Tutorials

Tutorials are designed to disseminate timely work by leading researchers and are highly valued by the attendees. They often include an in-depth, state-of-the-art lay-of-the-land of a field, given by an expert. Tutorials are a great resource for newcomers to a field but they also provide a comprehensive review for others who are familiar with the topic. This year, we have planned multiple tutorials on a broad spectrum of topics, ranging from fire management and inverse optimization to random graphs and artificial intelligence. We look forward to seeing several of you at these tutorials.

The Continuous Approximation Paradigm in Logistics Systems Analysis

John Carlsson, University of Southern California

The continuous approximation (CA) paradigm has been an effective tool for obtaining managerial insights in logistics problems since the seminal papers of Few and Beardwood, Halton, and Hammersley in the 1950s. The core concept in CA is that one replaces detailed data in a problem instance with concise algebraic expressions. This tutorial will provide an overview of recent advancements in this area, as well as promising future research directions.

Carlsson's Biography

John Gunnar Carlsson is the Kellner Family Associate Professor of Industrial and Systems Engineering at the University of Southern California. He received a Ph.D. in computational mathematics from Stanford University in 2009 and an A.B. in music and mathematics from Harvard College in 2005. He is the recipient of Popular Science magazine’s Brilliant 10 Award, the AFOSR Young Investigator Prize, the INFORMS Computing Society (ICS) Prize, and the DARPA Young Faculty Award, and serves as an Associate Editor for Operations Research, Management Science, and Transportation Science.

Inverse Optimization: Theory and Application

Timothy Chan, University of Toronto

Inverse optimization describes a process that is the “reverse” of traditional mathematical optimization. Unlike traditional optimization, which seeks to compute optimal decisions given an objective and constraints, inverse optimization takes decisions as input and determines an objective and/or constraints that render these decisions approximately or exactly optimal. In recent years, there has been an explosion of interest in the mathematics and applications of inverse optimization. This tutorial will provide a comprehensive introduction to the theory and application of inverse optimization.

Chan's Biography

Timothy Chan is the Canada Research Chair in Novel Optimization and Analytics in Health, a Professor in the department of Mechanical and Industrial Engineering, Director of the Centre for Analytics and AI Engineering, Associate Director of the Data Sciences Institute, and a Senior Fellow of Massey College at the University of Toronto. His primary research interests are in operations research, optimization, and applied machine learning, with applications in healthcare, medicine, sustainability, and sports. He received his B.Sc. in applied mathematics from the University of British Columbia, and his Ph.D. in operations research from the Massachusetts Institute of Technology. Before coming to Toronto, he was an Associate in the Chicago office of McKinsey and Company. During that time, he advised leading companies in the fields of medical device technology, travel and hospitality, telecommunications, and energy on issues of strategy, organization, technology and operations.

O.R. and Analytics for Public Policy: Lessons from the Pandemic

Peter Frazier, Cornell University

The COVID-19 pandemic laid bare gaps in governments’ ability to base policy on data and quantitative reasoning. This talk argues that O.R. and analytics can help craft more effective policy and that the COVID-19 crisis has created a window of opportunity for change. We draw lessons from the speaker’s experience at Cornell University, where OR is a fundamental part of the university’s pandemic response. We focus on practical tools and techniques for policy-focused O.R.-based decision support in public health and other public- and private-sector decision domains.

Frazier's Biography

Peter Frazier is the Eleanor and Howard Morgan Professor of Operations Research and Information Engineering at Cornell University. He is also a Staff Data Scientist at Uber. He leads Cornell’s COVID-19 Mathematical Modeling Team, which designed Cornell’s testing strategy to support safe in-person education during the pandemic. His academic research during more ordinary times is in Bayesian optimization, incentive design for social learning and multi-armed bandits. At Uber, he managed UberPool’s data science group and currently helps to design Uber’s pricing systems. His work has won best paper and best student paper awards from the ACM Conference on Economics and Computation, the INFORMS Applied Probability Society, the INFORMS Computing Society, and the Winter Simulation Conference.

Wildfire Management: An Operational Research Perspective

David Martell, University of Toronto

Fire is a natural component of many forest ecosystems but wildfires can have very significant detrimental impacts on people, property, forest resources and infrastructure. British Columbia’s 2021 fire season is of course, a recent poignant example. Fire cannot nor should it be eliminated from the forest but that poses complex challenges to those that live with fire and those that manage fire. I will provide a brief overview of wildfire management, describe some fire management problems I have studied, and identify some important open problems I believe operational researchers can help resolve.

Martell's Biography

David Martell is a Professor Emeritus in the Institute of Forestry and Conservation at the University of Toronto. He completed his Ph.D. in the Department of Industrial Engineering at the University of Toronto where he studied O.R. and its application to forest fire management. He was assigned as a research scientist, to one of Ontario’s fire Incident Management Teams from 2010 until 2021. His current research interests include the development and implementation of decision support systems that can be used to enhance the management of detection and initial attack systems and forest landscape management under uncertainty. He was the 2009 recipient of the Canadian Operational Research Society’s Award of Merit and the 2020 recipient of the International Association of Wildland Fire’s Ember Award. He is an area editor of INFOR, and an associate editor of The Forestry Chronicle, the International Journal of Wildland Fire and the Canadian Journal of Forest Research.

