peter Belcak is an AI researcher at NVIDIA Research. He works on making AI systems more reliable and efficient, with a focus on reducing the cost of building and running advanced AI. His research spans agentic systems, efficient deep learning, and practical methods for making AI more scalable and accessible.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_94dh918 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_txln918 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_nek7918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><strong>TUESDAY, nOVEMBER 3, 5:45-6:35PM<\/strong><\/h3>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"shen\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-shen tb_blos918 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_mrnr918 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_gi21918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-1 tb_rn8d918 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_s7yc918 image-top bordered  tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Tueng-Shen-764x1024-194x238.jpg\" width=\"194\" height=\"238\" class=\"wp-post-image wp-image-12163\" title=\"Tueng Shen\" alt=\"Tueng Shen  \">    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    Tueng Shen                            <\/h3>\n                        <div class=\"image-caption tb_text_wrap\">\n            University of Washington        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-3 tb_xrsi918 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_bh80918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4><strong>TBA<\/strong><\/h4>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_pn1t918 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-pn1t918-0\" class=\"tb_title_accordion\" aria-controls=\"acc-pn1t918-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Tueng Shen Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-pn1t918-0-content\" data-id=\"acc-pn1t918-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_uxzc918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_jqno918 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_5es9918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4>About Tueng T. Shen<\/h4>\n<p>Tueng T. Shen is the inaugural Associate Dean of Medical Technology Innovation, a joint position between UW Medicine and College of Engineering. As an eye surgeon as well as an engineer, Shen builds bridges between engineers and physicians to facilitate the translation of innovative engineering technologies into creative clinical solutions to transform health and healthcare. Shen seeks strong partnerships with our research\u00a0communities, technology industries and business communities to catalyze innovations that will improve healthcare delivery, especially leveraging UW Medicine\u2019s extensive WWAMI (Washington, Wyoming, Alaska, Montana and Idaho) network. Shen is a fellow of The American Institute for Medical and Biological Engineering (AIMBE). She is also the\u00a0inaugural Director of the Kren Engineering-based Medicine Institute at the University of Washington and elected member of the Washington State Academy of Sciences.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"georgiatech\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-georgiatech tb_fm1i918 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_usfj918 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_nzya918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-1 tb_82ys918 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_quk541 image-top   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" width=\"250\" height=\"250\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/08\/UPS-logo.png\" class=\"wp-post-image wp-image-12970\" title=\"Joel Sokol\" alt=\"Georgia Tech\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/08\/UPS-logo.png 250w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/08\/UPS-logo-150x150.png 150w\" sizes=\"auto, (max-width: 250px) 100vw, 250px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    Joel Sokol                            <\/h3>\n                        <div class=\"image-caption tb_text_wrap\">\n            Georgia Tech        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-3 tb_rour918 last\">\n                    <!-- module fancy heading -->\n<div  class=\"module module-fancy-heading tb_w9ka918 \" data-lazy=\"1\">\n        <h3 class=\"fancy-heading\">\n    <span class=\"main-head tf_block\">\n                                <\/span>\n\n    \n    <span class=\"sub-head tf_block tf_rel\">\n                    2026 UPS George D. Smith Reprise            <\/span>\n    <\/h3>\n<\/div>\n<!-- \/module fancy heading -->\n<!-- module text -->\n<div  class=\"module module-text tb_kan0918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4 class=\"subtitle\">Georgia Institute of Technology Team<\/h4>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_d2y8918 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-d2y8918-0\" class=\"tb_title_accordion\" aria-controls=\"acc-d2y8918-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">About Georgia Institute of Technology<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-d2y8918-0-content\" data-id=\"acc-d2y8918-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_g9r8918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_m9je918 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_fmlx918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Georgia Tech\u2019s Master of Science in Analytics (MSA) degree is an interdisciplinary analytics\/data science degree taught jointly <br>across computing, OR, statistics, and business. MSA has been ranked as high as #1 in Data Analytics, #3 in Data Science, and #3 <br>in Business Analytics.<\/p>\n<p>The MSA degree is offered in two ways: MSA Atlanta, an in-person program with premium perks like personalized career coaching, networking events, bootcamps, and alumni mentorship; and MSA Online (OMSA), a worldwide at-scale low-tuition program with proactive, high-touch advising and many opportunities for students to engage with each other and with instructors both within and outside of regular courses.<\/p>\n<p>MSA\u2019s curriculum is strongly informed by practitioner input, via an industry Advisory Board and alumni engagement. The innovative MSA curriculum includes a practice-focused integrated interdisciplinary core, five elective slots for personalization and specialization, non-technical skills training, a major applied practicum where each student works closely with a partner company\/organization, a dual-degree MSA\/MBA opportunity, and (because this is a rapidly-changing field) a learning-how-to-learn emphasis and the opportunity to return and take courses throughout one\u2019s career to keep pace with emerging trends.<\/p>\n<p>MSA is designed for accessibility, with a minimal set of prerequisites, availability anywhere in the world, a more affordable price point, a personalizable time scale, and a MicroMasters on-ramp for non-traditional students.<\/p>\n<p>MSA students and alumni are winning major national\/international analytics\/data-science contests, publishing in major journals and conferences in the field, and serving in positions from entry-level to C-level. MSA now has over 7000 alumni worldwide.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"fernandez\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-fernandez tb_ugks918 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_hr66918 first\">\n                            <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_2 tb_hjav918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column col4-1 tb_gqfr918 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_bilq918 image-top bordered  tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" width=\"591\" height=\"886\" src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/07\/elena-fernandez.jpg\" class=\"wp-post-image wp-image-12455\" title=\"Elena Fernandez\" alt=\"Elena Fernandez\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/07\/elena-fernandez.jpg 591w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/07\/elena-fernandez-200x300.jpg 200w\" sizes=\"auto, (max-width: 591px) 100vw, 591px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    Elena Fernandez                            <\/h3>\n                        <div class=\"image-caption tb_text_wrap\">\n            University of C\u00e1diz        <\/div>\n        <!-- \/image-caption -->\n            <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column sub_column col4-3 tb_bjl5918 last\">\n                    <!-- module fancy heading -->\n<div  class=\"module module-fancy-heading tb_pd4r588 \" data-lazy=\"1\">\n        <h3 class=\"fancy-heading\">\n    <span class=\"main-head tf_block\">\n                                <\/span>\n\n    \n    <span class=\"sub-head tf_block tf_rel\">\n                    IFORS DISTINGUISHED LECTURER            <\/span>\n    <\/h3>\n<\/div>\n<!