Plenaries
Sunday
Daniel Kuhn
Distributionally Robust Optimization: The Science of Underpromising and Overdelivering
About Daniel Kuhn
Daniel Kuhn is a Professor of Operations Research in the College of Management of Technology at EPFL, where he holds the Chair of Risk Analytics and Optimization. His research interests revolve around stochastic, robust and distributionally robust optimization, and his principal goal is to develop efficient algorithms as well as statistical guarantees for data-driven optimization problems. This work is primarily application-driven, the main application areas being energy systems, machine learning, business analytics and finance. Before joining EPFL, Daniel Kuhn was a faculty member in the Department of Computing at Imperial College London and a postdoctoral researcher in the Department of Management Science and Engineering at Stanford University. He holds a PhD degree in Economics from the University of St. Gallen and an MSc degree in Theoretical Physics from ETH Zurich. He is an INFORMS fellow and the recipient of several research and teaching prizes including the Friedrich Wilhelm Bessel Research Award by the Alexander von Humboldt Foundation and the Frederick W. Lanchester Prize by INFORMS. He is the editor-in-chief of Mathematical Programming and the area editor for continuous optimization of Operations Research.
Monday
Anne Robinson
About Anne Robinson
As Chief Strategy Officer, Anne is responsible for advancing Kinaxis strategic development to add continued value to customers. Her team delivers the strategic roadmap, extensive thought leadership, as well as internal communications and change management. Recognized in analytics and digital transformation, Dr. Robinson has extensive experience managing supply chains for global organizations. At Verizon, she was responsible for the strategic vision of the global supply chains, driving excellence through analytics and process innovation. Previously, Anne managed analytics and business performance teams for Cisco’s supply chain. Dr. Robinson is a past president of INFORMS, seasoned industry speaker, and recipient of the 2020 Starr Excellence in Production and Operations Management Practice Award. In 2021, she joined the Creative Destruction Lab as a Supply Chain Mentor. Anne has a BScH from Acadia University, MASc from the University of Waterloo and MSc and PhD from Stanford University.
Tuesday
John Halamka
The Power of Platforms to Transform Health Care
True transformation of the health care sector requires us to move from pipeline thinking to a platform approach. To do this, we must capture diverse data sources at scale, constantly derive new insights from that data and create closed-loop solutions in a highly repeatable model. This presentation will focus on the work of Mayo Clinic Platform to create this model through privacy-protecting, federated, deidentified data behind glass that protects both data and intellectual property and the partnerships required to enable the greatest impact.
About John Halamka
John D. Halamka, M.D., M.S., president of the Mayo Clinic Platform, leads a portfolio of platform businesses focused on transforming health care by leveraging artificial intelligence, connected health care devices and a network of trusted partners.
Trained in emergency medicine and medical informatics, Dr. Halamka has been developing and implementing health care information strategy and policy for more than 25 years.
Prior to his appointment at Mayo Clinic, he was chief information officer at Beth Israel Deaconess Medical Center, where he served governments, academia and industry worldwide. He is a practicing emergency medicine physician.
As the International Healthcare Innovation Professor at Harvard Medical School, Dr. Halamka helped the George W. Bush administration, the Obama administration and governments around the world plan their health care information strategies.
Dr. Halamka completed his undergraduate studies at Stanford University, earned his medical degree at the University of California, San Francisco, and pursued graduate work in bioengineering at the University of California, Berkeley. He completed his residency at Harbor — UCLA Medical Center in the Department of Emergency Medicine. Dr. Halamka has written a dozen books about technology-related issues, hundreds of articles and thousands of posts on the Geekdoctor blog. He was elected to the National Academy of Medicine in 2020. He and his wife also run Unity Farm Sanctuary in Sherborn, Massachusetts – the largest animal sanctuary in New England, which includes 300 animals, 30 acres of agricultural production and a cidery.
Wednesday
David Simchi-Levi
Reinventing Operations Management’s Research and Practice with Data Science
About David Simchi-Levi
David Simchi-Levi is a Professor of Engineering Systems at MIT and serves as the head of the MIT Data Science Lab. He is considered one of the premier thought leaders in supply chain management and business analytics.
His PhD students have accepted faculty positions in leading academic institutes including U. of California Berkeley, Carnegie Mellon U., Columbia U., Cornell U., Duke U., Georgia Tech, Harvard U., U. of Illinois Urbana-Champaign, U. of Michigan, Purdue U. and Virginia Tech.