Opinion Dynamics on Directed Random Graphs

Mariana Olvera-Cravioto, University of North Carolina

A popular way of modeling the exchange of information among individuals in a society is to use a large random graph whose vertices represent the individuals and whose edges represent acquaintances or friendships. Once the graph is realized we can model the exchange of information by defining a Markov chain on the graph whose transition probabilities determine how individuals will update their opinions once they listen to those of their acquaintances. If the listening relationship between individuals is not symmetric, we can assume the graph is directed. This tutorial will explain how to model and analyze opinion dynamics using the DeGroot-Friedkin model on directed random graphs, with the goal of proving conditions under which either consensus or polarization occurs. The techniques presented in the tutorial also extend to the analysis of a wide class of Markov chains on directed random graphs.

Olvera-Cravioto's Biography

Mariana Olvera-Cravioto has been an Associate Professor in the Department of Statistics and Operations Research at UNC Chapel Hill since 2018, and prior to that she was a faculty member at the Industrial Engineering and Operations Research departments at Columbia University and UC Berkeley. She obtained her Ph.D. in management science and engineering from Stanford University. Her recent research is mostly focused on the study of processes and algorithms on scale-free random graphs, such as those used to model the web and other social networks. More broadly, she is interested in random graph theory, branching processes, distributional fixed-point equations, heavy-tailed phenomena, and stochastic simulation. She serves as Associate Editor for QUESTA, Stochastic Models, and Stochastics.

Markov Decision Processes in Health Care

Steven Shechter, University of British Columbia

Markov Decision Processes (MDPs) provide a rich framework for sequential decision making under uncertainty. This tutorial will introduce theory, algorithms, and health care applications of MDPs. It will begin with basic MDP concepts, solution methods, and structural properties, and then provide overviews of two major extensions of MDPs: Partially Observable MDPs, and Reinforcement Learning. Applications in health care will be interspersed throughout, covering both system-level (i.e., resource allocation) and patient-level (i.e., medical decision making) models.

Shechter's Biography

Steven Shechter is a Professor in the Sauder School of Business at the University of British Columbia. He joined UBC in 2006, after receiving his Ph.D. in industrial engineering from the University of Pittsburgh. His primary research interests are in the application of optimization, dynamic programming, and simulation to health care. Recent projects include optimizing alarm thresholds for patients in an ICU, allocating operating room time for pediatric elective surgeries, timing vascular surgery for hemodialysis patients, and screening patients awaiting kidney transplant. Steven is a past recipient of the Career Investigator Award from the Michael Smith Foundation for Health Research in BC. He serves as an Associate Editor at Management Science and a Senior Editor at Production and Operations Management.

Analytics for Social Impact

Phebe Vayanos, University of Southern California

We discuss work in collaboration with community partners and policymakers focused on homelessness and public health in vulnerable communities. We present research advances to address one key cross-cutting question: how to assign scarce intervention resources while accounting for the challenges of open world deployment? We show concrete improvements over the state of the art based on real world data. We are convinced that by pushing this line of research, analytics can play a crucial role to help fight injustice and solve complex problems facing our society.

Vayanos's Biography

Phebe Vayanos is an Assistant Professor of Industrial & Systems Engineering and Computer Science at the University of Southern California. She is also an Associate Director of the Center for Artificial Intelligence in Society (CAIS) at USC. Her research is focused on operations research and artificial intelligence and in particular on optimization and machine learning. Her work is motivated by problems that are important for social good, such as those arising in public housing allocation, public health, and biodiversity conservation. Prior to joining USC, she was lecturer in the Operations Research and Statistics Group at the MIT Sloan School of Management, and a postdoctoral research associate in the Operations Research Center at MIT. She holds a Ph.D. degree in operations research and an MEng degree in electrical & electronic engineering, both from Imperial College London. She serves as a member of the ad hoc INFORMS AI Strategy Advisory Committee, she is an elected member of the Committee on Stochastic Programming (COSP), and the VP of Communications for the INFORMS Section on Public Sector Operations Research. She is an Associate Editor for Operations Research Letters and Computational Management Science. She is a recipient of the NSF CAREER award and the INFORMS Diversity, Equity, and Inclusion Ambassador Program Award.

Causal Inference in the Presence of Network Interference

Christina Lee Yu, Cornell University

Randomized experiments are widely used to estimate causal effects of proposed “treatments” in domains spanning the physical and biological sciences, social sciences, engineering, medicine and health, as well as in public service domains and the technology industry. However, classical approaches to experimental design rely on critical independence assumptions that are violated when the outcome of an individual A may be affected by the treatment of another individual B, referred to as network interference. This interference introduces computational and statistical challenges to causal inference. In this tutorial, we will survey the challenges of causal inference under network interference and the different approaches proposed in the literature to account for network interference. We will present a new hierarchy of models and estimators that enable statistically efficient and computationally simple solutions under nonparametric polynomial models, with theoretical guarantees even in settings where the network is completely unknown, the data is observational, or the model is misspecified.

Lee Yu's Biography

Christina Lee Yu is an Assistant Professor at Cornell University in the School of Operations Research and Information Engineering. Prior to Cornell, she was a postdoc at Microsoft Research New England. She received her Ph.D. and MS in electrical engineering and computer science from Massachusetts Institute of Technology, and she received her BS in computer science from California Institute of Technology. She received honorable mention for the 2018 INFORMS Dantzig Dissertation Award, and she is a recipient of the 2021 Intel Rising Stars Award and 2021 JPMorgan Faculty Research Award. Her research interests include algorithm design and analysis, high dimensional statistics, inference over networks, sequential decision making under uncertainty, online learning, and network causal inference.