-- \/module fancy heading -->\n<!-- module text -->\n<div  class=\"module module-text tb_9e5v918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h4>Location Science, Once More: New Challenges for a Mature Area<\/h4>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_gld6918 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  embossed shadow tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-gld6918-0\" class=\"tb_title_accordion\" aria-controls=\"acc-gld6918-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Presentation and Elena Fernandez Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-gld6918-0-content\" data-id=\"acc-gld6918-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                        <div  data-lazy=\"1\" class=\"module_subrow themify_builder_sub_row tf_w col_align_top tb_col_count_1 tb_ln8q918\">\n                <div  data-lazy=\"1\" class=\"module_column sub_column tb_3zqt918 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_pzsq918   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Location Science is a well-established field that has evolved from its origins in geometry and economics into a broad spectrum of problems, many of them motivated by real-world applications. Operations Research provides an ideal framework for addressing location problems by combining theoretical, modeling, and algorithmic perspectives, thereby offering a comprehensive view of these problems.<\/p>\n<p>While the final decades of the twentieth century laid the foundations of modern Location Science, particularly in the context of discrete and network-based models, recent decades have witnessed a significant expansion of the field. New developments include hybrid models that integrate different types of decisions, as well as multi-level problems that capture the objectives of multiple stakeholders, often with conflicting interests, among many other advances.<\/p>\n<p>Although this broadening of the field is clearly reflected in the growing number of publications devoted to these challenging new models, the sense of community among researchers in the area appears to be gradually weakening.<\/p>\n<p>In this talk, I will explore some possible reasons behind this trend. I will also discuss several emerging challenges facing the field and outline potential directions for addressing them.<\/p>\n<h4>About Elena Fernandez<\/h4>\n<p>Elena Fernandez is full professor in Operations Research since 2007. She has spent much of her academic career at the Universitat Polit\u00e8cnica de Catalunya in Barcelona; since 2019 she is affiliated to the University of C\u00e1diz.<\/p>\n<p>Her research interest focuses on mathematical optimization models for discrete optimization, mainly on applications for transportation and logistics involving discrete location, network design and vehicle routing. She has published scientific papers in the flagship OR journals, including Operations Research, Transportation Science, and INFORMS Journal of Computing, with about 70 co-authors from a dozen of different countries.<\/p>\n<p>Elena has been plenary speaker at several international conferences and was the Chair of the Scientific Committee of the Royal Spanish Mathematical Society (RSME). In 2021 she received the Lifetime Achievement in Location Analysis Award 2021 of the Section on Locational Analysis (SOLA) of INFORMS.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                    <\/div>\n                    <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <\/div>\n                <\/div>\n                        <\/div>\n        <\/div>\n        <\/div>\n<!--\/themify_builder_content-->","protected":false},"excerpt":{"rendered":"<p>Plenaries &amp; Keynotes<\/p>\n","protected":false},"author":46,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"class_list":["post-12072","page","type-page","status-publish","hentry","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.0 (Yoast SEO v26.0) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Plenaries &amp; Keynotes &#187; 2026 INFORMS Annual Meeting<\/title>\n<meta name=\"description\" content=\"View the Plenaries and Keynotes at the 2026 INFORMS Annual Meeting in San Francisco.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/meetings.informs.org\/wordpress\/annual\/plenaries-keynotes\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Plenaries &amp; Keynotes\" \/>\n<meta property=\"og:description\" content=\"View the Plenaries and Keynotes at the 2026 INFORMS Annual Meeting in San Francisco.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/meetings.informs.org\/wordpress\/annual\/plenaries-keynotes\/\" \/>\n<meta property=\"og:site_name\" content=\"2026 INFORMS Annual Meeting\" \/>\n<meta property=\"article:modified_time\" content=\"2026-08-24T13:55:07+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Michael-Jordan.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"293\" \/>\n\t<meta property=\"og:image:height\" content=\"293\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"55 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/annual\/plenaries-keynotes\/\",\"url\":\"https:\/\/meetings.informs.org\/wordpress\/annual\/plenaries-keynotes\/\",\"name\":\"Plenaries &amp; 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Keynotes"}]},{"@type":"WebSite","@id":"https:\/\/meetings.informs.org\/wordpress\/annual\/#website","url":"https:\/\/meetings.informs.org\/wordpress\/annual\/","name":"2026 INFORMS Annual Meeting","description":"November 1-4, 2026 | San Francisco, CA","publisher":{"@id":"https:\/\/meetings.informs.org\/wordpress\/annual\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/meetings.informs.org\/wordpress\/annual\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/meetings.informs.org\/wordpress\/annual\/#organization","name":"2026 INFORMS Annual Meeting","url":"https:\/\/meetings.informs.org\/wordpress\/annual\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/meetings.informs.org\/wordpress\/annual\/#\/schema\/logo\/image\/","url":"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2025\/10\/cropped-2026_INFORMS_Annual_Meeting_Logo.png","contentUrl":"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2025\/10\/cropped-2026_INFORMS_Annual_Meeting_Logo.png","width":512,"height":512,"caption":"2026 INFORMS Annual Meeting"},"image":{"@id":"https:\/\/meetings.informs.org\/wordpress\/annual\/#\/schema\/logo\/image\/"}}]}},"builder_content":"<h1 style=\"text-align: left\">Plenaries &amp; Keynotes<\/h1>\n<ul id=\"menu-plenaries-1\"><li><a href=\"#plenaries\">Plenaries<\/a><\/li> <li><a href=\"#keynotes\">Keynotes<\/a><\/li> <\/ul>\n<h2>Plenaries<\/h2>\n<h3><strong>SUNDAY, November 1, 9:30-10:45AM<\/strong><\/h3>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Michael-Jordan.jpg\" title=\"MICHAEL JORDAN\" alt=\"Michael Jordan\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Michael-Jordan.jpg 293w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Michael-Jordan-150x150.jpg 150w\" sizes=\"(max-width: 293px) 100vw, 293px\" \/> <h3> MICHAEL JORDAN <\/h3> Inria Paris and University of California, Berkeley\n<h4>A Collectivist, Economic Perspective on AI<\/h4>\n<ul><li><h4>Presentation and Michael Jordan Bio<\/h4><p>Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before.\u00a0 The word \"intelligence\" is being used as a North Star for the development of this technology, with human cognition viewed as a baseline.\u00a0 This view neglects the fact that humans are social animals, and that much of our intelligence is social and cultural in origin.\u00a0 Thus, a broader framing is to consider the system level, where the agents in the system, be they computers or humans, are active, they are cooperative, and they wish to obtain value from their participation in learning-based systems.\u00a0 Agents may supply data and other resources to the system only if it is in their interest to do so, and they may be honest and cooperative only if it is in their interest to do so.\u00a0 Critically, intelligence inheres as much in the overall system as it does in individual agents.\u00a0 In the overall system, computation of equilibria replaces computation of optima.\u00a0 This is a perspective that is familiar in economics, although without the focus on learning algorithms.\u00a0 A key challenge is thus to bring (micro)economic concepts into contact with foundational issues in the computing, statistical, and decision-making sciences.\u00a0 I'll discuss some concrete examples of problems and solutions at this tripartite interface.<\/p> <h4>About Michael Jordan<\/h4> <p>Michael I. Jordan is a researcher at Inria Paris and Professor Emeritus at the University of California, Berkeley.\u00a0 His research interests bridge the computational, statistical, cognitive, biological and social sciences.\u00a0 Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering, a member of the American Academy of Arts and Sciences, a Foreign Member of the Royal Society, and a Foreign Member of the Chinese Academy of Sciences.