Professor Simchi-Levi is the current Editor-in-Chief of Management Science, one of the two flagship journals of INFORMS. He served as the Editor-in-Chief for Operations Research (2006-2012), the other flagship journal of INFORMS and for Naval Research Logistics (2003-2005).
In 2023, he was elected a member of the National Academy of Engineering. In 2020, he was awarded the prestigious INFORMS Impact Prize for playing a leading role in developing and disseminating a new highly impactful paradigm for the identification and mitigation of risks in global supply chains.
He is an INFORMS Fellow and MSOM Distinguished Fellow and the recipient of the 2020 INFORMS Koopman Award given to an outstanding publication in military operations research; Ford Motor Company 2015 Engineering Excellence Award; 2014 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice; 2014 INFORMS Revenue Management and Pricing Section Practice Award; and 2009 INFORMS Revenue Management and Pricing Section Prize.
He was the founder of LogicTools which provided software solutions and professional services for supply chain optimization. LogicTools became part of IBM in 2009. In 2012 he co-founded OPS Rules, an operations analytics consulting company. The company became part of Accenture in 2016. In 2014, he co-founded Opalytics, a cloud analytics platform company focusing on operations and supply chain decisions. The company became part of the Accenture Applied Intelligence in 2018.
Keynotes
Sunday
2023 Edelman Award Winner Reprise
Daniel Bienstock
The Electric Grid in Evolution: Data, Optimization, and Risk-Taking
About Daniel Bienstock
Daniel Bienstock is Liu Family Professor at the IEOR Department at Columbia University, with a joint appointment in applied mathematics, and a courtesy appointment in electrical engineering. His research focuses on all aspects of optimization, in particular computational issues in nonconvex and discrete optimization; and on algorithms for and analysis of electrical power systems. He was awarded the Khachiyan Prize in Optimization in 2022 and became an INFORMS Fellow in 2013. He received the PhD in operations research from MIT.
Jose Blanchet
Making Good Decisions with Wrong Models
Our capacity to access massive data sets and computing resources at scale has enabled the application of OR/MS methods in data driven decision making. But what is the impact of decisions that are made when the data is corrupted or possibly contains anomalies? Or when the deployment/prescription environment is different from the training/learning environment? These situations appear similar in the sense that direct data driven OR/MS approaches lead to making decisions based on wrong models, but, as we shall discuss, they are fundamentally different conceptually. In this talk, we will discuss the differences and provide a disciplined yet practical approach to dealing with these types of problems.
About Jose Blanchet
Jose Blanchet is a Professor of Management Science and Engineering (MS&E) at Stanford. Prior to joining MS&E, he was a Professor at the Departments of IEOR and Statistics at Columbia and before that a Professor of Statistics at Harvard. Jose is a recipient of the 2010 Erlang Prize and has won several best publication awards in areas such as applied probability, simulation, operations management, and revenue management. He also received a Presidential Early Career Award for Scientists and Engineers in 2010. He currently leads a Department of Defense sponsored Multi-University Research Initiative involving teams from Duke, Harvard, Maryland, MIT and Stanford on rare event analysis. He was the President of the INFORMS Applied Probability Society during 2020-2022 and has participated in various INFORMS committees. He is an area editor of Stochastic Models in Mathematics of Operations Research. He has served on the editorial board of Advances in Applied Probability, Bernoulli, Extremes, Insurance: Mathematics and Economics, Journal of Applied Probability, Queueing Systems: Theory and Applications, and Stochastic Systems, among others.
Monday
Julie Ivy
The Model Made Me Do It – Ethical ORMS in a Data-Driven World
ORMS has the power to help the world but with great power comes great responsibility. How are we considering the impact of our models and using that information to create better models?
About Julie Ivy
Julie Simmons Ivy, Ph.D., is a Professor and Fitts Faculty Fellow of Health Systems Engineering in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University with extensive background in decision making under conditions of uncertainty using stochastic and statistical modeling. She received her B.S. and Ph.D. in Industrial and Operations Engineering from the University of Michigan. She also received her M.S. in Industrial and Systems Engineering from Georgia Tech. She is an active member of the Institute of Operations Research and Management Science (INFORMS), Dr. Ivy served as the 2007 Chair (President) of the INFORMS Health Applications Society and the 2012 – 13 President for the INFORMS Minority Issues Forum. Recently, Dr. Ivy was elected as a 2022 INFORMS Fellow. In 2023, she was selected for the National Academies Board on Mathematical Sciences and Analytics (BMSA). Dr. Ivy’s research seeks to model complex interactions and quantitatively capture the impact of different factors, objectives, system dynamics, intervention options and policies on outcomes with the goal of improving decision quality. In particular, Dr. Ivy has extensive background in the application of systems science methods, including the analysis and modeling of large data sets, to hunger relief and health decision making. This research has made an impact on how researchers and practitioners address complex societal issues, such as health disparities, public health preparedness, hunger relief, student performance, and personalized medical decision-making and has been funded by the CDC, NSF, and NIH.
