Plenaries & Keynotes

Plenaries

SUNDAY, Novemeber 1, 9:30-10:45AM

MICHAEL JORDAN

University of California, Berkeley

TBA

About Michael Jordan

Michael I. Jordan is a researcher at Inria Paris and Professor Emeritus at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, biological and social sciences. 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. 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. He was a Plenary Lecturer at the International Congress of Mathematicians in 2018. 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. Rumelhart Prize, and the ACM/AAAI Allen Newell Award.

MONDAY, November 2, 9:45-10:45AM

Karen Smilowitz

Northwestern University

TBA

About Karen Smilowitz

Dr. Karen Smilowitz is Associate Provost for Undergraduate Education at Northwestern University. 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. Dr. Smilowitz is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and nonprofit applications. She has been instrumental in promoting the use of operations research within the humanitarian and nonproﬁt 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. She received a CAREER award from the National Science Foundation and a Sloan Industry Studies Fellowship. 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.

TUESDAY, November 3, 9:45-10:45AM

GUIDO IMBENS

Stanford University

TBA

About Guido Imbens

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.”

WEDNESDAY, November 4, 11AM-12Noon

ANDREA LODI

Cornell Tech

TBA

About Andrea Lodi

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 2005–2006, full professor of Operations Research at DEI, University of Bologna 2007-2015, and Canada Excellence Research Chair in “Data Science for Real-time Decision Making” at Polytechnique Montréal 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.

Keynotes

SUNDAY, November 1, 5:45-6:35PM

MENGDI WANG

Princeton University

OMEGA RHO DISTINGUISHED LECTURE

LabOS: The AI-XR Co-Scientist That Sees and Works With Humans

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.

About Mengdi Wang

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.

Cynthia Dwork

Harvard Law School

TBA

About Cynthia Dwork

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. Her honors include the US National Medal of Science, the Japan Prize, the Hamming Medal, and the Dijkstra, Gödel, 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.

Benjamin Van Roy

Stanford University

Morse Lectureship

TBA

About Benjamin Van Roy

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 – DeepMind’s first US-based research team (now part of Google) – 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.

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.

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).

MONDAY, nOVEMBER 2, 5:45-6:35PM

Konstantina Mellou

Microsoft

2026 INFORMS franz edelman award REPRISE

Microsoft Cloud Supply Chain: Democratizing Hyperscale Optimization for Cloud Fulfillment

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.

About Microsoft

Dolores Romero Morales

Copenhagen Business School

TBA

Peter Belcak

NVIDIA

TBA

About Peter Belcak

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.

TUESDAY, nOVEMBER 3, 5:45-6:35PM

Tueng Shen

University of Washington

TBA

About Tueng Shen

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
communities, technology industries and business communities to catalyze innovations that will improve healthcare delivery, especially leveraging UW Medicine’s 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
inaugural Director of the Kren Engineering-based Medicine Institute at the University of Washington and elected member of the Washington State Academy of Sciences.

GEORGIA TECH

Joel Sokol

2026 UPS George D. Smith Reprise

Georgia Institute of Technology Team

Georgia Tech’s Master of Science in Analytics (MSA) degree is an interdisciplinary analytics/data science degree taught jointly across computing, OR, statistics, and business. MSA has been ranked as high as #1 in Data Analytics, #3 in Data Science, and #3 in Business Analytics.

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.

MSA’s 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’s career to keep pace with emerging trends.

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.

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.

Elena Fernandez

IFORS DISTINGUISHED LECTURER

TBA

About Elena Fernandez