\u00a0 He was a winner of a BBVA Foundation Frontiers of Knowledge Award in 2025 and was the inaugural winner of the World Laureates Association (WLA) Prize in 2022.\u00a0 He was a Plenary Lecturer at the International Congress of Mathematicians in 2018.\u00a0 He has received the Ulf Grenander Prize from the American Mathematical Society, the IEEE John von Neumann Medal, the IJCAI Research Excellence Award, the David E.\u00a0 Rumelhart Prize, and the ACM\/AAAI Allen Newell Award.\u00a0<\/p><\/li><\/ul>\n<p>Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before.\u00a0 The word \"intelligence\" is being used as a North Star for the development of this technology, with human cognition viewed as a baseline.\u00a0 This view neglects the fact that humans are social animals, and that much of our intelligence is social and cultural in origin.\u00a0 Thus, a broader framing is to consider the system level, where the agents in the system, be they computers or humans, are active, they are cooperative, and they wish to obtain value from their participation in learning-based systems.\u00a0 Agents may supply data and other resources to the system only if it is in their interest to do so, and they may be honest and cooperative only if it is in their interest to do so.\u00a0 Critically, intelligence inheres as much in the overall system as it does in individual agents.\u00a0 In the overall system, computation of equilibria replaces computation of optima.\u00a0 This is a perspective that is familiar in economics, although without the focus on learning algorithms.\u00a0 A key challenge is thus to bring (micro)economic concepts into contact with foundational issues in the computing, statistical, and decision-making sciences.\u00a0 I'll discuss some concrete examples of problems and solutions at this tripartite interface.<\/p> <h4>About Michael Jordan<\/h4> <p>Michael I. Jordan is a researcher at Inria Paris and Professor Emeritus at the University of California, Berkeley.\u00a0 His research interests bridge the computational, statistical, cognitive, biological and social sciences.\u00a0 Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering, a member of the American Academy of Arts and Sciences, a Foreign Member of the Royal Society, and a Foreign Member of the Chinese Academy of Sciences.\u00a0 He was a winner of a BBVA Foundation Frontiers of Knowledge Award in 2025 and was the inaugural winner of the World Laureates Association (WLA) Prize in 2022.\u00a0 He was a Plenary Lecturer at the International Congress of Mathematicians in 2018.\u00a0 He has received the Ulf Grenander Prize from the American Mathematical Society, the IEEE John von Neumann Medal, the IJCAI Research Excellence Award, the David E.\u00a0 Rumelhart Prize, and the ACM\/AAAI Allen Newell Award.\u00a0<\/p>\n<h3><strong>MONDAY, November 2, 9:45-10:45AM<\/strong><\/h3>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Karen-Smilowitz-194x173.jpg\" width=\"194\" height=\"173\" title=\"Karen Smilowitz \" alt=\"Karen Smilowitz \"> <h3> Karen Smilowitz <\/h3> Northwestern University\n<h4>The Use of Optimization Models in Public Sector Decision-making<\/h4>\n<ul><li><h4>Presentation and Karen Smilowitz Bio<\/h4><p>Operations research methods have been used to improve access to education for decades.\u00a0 The talk will focus specifically on connections between evolving issues in public education and advances in optimization, computing and geographic information systems.\u00a0 The talk will present examples of how operations research has impacted access to education.\u00a0 To illustrate the impact of such work, we present a case study from a research-practice partnership focused on district redesign to address access to education.\u00a0 \u00a0The talk will also reflect on challenges related to the use of optimization models in public sector decision-making.<\/p> <h4>About Karen Smilowitz<\/h4> <p>Dr. Karen Smilowitz is Associate Provost for Undergraduate Education at Northwestern University. \u00a0 She holds the James N. and Margie M. Krebs Professorship in Industrial Engineering and Management Science, with a joint appointment in the Operations group at the Kellogg School of Management.\u00a0 Dr. Smilowitz is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and nonprofit applications.\u00a0 She has been instrumental in promoting the use of operations research within the humanitarian and nonpro\ufb01t sectors through the Woodrow Wilson International Center for Scholars, the American Association for the Advancement of Science, and the National Academy of Engineering, as well as various media outlets.\u00a0 She received a CAREER award from the National Science Foundation and a Sloan Industry Studies Fellowship.\u00a0 She is an INFORMS Fellow and recently served as Editor-in-Chief of Transportation Science, the flagship journal of the INFORMS Transportation Science and Logistics Society.<\/p><\/li><\/ul>\n<p>Operations research methods have been used to improve access to education for decades.\u00a0 The talk will focus specifically on connections between evolving issues in public education and advances in optimization, computing and geographic information systems.\u00a0 The talk will present examples of how operations research has impacted access to education.\u00a0 To illustrate the impact of such work, we present a case study from a research-practice partnership focused on district redesign to address access to education.\u00a0 \u00a0The talk will also reflect on challenges related to the use of optimization models in public sector decision-making.<\/p> <h4>About Karen Smilowitz<\/h4> <p>Dr. Karen Smilowitz is Associate Provost for Undergraduate Education at Northwestern University. \u00a0 She holds the James N. and Margie M. Krebs Professorship in Industrial Engineering and Management Science, with a joint appointment in the Operations group at the Kellogg School of Management.\u00a0 Dr. Smilowitz is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and nonprofit applications.\u00a0 She has been instrumental in promoting the use of operations research within the humanitarian and nonpro\ufb01t sectors through the Woodrow Wilson International Center for Scholars, the American Association for the Advancement of Science, and the National Academy of Engineering, as well as various media outlets.\u00a0 She received a CAREER award from the National Science Foundation and a Sloan Industry Studies Fellowship.\u00a0 She is an INFORMS Fellow and recently served as Editor-in-Chief of Transportation Science, the flagship journal of the INFORMS Transportation Science and Logistics Society.<\/p>\n<h3><strong>TUESDAY, November 3, 9:45-10:45AM<\/strong><\/h3>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/guido-220x220.jpg\" width=\"220\" height=\"220\" title=\"GUIDO IMBENS\" alt=\"GUIDO IMBENS\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/guido-220x220.jpg 220w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/guido-150x150.jpg 150w\" sizes=\"(max-width: 220px) 100vw, 220px\" \/> <h3> GUIDO IMBENS <\/h3> Stanford University\n<h4>TBA<\/h4>\n<ul><li><h4>Guido Imbens Bio<\/h4><p>Guido Imbens is Professor at the Stanford Graduate School of Business and the Economics Department at Stanford University and consults for tech companies in Silicon Valley. Previously he held tenured positions at UCLA, UC Berkeley, and Harvard University. Imbens specializes in methods for drawing causal inferences from experimental and observational data, currently focusing on methods for bringing causality into AI models. Guido Imbens is a fellow of the Econometric Society, the Royal Holland Society of Sciences and Humanities, the Royal Netherlands Academy of Sciences, the American Academy of Arts and Sciences, and the National Academy of Sciences. He holds honorary doctorates from the University of St. Gallen, The University of Hull, Erasmus University, Brown University and the European University Institute in Florence. In 2021 he shared the Nobel prizes in economics with David Card and Joshua Angrist for \"methodological contributions to the analysis of causal relationships.\"<\/p><\/li><\/ul>\n<p>Guido Imbens is Professor at the Stanford Graduate School of Business and the Economics Department at Stanford University and consults for tech companies in Silicon Valley. Previously he held tenured positions at UCLA, UC Berkeley, and Harvard University. Imbens specializes in methods for drawing causal inferences from experimental and observational data, currently focusing on methods for bringing causality into AI models. Guido Imbens is a fellow of the Econometric Society, the Royal Holland Society of Sciences and Humanities, the Royal Netherlands Academy of Sciences, the American Academy of Arts and Sciences, and the National Academy of Sciences. He holds honorary doctorates from the University of St. Gallen, The University of Hull, Erasmus University, Brown University and the European University Institute in Florence. In 2021 he shared the Nobel prizes in economics with David Card and Joshua Angrist for \"methodological contributions to the analysis of causal relationships.