Omega Rho Lecture
Hamsa Balakrishnan
Building Smarter Air Transportation Systems: From Research to Practice
About Hamsa Balakrishnan
Hamsa Balakrishnan is the William E. Leonhard (1940) Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT), where she is also affiliated with the Operations Research Center and the Institute for Data, Systems, and Society. She was previously the Associate Department Head of Aeronautics and Astronautics at MIT. She received her PhD from Stanford University, and a B.Tech. from the Indian Institute of Technology Madras. Her research is in the design, analysis, and implementation of control and optimization algorithms for cyber-physical infrastructures, with an emphasis on air transportation. She is the co-founder and chief scientist of Lumo, a Boston-based travel startup.
Prof. Balakrishnan is an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA), and the recipient of an NSF CAREER Award in 2008, the inaugural CNA Award for Operational Analysis in 2012, the AIAA Lawrence Sperry Award in 2012, the American Automatic Control Council’s Donald P. Eckman Award in 2014, the MIT AIAA Undergrad Advising (2014) and Undergraduate Teaching (2019) Awards, and multiple best paper awards.
Jonathan Owen, CAP
Leveraging Analytics in Automotive
About Jonathan H. Owen, PhD, CAP
Jon Owen has served as director of GM’s Advanced Analytics Center of Expertise (AACE) in the Enterprise Data, Analytics, and Insights (EDAI) organization since January 1, 2019. He also serves as Chief Scientist for OR/MS and Analytics at GM since being named to the role in 2017.
In his current position, Jon leads strategic innovation for prescriptive analytics and applied data science activities across the company. His team partners with internal stakeholders to grow revenue, profit and operational effectiveness through improved data-driven decision making in diverse areas such as revenue management, portfolio planning, vehicle technology selection and content optimization, supply chain and logistics, market demand modeling, and dealer effectiveness.
Jon Owen has served as director of GM’s Advanced Analytics Center of Expertise (AACE) in the Enterprise Data, Analytics, and Insights (EDAI) organization since January 1, 2019. He also serves as Chief Scientist for OR/MS and Analytics at GM since being named to the role in 2017.
In his current position, Jon leads strategic innovation for prescriptive analytics and applied data science activities across the company. His team partners with internal stakeholders to grow revenue, profit and operational effectiveness through improved data-driven decision making in diverse areas such as revenue management, portfolio planning, vehicle technology selection and content optimization, supply chain and logistics, market demand modeling, and dealer effectiveness.
Prior to joining EDAI, Jon served as director of the Global R&D Operations Research Lab since 2013. In this role, Jon led internal research activities as well as collaboration with university partners, external labs, and other organizations to tackle GM’s most significant technical challenges and advance the state-of-the-art knowledge in applied OR/MS and analytics.
Jon began his career at GM in 1999 as a member of the research staff in R&D and Strategic Planning. He advanced through several roles, attaining the rank of Technical Fellow, GM’s highest technical classification, before being promoted to an executive position. He earned a BS degree from University of North Carolina, MS and PhD degrees from Northwestern University, and is a graduate of Harvard Business School’s General Management Program.
Jon’s contributions have been recognized by GM’s highest internal awards, as well as external awards from IISE, SME, and INFORMS. He is a recipient of Northwestern University’s IE/MS Distinguished Alumni Award and was inducted as an INFORMS Fellow in 2018. In addition to serving on several advisory boards, Jon currently serves on the Board of Directors for MATHCOUNTS (www.mathcounts.org), a non-profit organization that provides engaging math programs to middle school students of all ability levels to build confidence and improve attitudes about math and problem solving.