\"<\/p>\n<h3><strong>WEDNESDAY, November 4, 11AM-12Noon<\/strong><\/h3>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Andrea-Lodi.jpg\" title=\"ANDREA LODI\" alt=\"Andrea Lodi\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Andrea-Lodi.jpg 770w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Andrea-Lodi-269x300.jpg 269w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Andrea-Lodi-768x858.jpg 768w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/> <h3> ANDREA LODI <\/h3> Cornell Tech\n<h4>Three Ideas toward GPU-Enhanced Mixed-Integer Optimization<\/h4>\n<ul><li><h4>Presentation and Andrea Lodi Bio<\/h4><p>The possibility of using GPUs to enhance the parallelization of Mixed-Integer Optimization algorithms has been discussed for several years, but the development of new approaches has been slower than anticipated. This is due to several factors, including the technological burden of moving data between CPUs, where the computation has traditionally been performed, and GPUs, as well as a more fundamental question: which algorithmic components of branch and bound should be executed on GPUs, and how should they be redesigned to exploit massive parallelism?<\/p> <p>The recent rediscovery of first-order methods for linear programming (LP), and the subsequent efficient implementations of the primal-dual hybrid gradient algorithm, have renewed interest in the topic. This has led software vendors to add first-order methods for LP to their algorithmic arsenal and experiment with branch-and-bound versions based on them, while GPU hardware vendors have started developing their own optimization solvers. These developments raise a broader question: should GPUs simply be used to accelerate existing Mixed-Integer Optimization algorithms, or should we rethink the algorithms themselves to better match the hardware?<\/p> <p>Within this exciting context, we propose three ideas that could contribute to the development of effective GPU-enhanced exact algorithms for Mixed-Integer Optimization. First, we show how to effectively batch similar LPs associated with important branch-and-bound components, such as strong branching and bound tightening, and solve them in parallel on GPUs. Second, we extend the batching idea to the branch-and-bound tree itself, processing multiple tree nodes in parallel on GPUs rather than following the traditional one-node-at-a-time computation. Third, we observe that this approach is not restricted to first-order methods for solving linear or convex relaxations: lightweight neural proxies, such as graph neural networks, can be designed to provide valid bounds and replace more expensive relaxation solvers while preserving the exactness of branch and bound.<\/p> <p>Together, these three ideas suggest that fully exploiting GPUs for Mixed-Integer Optimization may require more than accelerating individual components of existing solvers. It may require rethinking the architecture of exact algorithms around massive parallelism, while preserving the mathematical guarantees that make them exact.<\/p> <h4><strong>About Andrea Lodi<\/strong><\/h4> <p>Andrea Lodi is an Andrew H. and Ann R. Tisch Professor at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion. He is a member of both the Operations Research and Information Engineering and the Computer Science fields at Cornell University. Before joining Cornell, he was a Herman Goldstine Fellow at the IBM Mathematical Sciences Department, NY in 2005\u20132006, full professor of Operations Research at DEI, University of Bologna 2007-2015, and Canada Excellence Research Chair in \u201cData Science for Real-time Decision Making\u201d at Polytechnique Montr\u00e9al 2015-2022. His main research interests are in Mixed-Integer Linear and Nonlinear Programming and Data-driven Optimization, and his work has received several recognitions including the IBM and Google faculty awards. Andrea is the recipient of the INFORMS Optimization Society 2021 Farkas Prize and has been elected an INFORMS Fellow in 2023. Andrea has been the principal investigator of scientific projects (often involving industrial partners) for Italy, European Union, Canada, and USA. In the period 2006-2021, he was a consultant of the IBM CPLEX research and development team, developing CPLEX, one of the leading software for Mixed-Integer Optimization.\u00a0<\/p><\/li><\/ul>\n<p>The possibility of using GPUs to enhance the parallelization of Mixed-Integer Optimization algorithms has been discussed for several years, but the development of new approaches has been slower than anticipated. This is due to several factors, including the technological burden of moving data between CPUs, where the computation has traditionally been performed, and GPUs, as well as a more fundamental question: which algorithmic components of branch and bound should be executed on GPUs, and how should they be redesigned to exploit massive parallelism?<\/p> <p>The recent rediscovery of first-order methods for linear programming (LP), and the subsequent efficient implementations of the primal-dual hybrid gradient algorithm, have renewed interest in the topic. This has led software vendors to add first-order methods for LP to their algorithmic arsenal and experiment with branch-and-bound versions based on them, while GPU hardware vendors have started developing their own optimization solvers. These developments raise a broader question: should GPUs simply be used to accelerate existing Mixed-Integer Optimization algorithms, or should we rethink the algorithms themselves to better match the hardware?<\/p> <p>Within this exciting context, we propose three ideas that could contribute to the development of effective GPU-enhanced exact algorithms for Mixed-Integer Optimization. First, we show how to effectively batch similar LPs associated with important branch-and-bound components, such as strong branching and bound tightening, and solve them in parallel on GPUs. Second, we extend the batching idea to the branch-and-bound tree itself, processing multiple tree nodes in parallel on GPUs rather than following the traditional one-node-at-a-time computation. Third, we observe that this approach is not restricted to first-order methods for solving linear or convex relaxations: lightweight neural proxies, such as graph neural networks, can be designed to provide valid bounds and replace more expensive relaxation solvers while preserving the exactness of branch and bound.<\/p> <p>Together, these three ideas suggest that fully exploiting GPUs for Mixed-Integer Optimization may require more than accelerating individual components of existing solvers. It may require rethinking the architecture of exact algorithms around massive parallelism, while preserving the mathematical guarantees that make them exact.<\/p> <h4><strong>About Andrea Lodi<\/strong><\/h4> <p>Andrea Lodi is an Andrew H. and Ann R. Tisch Professor at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion. He is a member of both the Operations Research and Information Engineering and the Computer Science fields at Cornell University. Before joining Cornell, he was a Herman Goldstine Fellow at the IBM Mathematical Sciences Department, NY in 2005\u20132006, full professor of Operations Research at DEI, University of Bologna 2007-2015, and Canada Excellence Research Chair in \u201cData Science for Real-time Decision Making\u201d at Polytechnique Montr\u00e9al 2015-2022. His main research interests are in Mixed-Integer Linear and Nonlinear Programming and Data-driven Optimization, and his work has received several recognitions including the IBM and Google faculty awards. Andrea is the recipient of the INFORMS Optimization Society 2021 Farkas Prize and has been elected an INFORMS Fellow in 2023. Andrea has been the principal investigator of scientific projects (often involving industrial partners) for Italy, European Union, Canada, and USA. In the period 2006-2021, he was a consultant of the IBM CPLEX research and development team, developing CPLEX, one of the leading software for Mixed-Integer Optimization.\u00a0<\/p>\n<h2>Keynotes<\/h2>\n<h3><strong>SUNDAY, November 1, 5:45-6:35PM<\/strong><\/h3>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/mengdi-wang-194x204.png\" width=\"194\" height=\"204\" title=\"MENGDI WANG\" alt=\"Mengdi Wang\"> <h3> MENGDI WANG <\/h3> Princeton University\n<h3><br\/>OMEGA RHO DISTINGUISHED LECTURE<\/h3>\n<h4><strong>LabOS: The AI-XR Co-Scientist That Sees and Works With Humans<\/strong><\/h4>\n<ul><li><h4>Presentation and Mengdi Wang Bio<\/h4><p>Modern science advances fastest when thought meets action. LabOS represents the first AI co-scientist that unites computational reasoning with physical experimentation through multimodal perception, self-evolving agents, and Extended-Reality(XR)-enabled human-AI collaboration. By connecting multi-model AI agents, smart glasses, and robots, LabOS allows AI to see what scientists see, understand experimental context, and assist in real-time execution. Across applications -- from cancer immunotherapy target discovery to stem-cell engineering and material science -- LabOS shows that AI can move beyond computational design to participation, turning the laboratory into an intelligent, collaborative environment where human and machine discovery evolve together.<\/p> <h4>About Mengdi Wang<\/h4> <p>Mengdi Wang is Co-Director of Princeton AI for Accelerated Invention, and Professor of the Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning at Princeton University. She is also affiliated with the Department of Computer Science, Omenn-Darling Bioengineering Institute, and Princeton Language+Intelligence. She was a visiting research scientist at Google DeepMind, IAS and Simons Institute on Theoretical Computer Science. Her research focuses on machine learning, reinforcement learning, generative AI, large language models, and AI for science. Mengdi received her PhD in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2013, where she was affiliated with the Laboratory for Information and Decision Systems and advised by Dimitri P. Bertsekas. Before that, she got her bachelor degree from the Department of Automation, Tsinghua University. Mengdi received the Young Researcher Prize in Continuous Optimization of the Mathematical Optimization Society in 2016 (awarded once every three years), the Princeton SEAS Innovation Award in 2016, the NSF Career Award in 2017, the Google Faculty Award in 2017, and the MIT Tech Review 35-Under-35 Innovation Award (China region) in 2018, WAIC YunFan Award 2022, American Automatic Control Council's Donald Eckman Award 2024 for \"extraordinary contributions to the intersection of control, dynamical systems, machine learning and information theory\". She serves as a Program Chair for ICLR 2023 and Senior AC for Neurips, ICML, COLT, associate editor for Harvard Data Science Review, Operations Research. Research supported by NSF, AFOSR, NIH, ONR, Google, Microsoft C3.ai, FinUP, RVAC Medicines, MURI, GenMab.<\/p><\/li><\/ul>\n<p>Modern science advances fastest when thought meets action. LabOS represents the first AI co-scientist that unites computational reasoning with physical experimentation through multimodal perception, self-evolving agents, and Extended-Reality(XR)-enabled human-AI collaboration. By connecting multi-model AI agents, smart glasses, and robots, LabOS allows AI to see what scientists see, understand experimental context, and assist in real-time execution. Across applications -- from cancer immunotherapy target discovery to stem-cell engineering and material science -- LabOS shows that AI can move beyond computational design to participation, turning the laboratory into an intelligent, collaborative environment where human and machine discovery evolve together.<\/p> <h4>About Mengdi Wang<\/h4> <p>Mengdi Wang is Co-Director of Princeton AI for Accelerated Invention, and Professor of the Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning at Princeton University. She is also affiliated with the Department of Computer Science, Omenn-Darling Bioengineering Institute, and Princeton Language+Intelligence. She was a visiting research scientist at Google DeepMind, IAS and Simons Institute on Theoretical Computer Science. Her research focuses on machine learning, reinforcement learning, generative AI, large language models, and AI for science. Mengdi received her PhD in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2013, where she was affiliated with the Laboratory for Information and Decision Systems and advised by Dimitri P. Bertsekas. Before that, she got her bachelor degree from the Department of Automation, Tsinghua University. Mengdi received the Young Researcher Prize in Continuous Optimization of the Mathematical Optimization Society in 2016 (awarded once every three years), the Princeton SEAS Innovation Award in 2016, the NSF Career Award in 2017, the Google Faculty Award in 2017, and the MIT Tech Review 35-Under-35 Innovation Award (China region) in 2018, WAIC YunFan Award 2022, American Automatic Control Council's Donald Eckman Award 2024 for \"extraordinary contributions to the intersection of control, dynamical systems, machine learning and information theory\". She serves as a Program Chair for ICLR 2023 and Senior AC for Neurips, ICML, COLT, associate editor for Harvard Data Science Review, Operations Research. Research supported by NSF, AFOSR, NIH, ONR, Google, Microsoft C3.ai, FinUP, RVAC Medicines, MURI, GenMab.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Cynthia-Dwork-818x1024-194x220.jpg\" width=\"194\" height=\"220\" title=\"Cynthia Dwork\" alt=\"Harvard University\"> <h3> Cynthia Dwork <\/h3> Harvard University\n<h4>Indistinguishability: The Stuff of Magic<\/h4>\n<ul><li><h4>Presentation and Cynthia Dwork Bio<\/h4><p>CS theory has given us many magical concepts, such as zero-knowledge proofs, pseudo-randomness, privacy-preserving data analysis, and public-key cryptography. These, and many others, share a common thread: their very definitions are based on indistinguishability, a core concept in complexity theory. Indistinguishability is a form of impossibility: encryptions of zero cannot be distinguished from encryptions of one; outputs of an analysis operating on a dataset D cannot be distinguished from outputs of an analysis operating on D+Me (or D+You). It is remarkable that this negative, complexity-based notion has proven so powerful as an instrument of positive algorithmic construction. We will survey several flavors of indistinguishability and their applications, and conclude with thoughts on the role indistinguishability can play in addressing pressing questions of values in modern computer systems.<\/p> <h4><strong>About Cynthia Dwork<\/strong><\/h4> <p>Cynthia Dwork, Gordon McKay Professor of Computer Science at Harvard, and Affiliated Faculty at Harvard Law School and Department of Statistics, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation through her invention of Differential Privacy. She has also made seminal contributions in cryptography and distributed computing, and she spearheaded the field of algorithmic fairness.\u00a0 Her honors include the US National Medal of Science, the Japan Prize, the Hamming Medal, and the Dijkstra, G\u00f6del, Knuth, and Kanellakis Awards. She is a member of the US National Academy of Sciences and the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences and the American Philosophical Society.<\/p><\/li><\/ul>\n<p>CS theory has given us many magical concepts, such as zero-knowledge proofs, pseudo-randomness, privacy-preserving data analysis, and public-key cryptography. These, and many others, share a common thread: their very definitions are based on indistinguishability, a core concept in complexity theory. Indistinguishability is a form of impossibility: encryptions of zero cannot be distinguished from encryptions of one; outputs of an analysis operating on a dataset D cannot be distinguished from outputs of an analysis operating on D+Me (or D+You). It is remarkable that this negative, complexity-based notion has proven so powerful as an instrument of positive algorithmic construction. We will survey several flavors of indistinguishability and their applications, and conclude with thoughts on the role indistinguishability can play in addressing pressing questions of values in modern computer systems.<\/p> <h4><strong>About Cynthia Dwork<\/strong><\/h4> <p>Cynthia Dwork, Gordon McKay Professor of Computer Science at Harvard, and Affiliated Faculty at Harvard Law School and Department of Statistics, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation through her invention of Differential Privacy. She has also made seminal contributions in cryptography and distributed computing, and she spearheaded the field of algorithmic fairness.\u00a0 Her honors include the US National Medal of Science, the Japan Prize, the Hamming Medal, and the Dijkstra, G\u00f6del, Knuth, and Kanellakis Awards. She is a member of the US National Academy of Sciences and the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences and the American Philosophical Society.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Van-Roy-731x1024-194x211.jpg\" width=\"194\" height=\"211\" title=\"Benjamin Van Roy \" alt=\"Benjamin Van Roy \"> <h3> Benjamin Van Roy <\/h3> Stanford University\n<h3><br\/>Morse Lectureship<\/h3>\n<h4>Aligning Superintelligence<\/h4>\n<ul><li><h4>Benjamin Van Roy Bio<\/h4><p>\u00a0<\/p> <h4>About Benjamin Van Roy<\/h4> <p>Benjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His research focuses on reinforcement learning and alignment. Beyond academia, he founded the Efficient Agent Team \u2013 DeepMind's first US-based research team (now part of Google) \u2013 and Enuvis (acquired by SiRF\/Qualcomm). He has also led research programs at Morgan Stanley and Unica (acquired by IBM). He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT, where his doctoral research was advised by John N. Tsitsiklis.