Yael Grushka-Cockayne
Harnessing data for Operations Forecasting
About Yael Grushka-Cockayne
Professor Yael Grushka-Cockayne’s research and teaching activities focus on decision analysis, data science, business analytics, forecasting, forecast aggregation and the wisdom of crowds, decision analysis, project management, and behavioral decision-making. Yael is an award-winning teacher and was named one of “21 Thought-Leader Professors” in data science. At Darden Yael teaches courses on decision analysis, project management, and data science in business. Yael’s “Fundamentals of Project Planning and Management” Coursera MOOC and “Data Science for Business” HarvardX course have over 300,000 enrolled, across 200 countries worldwide. Yael is an associate editor at Management Science, Operations Research, Decision Analysis, and INFORMS Journal on Data Science.
Before starting her academic career, she worked in San Francisco as a marketing director of an ERP company. As an expert in the areas of decision analysis and critical thinking, project management, and digital transformation, she has served as a consultant to international firms in the ed-tech, aerospace and pharma industries, such as Merck Serono, Pfizer, Eli Lilly, 2U, High Speed 2 Rail, PPL Electric Utilities, Heathrow Airport and Eurocontrol, Network Rail UK and the Department for Transport UK, and Dunlop Aerospace.
Tuesday
2023 Daniel H. Wagner Prize Winner Reprise
The Daniel H. Wagner Prize is awarded for a paper and presentation that describe a real-world, successful application of operations research or advanced analytics. The prize criteria emphasizes innovative, elegant mathematical modeling and clear exposition. To learn more about the prize, visit the information page.
Dorit Hochbaum
Network Flows and Minimum Cuts in Ranking, Clustering, Machine Learning, Imaging and Diversity Problems
About Dorit Hochbaum
Dorit S. Hochbaum is a distinguished professor in Industrial Engineering and Operations Research (IEOR) at UC Berkeley. Professor Hochbaum holds a PhD from the Wharton school of Business at the University of Pennsylvania. Her research interests are in areas of discrete optimization, network flow techniques, data mining, image segmentation, supply chain management and efficient utilization of resources. Her work contributed to the analysis of heuristics and approximation algorithms in the worst case, and on average, to the complexity analysis of algorithms in general, and nonlinear optimization algorithms in particular. Her theoretical work focuses on particularly efficient techniques using network flow for data mining and image segmentation including parametric flow for the convex Markov Random Fields problem establishing it as polynomial; the PseudoFlow algorithm for the maximum flow problem and the parametric flow and cut algorithms. Her recent research is on problems relating to machine learning on recognizing bias in labeled data; reducing dependence in training data; using pairwise relationships to enhance clustering methods. Applications include improved yield prediction in semi-conductor manufacturing; devising balanced covariates for experimental design; the maximum diversity and dispersion problem and group rankings and aggregate decision problems. Professor Hochbaum is the author of over 190 papers that appeared in the Operations Research, Management Science and Theoretical Computer Science literature. She served as department editor for Management Science department of optimization and modeling, and on a number of editorial boards.
Professor Hochbaum was named in 2004 as honorary doctorate of Sciences of the University of Copenhagen, for her work on approximation algorithms. She was appointed the Pinhas Naor lecturer of the Technion for 2013, and a Research Excellence professor at the University of Vienna in 2007. She is the winner of the 2011 INFORMS Computing Society prize for her work on algorithms for image segmentation. Professor Hochbaum is a fellow of INFORMS and a fellow of SIAM (Society of Industrial and Applied Mathematics).
Les Servi
Operations Research Methods in National Security
About Les Servi
Les Servi is the Chief Scientist for Cyber Operations Research at The MITRE Corporation and President of the Military Operations Research Society (MORS). In 2020 he led the optimization modeling effort for the Joint Acquisition Task Force established by USD(A&S) for a multi-tier Covid-19 medical supply chain. He previously served on a Defense Science Board task force on Counterinsurgency and another on Constrained Military Operations both briefed out to the USD(I). He received a Certificate of Appreciation from the Director of National Intelligence, James Clapper in 2017.
Previously he received his PhD from Harvard University, worked at Bell Laboratories and GTE (now Verizon) Laboratories pursuing telecommunication research, served 1 year as a visiting scientist at Harvard University and MIT, and worked at MIT Lincoln Lab.
He is an INFORM Fellow, former chair of the INFORMS Social Media Analysis, Telecommunication, and Applied Probability subdivisions and Board member of INFORMS for six years and MORS for four years. He is a former assistant editor of Operations Research, Management Science, and INFORMS Journal on Computing. He is currently a member of the WPI Data Science Executive Advisory Board and Board member and mentor for the Notre Dame Cristo Rey High School.