<\/p> <p>He is a Fellow of INFORMS and IEEE and has served on the editorial boards of Machine Learning, Mathematics of Operations Research, for which he edited the Learning Theory Area, Operations Research, for which he edited the Financial Engineering Area, the INFORMS Journal on Optimization, and Foundations and Trends in Machine Learning. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master's Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, the Management Science and Engineering Department's Graduate Teaching Award, and the INFORMS Frederick W. Lanchester Prize.<\/p> <p>He has graduated dozens of doctoral students, who have gone on to careers in academia (Carnegie Mellon, Columbia, Cornell, MIT, Northwestern, Rice, Stanford, USC), technology (Adobe, Amazon, DeepMind, Meta, Microsoft, Netflix, OpenAI, Spotify, Tesla, xAI), and finance (Citadel, DE Shaw, Goldman Sachs, Jane Street, Morgan Stanley, Two Sigma).<\/p><\/li><\/ul>\n<p>\u00a0<\/p> <h4>About Benjamin Van Roy<\/h4> <p>Benjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His research focuses on reinforcement learning and alignment. Beyond academia, he founded the Efficient Agent Team \u2013 DeepMind's first US-based research team (now part of Google) \u2013 and Enuvis (acquired by SiRF\/Qualcomm). He has also led research programs at Morgan Stanley and Unica (acquired by IBM). He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT, where his doctoral research was advised by John N. Tsitsiklis.<\/p> <p>He is a Fellow of INFORMS and IEEE and has served on the editorial boards of Machine Learning, Mathematics of Operations Research, for which he edited the Learning Theory Area, Operations Research, for which he edited the Financial Engineering Area, the INFORMS Journal on Optimization, and Foundations and Trends in Machine Learning. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master's Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, the Management Science and Engineering Department's Graduate Teaching Award, and the INFORMS Frederick W. Lanchester Prize.<\/p> <p>He has graduated dozens of doctoral students, who have gone on to careers in academia (Carnegie Mellon, Columbia, Cornell, MIT, Northwestern, Rice, Stanford, USC), technology (Adobe, Amazon, DeepMind, Meta, Microsoft, Netflix, OpenAI, Spotify, Tesla, xAI), and finance (Citadel, DE Shaw, Goldman Sachs, Jane Street, Morgan Stanley, Two Sigma).<\/p>\n<h3><strong>MONDAY, nOVEMBER 2, 5:45-6:35PM<\/strong><\/h3>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/08\/Edelman-194x187.jpg\" width=\"194\" height=\"187\" title=\"KONSTANTINA MELLOU\" alt=\"Microsoft\"> <h4> KONSTANTINA MELLOU <\/h4> Microsoft\n<h3><br\/>2026 INFORMS franz edelman award REPRISE<\/h3>\n<h4>Microsoft Cloud Supply Chain: Democratizing Hyperscale Optimization for Cloud Fulfillment\u00a0<\/h4>\n<ul><li><h4>Reprise Presentation and About Microsoft<\/h4><p>Microsoft transformed Cloud Supply Chain with the Intelligent Fulfillment Service (IFS), a breakthrough platform that combines machine learning, mathematical optimization, and generative AI. By automating global shipment planning, IFS cuts cycle times in half and delivers tens to hundreds of millions of dollars in annual savings while helping mitigate tariff exposure. Its large-language-model-powered assistant, built on the pioneering OptiGuide framework, brings real-time explainability and scenario exploration to planners, significantly reducing the fulfillment team's workload by compressing decision cycles from days to minutes.<\/p> <p>Microsoft creates platforms and tools powered by AI to deliver innovative solutions that meet the evolving needs of our customers. The technology company is committed to making AI available broadly and doing so responsibly, with a mission to empower every person and every organization on the planet to achieve more.<\/p><\/li><\/ul>\n<p>Microsoft transformed Cloud Supply Chain with the Intelligent Fulfillment Service (IFS), a breakthrough platform that combines machine learning, mathematical optimization, and generative AI. By automating global shipment planning, IFS cuts cycle times in half and delivers tens to hundreds of millions of dollars in annual savings while helping mitigate tariff exposure. Its large-language-model-powered assistant, built on the pioneering OptiGuide framework, brings real-time explainability and scenario exploration to planners, significantly reducing the fulfillment team's workload by compressing decision cycles from days to minutes.<\/p> <p>Microsoft creates platforms and tools powered by AI to deliver innovative solutions that meet the evolving needs of our customers. The technology company is committed to making AI available broadly and doing so responsibly, with a mission to empower every person and every organization on the planet to achieve more.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Dolores-Romero-Morales-808x1024-194x196.jpg\" width=\"194\" height=\"196\" title=\"Dolores Romero Morales \" alt=\"Dolores Romero Morales \"> <h3> Dolores Romero Morales <\/h3> Copenhagen Business School\n<h4>Optimization Models for Group-Level Explainability in Machine Learning<\/h4>\n<ul><li><h4>Presentation and Dolores Romero Morales Bio <\/h4><p>State-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms have become ubiquitous across industries due to their high predictive performance. However, despite their widespread deployment, these models are often criticized for their lack of transparency and accountability. Their \u201cblack-box\u201d nature obscures the reasoning behind decisions, limiting trust and hindering their use in critical, data-driven decision-making processes. Moreover, algorithmic decisions can perpetuate or amplify societal biases, leading to unfair and discriminatory outcomes. These concerns are especially pressing in high-stakes domains such as healthcare, criminal justice, credit scoring, and public benefits, where algorithmic decisions can significantly affect individuals\u2019 lives.<\/p> <p>In Explainable Artificial Intelligence (XAI), local explanations seek to shed light on the prediction of a machine learning model at a given instance. Popular methodologies include LIME, which builds an interpretable surrogate model that approximates predictions around the instance, and SHAP, which attributes the difference between the instance prediction and a baseline prediction to individual features. While such explanations are valuable to the individuals affected by a prediction, they provide limited information to modelers and decision-makers seeking to understand model behavior across many individuals. This raises broader questions concerning which features drive explanations across groups of instances, whether explanations are consistent and fair across individuals, and how individuals with similar explanations can be identified and represented collectively. This presentation shows how Operations Research provides a natural framework for addressing these questions and moving from individual explanations toward explainability at the group level.<\/p> <h4>About Dolores Romero Morales<\/h4> <p>Dolores Romero Morales is a Professor in Operations Research at Copenhagen Business School. Her areas of expertise include Data Science, Supply Chain Optimization and Revenue Management. In Data Science she investigates explainability\/interpretability, fairness and visualization matters. In Supply Chain Optimization she works on environmental issues and robustness. In Revenue Management she works on large-scale network models. Her work has appeared in a variety of leading scholarly journals, including European Journal of Operational Research, Management Science, Mathematical Programming and Operations Research. She has received various distinctions, such as the SEIO Medal for an outstanding contribution to the Operations Research discipline, and since June 2025, she is President-Elect of EURO - The Association of European Operational Research Societies.<\/p> <p>Dolores has received funding from the EU as well as national research councils to conduct her research. She has worked with and advised various companies on these topics, including IBM, SAS, KLM and Radisson Edwardian Hotels, as a result of which these companies managed to improve some of their practices. SAS named her an Honorary SAS Fellow and member of the SAS Academic Advisory Board. Currently, she is a member of the Editorial Board of the International Journal of Production Research, and an Associate Editor of Journal of the Operational Research Society, the INFORMS Journal on Data Science, and TOP-Transactions in Operations Research.<\/p> <p>Dolores joined Copenhagen Business School in 2014. Prior to coming to Copenhagen Business School, she was a Full Professor at University of Oxford (2003-2014) and an Assistant Professor at Maastricht University (2000-2003). She has a BSc and an MSc in Mathematics from Universidad de Sevilla and a PhD in Operations Research from Erasmus University Rotterdam.<\/p><\/li><\/ul>\n<p>State-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms have become ubiquitous across industries due to their high predictive performance. However, despite their widespread deployment, these models are often criticized for their lack of transparency and accountability. Their \u201cblack-box\u201d nature obscures the reasoning behind decisions, limiting trust and hindering their use in critical, data-driven decision-making processes. Moreover, algorithmic decisions can perpetuate or amplify societal biases, leading to unfair and discriminatory outcomes. These concerns are especially pressing in high-stakes domains such as healthcare, criminal justice, credit scoring, and public benefits, where algorithmic decisions can significantly affect individuals\u2019 lives.<\/p> <p>In Explainable Artificial Intelligence (XAI), local explanations seek to shed light on the prediction of a machine learning model at a given instance. Popular methodologies include LIME, which builds an interpretable surrogate model that approximates predictions around the instance, and SHAP, which attributes the difference between the instance prediction and a baseline prediction to individual features. While such explanations are valuable to the individuals affected by a prediction, they provide limited information to modelers and decision-makers seeking to understand model behavior across many individuals. This raises broader questions concerning which features drive explanations across groups of instances, whether explanations are consistent and fair across individuals, and how individuals with similar explanations can be identified and represented collectively. This presentation shows how Operations Research provides a natural framework for addressing these questions and moving from individual explanations toward explainability at the group level.<\/p> <h4>About Dolores Romero Morales<\/h4> <p>Dolores Romero Morales is a Professor in Operations Research at Copenhagen Business School. Her areas of expertise include Data Science, Supply Chain Optimization and Revenue Management. In Data Science she investigates explainability\/interpretability, fairness and visualization matters. In Supply Chain Optimization she works on environmental issues and robustness. In Revenue Management she works on large-scale network models. Her work has appeared in a variety of leading scholarly journals, including European Journal of Operational Research, Management Science, Mathematical Programming and Operations Research. She has received various distinctions, such as the SEIO Medal for an outstanding contribution to the Operations Research discipline, and since June 2025, she is President-Elect of EURO - The Association of European Operational Research Societies.<\/p> <p>Dolores has received funding from the EU as well as national research councils to conduct her research. She has worked with and advised various companies on these topics, including IBM, SAS, KLM and Radisson Edwardian Hotels, as a result of which these companies managed to improve some of their practices. SAS named her an Honorary SAS Fellow and member of the SAS Academic Advisory Board. Currently, she is a member of the Editorial Board of the International Journal of Production Research, and an Associate Editor of Journal of the Operational Research Society, the INFORMS Journal on Data Science, and TOP-Transactions in Operations Research.<\/p> <p>Dolores joined Copenhagen Business School in 2014. Prior to coming to Copenhagen Business School, she was a Full Professor at University of Oxford (2003-2014) and an Assistant Professor at Maastricht University (2000-2003). She has a BSc and an MSc in Mathematics from Universidad de Sevilla and a PhD in Operations Research from Erasmus University Rotterdam.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/photo-186x186.jpeg\" width=\"186\" height=\"186\" title=\"Peter Belcak \" alt=\"NVIDIA\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/photo-186x186.jpeg 186w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/photo-300x300.jpeg 300w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/photo-150x150.jpeg 150w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/photo.jpeg 399w\" sizes=\"(max-width: 186px) 100vw, 186px\" \/> <h3> Peter Belcak <\/h3> NVIDIA\n<h4><strong>TBA<\/strong><\/h4>\n<ul><li><h4>Peter Belcak Bio<\/h4><h4>About Peter Belcak<\/h4> <p>Peter Belcak is an AI researcher at NVIDIA Research. He works on making AI systems more reliable and efficient, with a focus on reducing the cost of building and running advanced AI. His research spans agentic systems, efficient deep learning, and practical methods for making AI more scalable and accessible.<\/p><\/li><\/ul>\n<h4>About Peter Belcak<\/h4> <p>Peter Belcak is an AI researcher at NVIDIA Research. He works on making AI systems more reliable and efficient, with a focus on reducing the cost of building and running advanced AI. His research spans agentic systems, efficient deep learning, and practical methods for making AI more scalable and accessible.<\/p>\n<h3><strong>TUESDAY, nOVEMBER 3, 5:45-6:35PM<\/strong><\/h3>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/06\/Tueng-Shen-764x1024-194x238.jpg\" width=\"194\" height=\"238\" title=\"Tueng Shen\" alt=\"Tueng Shen \"> <h3> Tueng Shen <\/h3> University of Washington\n<h4><strong>TBA<\/strong><\/h4>\n<ul><li><h4>Tueng Shen Bio<\/h4><h4>About Tueng T. Shen<\/h4> <p>Tueng T. Shen is the inaugural Associate Dean of Medical Technology Innovation, a joint position between UW Medicine and College of Engineering. As an eye surgeon as well as an engineer, Shen builds bridges between engineers and physicians to facilitate the translation of innovative engineering technologies into creative clinical solutions to transform health and healthcare. Shen seeks strong partnerships with our research\u00a0communities, technology industries and business communities to catalyze innovations that will improve healthcare delivery, especially leveraging UW Medicine\u2019s extensive WWAMI (Washington, Wyoming, Alaska, Montana and Idaho) network. Shen is a fellow of The American Institute for Medical and Biological Engineering (AIMBE). She is also the\u00a0inaugural Director of the Kren Engineering-based Medicine Institute at the University of Washington and elected member of the Washington State Academy of Sciences.<\/p><\/li><\/ul>\n<h4>About Tueng T. Shen<\/h4> <p>Tueng T. Shen is the inaugural Associate Dean of Medical Technology Innovation, a joint position between UW Medicine and College of Engineering. As an eye surgeon as well as an engineer, Shen builds bridges between engineers and physicians to facilitate the translation of innovative engineering technologies into creative clinical solutions to transform health and healthcare. Shen seeks strong partnerships with our research\u00a0communities, technology industries and business communities to catalyze innovations that will improve healthcare delivery, especially leveraging UW Medicine\u2019s extensive WWAMI (Washington, Wyoming, Alaska, Montana and Idaho) network. Shen is a fellow of The American Institute for Medical and Biological Engineering (AIMBE). She is also the\u00a0inaugural Director of the Kren Engineering-based Medicine Institute at the University of Washington and elected member of the Washington State Academy of Sciences.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/08\/UPS-logo.png\" title=\"Joel Sokol\" alt=\"Georgia Tech\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/08\/UPS-logo.png 250w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/08\/UPS-logo-150x150.png 150w\" sizes=\"(max-width: 250px) 100vw, 250px\" \/> <h3> Joel Sokol <\/h3> Georgia Tech\n<h3><br\/>2026 UPS George D. Smith Reprise<\/h3>\n<h4>Georgia Institute of Technology Team<\/h4>\n<ul><li><h4>About Georgia Institute of Technology<\/h4><p>Georgia Tech\u2019s Master of Science in Analytics (MSA) degree is an interdisciplinary analytics\/data science degree taught jointly <br>across computing, OR, statistics, and business. MSA has been ranked as high as #1 in Data Analytics, #3 in Data Science, and #3 <br>in Business Analytics.<\/p> <p>The MSA degree is offered in two ways: MSA Atlanta, an in-person program with premium perks like personalized career coaching, networking events, bootcamps, and alumni mentorship; and MSA Online (OMSA), a worldwide at-scale low-tuition program with proactive, high-touch advising and many opportunities for students to engage with each other and with instructors both within and outside of regular courses.<\/p> <p>MSA\u2019s curriculum is strongly informed by practitioner input, via an industry Advisory Board and alumni engagement. The innovative MSA curriculum includes a practice-focused integrated interdisciplinary core, five elective slots for personalization and specialization, non-technical skills training, a major applied practicum where each student works closely with a partner company\/organization, a dual-degree MSA\/MBA opportunity, and (because this is a rapidly-changing field) a learning-how-to-learn emphasis and the opportunity to return and take courses throughout one\u2019s career to keep pace with emerging trends.<\/p> <p>MSA is designed for accessibility, with a minimal set of prerequisites, availability anywhere in the world, a more affordable price point, a personalizable time scale, and a MicroMasters on-ramp for non-traditional students.<\/p> <p>MSA students and alumni are winning major national\/international analytics\/data-science contests, publishing in major journals and conferences in the field, and serving in positions from entry-level to C-level. MSA now has over 7000 alumni worldwide.<\/p><\/li><\/ul>\n<p>Georgia Tech\u2019s Master of Science in Analytics (MSA) degree is an interdisciplinary analytics\/data science degree taught jointly <br>across computing, OR, statistics, and business. MSA has been ranked as high as #1 in Data Analytics, #3 in Data Science, and #3 <br>in Business Analytics.<\/p> <p>The MSA degree is offered in two ways: MSA Atlanta, an in-person program with premium perks like personalized career coaching, networking events, bootcamps, and alumni mentorship; and MSA Online (OMSA), a worldwide at-scale low-tuition program with proactive, high-touch advising and many opportunities for students to engage with each other and with instructors both within and outside of regular courses.<\/p> <p>MSA\u2019s curriculum is strongly informed by practitioner input, via an industry Advisory Board and alumni engagement. The innovative MSA curriculum includes a practice-focused integrated interdisciplinary core, five elective slots for personalization and specialization, non-technical skills training, a major applied practicum where each student works closely with a partner company\/organization, a dual-degree MSA\/MBA opportunity, and (because this is a rapidly-changing field) a learning-how-to-learn emphasis and the opportunity to return and take courses throughout one\u2019s career to keep pace with emerging trends.<\/p> <p>MSA is designed for accessibility, with a minimal set of prerequisites, availability anywhere in the world, a more affordable price point, a personalizable time scale, and a MicroMasters on-ramp for non-traditional students.<\/p> <p>MSA students and alumni are winning major national\/international analytics\/data-science contests, publishing in major journals and conferences in the field, and serving in positions from entry-level to C-level. MSA now has over 7000 alumni worldwide.<\/p>\n<img src=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/07\/elena-fernandez.jpg\" title=\"Elena Fernandez\" alt=\"Elena Fernandez\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/07\/elena-fernandez.jpg 591w, https:\/\/meetings.informs.org\/wordpress\/annual\/files\/2026\/07\/elena-fernandez-200x300.jpg 200w\" sizes=\"(max-width: 591px) 100vw, 591px\" \/> <h3> Elena Fernandez <\/h3> University of C\u00e1diz\n<h3><br\/>IFORS DISTINGUISHED LECTURER<\/h3>\n<h4>Location Science, Once More: New Challenges for a Mature Area<\/h4>\n<ul><li><h4>Presentation and Elena Fernandez Bio<\/h4><p>Location Science is a well-established field that has evolved from its origins in geometry and economics into a broad spectrum of problems, many of them motivated by real-world applications. Operations Research provides an ideal framework for addressing location problems by combining theoretical, modeling, and algorithmic perspectives, thereby offering a comprehensive view of these problems.<\/p> <p>While the final decades of the twentieth century laid the foundations of modern Location Science, particularly in the context of discrete and network-based models, recent decades have witnessed a significant expansion of the field. New developments include hybrid models that integrate different types of decisions, as well as multi-level problems that capture the objectives of multiple stakeholders, often with conflicting interests, among many other advances.<\/p> <p>Although this broadening of the field is clearly reflected in the growing number of publications devoted to these challenging new models, the sense of community among researchers in the area appears to be gradually weakening.<\/p> <p>In this talk, I will explore some possible reasons behind this trend. I will also discuss several emerging challenges facing the field and outline potential directions for addressing them.<\/p> <h4>About Elena Fernandez<\/h4> <p>Elena Fernandez is full professor in Operations Research since 2007. She has spent much of her academic career at the Universitat Polit\u00e8cnica de Catalunya in Barcelona; since 2019 she is affiliated to the University of C\u00e1diz.<\/p> <p>Her research interest focuses on mathematical optimization models for discrete optimization, mainly on applications for transportation and logistics involving discrete location, network design and vehicle routing. She has published scientific papers in the flagship OR journals, including Operations Research, Transportation Science, and INFORMS Journal of Computing, with about 70 co-authors from a dozen of different countries.<\/p> <p>Elena has been plenary speaker at several international conferences and was the Chair of the Scientific Committee of the Royal Spanish Mathematical Society (RSME). In 2021 she received the Lifetime Achievement in Location Analysis Award 2021 of the Section on Locational Analysis (SOLA) of INFORMS.<\/p><\/li><\/ul>\n<p>Location Science is a well-established field that has evolved from its origins in geometry and economics into a broad spectrum of problems, many of them motivated by real-world applications. Operations Research provides an ideal framework for addressing location problems by combining theoretical, modeling, and algorithmic perspectives, thereby offering a comprehensive view of these problems.<\/p> <p>While the final decades of the twentieth century laid the foundations of modern Location Science, particularly in the context of discrete and network-based models, recent decades have witnessed a significant expansion of the field. New developments include hybrid models that integrate different types of decisions, as well as multi-level problems that capture the objectives of multiple stakeholders, often with conflicting interests, among many other advances.<\/p> <p>Although this broadening of the field is clearly reflected in the growing number of publications devoted to these challenging new models, the sense of community among researchers in the area appears to be gradually weakening.<\/p> <p>In this talk, I will explore some possible reasons behind this trend. I will also discuss several emerging challenges facing the field and outline potential directions for addressing them.<\/p> <h4>About Elena Fernandez<\/h4> <p>Elena Fernandez is full professor in Operations Research since 2007. She has spent much of her academic career at the Universitat Polit\u00e8cnica de Catalunya in Barcelona; since 2019 she is affiliated to the University of C\u00e1diz.<\/p> <p>Her research interest focuses on mathematical optimization models for discrete optimization, mainly on applications for transportation and logistics involving discrete location, network design and vehicle routing. She has published scientific papers in the flagship OR journals, including Operations Research, Transportation Science, and INFORMS Journal of Computing, with about 70 co-authors from a dozen of different countries.<\/p> <p>Elena has been plenary speaker at several international conferences and was the Chair of the Scientific Committee of the Royal Spanish Mathematical Society (RSME). In 2021 she received the Lifetime Achievement in Location Analysis Award 2021 of the Section on Locational Analysis (SOLA) of INFORMS.<\/p>","_links":{"self":[{"href":"https:\/\/meetings.informs.org\/wordpress\/annual\/wp-json\/wp\/v2\/pages\/12072","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meetings.informs.org\/wordpress\/annual\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/meetings.informs.org\/wordpress\/annual\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/annual\/wp-json\/wp\/v2\/users\/46"}],"replies":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/annual\/wp-json\/wp\/v2\/comments?post=12072"}],"version-history":[{"count":233,"href":"https:\/\/meetings.informs.org\/wordpress\/annual\/wp-json\/wp\/v2\/pages\/12072\/revisions"}],"predecessor-version":[{"id":13135,"href":"https:\/\/meetings.informs.org\/wordpress\/annual\/wp-json\/wp\/v2\/pages\/12072\/revisions\/13135"}],"wp:attachment":[{"href":"https:\/\/meetings.informs.org\/wordpress\/annual\/wp-json\/wp\/v2\/media?parent=12072"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}