Business Analytics Series
In this Thought Leaders series organized by Tippie's Business Analytics department, leading-edge researchers from the field of Business Analytics spoke about some of the most critical issues in the field.
All seminars took place via Zoom and most were recorded. Video links are provided below.
Dr. Xi Chen
New York University
Topic: Robust Online Learning and Its Applications to Assortment Optimization
Abstract: In this talk, we will first provide an overview of my research on online learning and decision-making. We will highlight a few applications on dynamic pricing, assortment optimization for online recommendation, and crowdsourcing.
Most online learning problems are built on an underlying probabilistic model. However, these models are inherently misspecified to a certain degree, which calls for robust learning methods. The second part of the talk will discuss robust online learning and its applications to recommendations. In particular, we consider the dynamic assortment optimization problem under the multinomial logit model (MNL) with unknown utility parameters. Based on online eps-contamination modeling of customers’ purchase behavior, we develop a rate-optimal robust online assortment optimization policy via an active elimination strategy.
Bio: Xi Chen is an associate professor at Stern School of Business at New York University, who is also an affiliated professor to Computer Science and Center for Data Science. Before that, he was a Postdoc in the group of Prof. Michael Jordan at UC Berkeley. He obtained his PhD from the Machine Learning Department at Carnegie Mellon University. He studies high-dimensional statistical learning, online learning, large-scale stochastic optimization, and applications to revenue management and crowdsourcing. He received an NSF Career Award and Outstanding Faculty Research Awards from Google, Facebook, Adobe, Alibaba, Bloomberg, and JPMorgan, and was featured in Forbes list of "30 Under 30 in Science."
Dr. Rong Chen
Topic: New Approaches to Statistical Learning of Modern Time Series
Abstract: In the BIGDATA era, many new forms of data have become available and useful in various important applications. When these data are observed over time, they form new types of time series that require new statistical models and analytical tools in order to extract useful information. In this talk we present new developments in analyzing matrix/tensor time series, dynamic networks, functional time series and compositional time series, with applications ranging from economics, finance, international trade, electricity loading and others. We will also briefly discuss approaches on modeling other forms of time series, including text time series and dynamic social networks.
Bio: Dr. Chen is a Distinguished Professor of Statistics and Chair of the Department of Statistics at Rutgers University. His teaching and research interests include analysis of complex time series and dynamic systems, Monte Carlo methods and statistical applications in bioinformatics, business and economics, and engineering. He is an elected Fellow of the American Statistical Association and the Institute of Mathematical Statistics. Dr. Chen served as a co-editor of Journal of Business and Economic Statistics and is currently serving as co-editor of Statistica Sinica. He is former Treasurer of the Institute of Mathematical Statistics and former program director in the Division of Mathematical Sciences at the National Science Foundation. Dr. Chen received both his PhD and MS in Statistics from Carnegie Mellon University and his BS in mathematics from Peking University in China.
Dr. Melanie Mitchell
Santa Fe Institute
Topic: Abstraction and Analogy in Natural and Artificial Intelligence
Abstract: In 1955, John McCarthy and colleagues proposed an AI summer research project with the following aim: “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” More than six decades later, all of these research topics remain open and actively investigated in the AI community. While AI has made dramatic progress over the last decade in areas such as vision, natural language processing, and robotics, current AI systems still almost entirely lack the ability to form humanlike concepts and abstractions.
In this talk, I will argue that the inability to form conceptual abstractions—and to make abstraction-driven analogies—is a primary source of brittleness in state-of-the-art AI systems, which often struggle in adapting what they have learned to situations outside their training regimes. I will reflect on the role played by analogy-making at all levels of intelligence, and on the prospects for developing AI systems with humanlike abilities for abstraction and analogy.
Bio: Melanie Mitchell is the Davis Professor at the Santa Fe Institute. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. Dr. Mitchell is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award and was named by Amazon.com as one of the ten best science books of 2009. Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).
Dr. Özlem Ergun
Topic: Optimizing Post-Disruption Response Operations to Improve Resilience of Critical Infrastructure Systems
Abstract: Critical infrastructure systems (CIS) underpin almost every aspect of the modern society by enabling the essential functions through overlaying service networks. After a disruption impacting the CIS, the functionality of the overlaying service networks degrades. Thus, after an extreme event, in order to minimize the negative impact to society, it is crucial to restore the disrupted CIS as soon as possible. In this talk, we focus on disruptions created by natural hazards on transportation CIS and develop methods to efficiently plan the post-disaster response operations.
In the aftermath of a natural disaster, the transportation network is disrupted due to the debris blocking the roads and obstructing the flow of relief aid and search-and-rescue teams between critical facilities and disaster sites. In the first few days following a disaster, in order to deliver aid to those in need, blocked roads must be cleared by pushing the debris to the sides. In this context, we define the road network recovery problem (RNRP) as finding a schedule to clear the roads with limited resources such that all the service demanding locations are served in the shortest possible time. First, we address the deterministic RNRP and propose a novel network science inspired measure to quantify the criticality of the components within a disrupted service network and develop a restoration heuristic. Next, we consider RNRP with stochastic demand and propose an approximate dynamic programming approach for identifying an effective policy under uncertainty.
Bio: Dr. Özlem Ergun is a professor and Associate Chair for Graduate Studies in Mechanical and Industrial Engineering at Northeastern University. Dr. Ergun’s research focuses on design and management of large-scale and decentralized networks. She has applied her work on network design, management, and resilience to problems arising in many critical systems including transportation, pharmaceuticals, and healthcare. She has worked with organizations that respond to emergencies and humanitarian crises around the world, including USAID, UNWFP, UNHCR, IFRC, OXFAM America, CARE USA, FEMA, USACE, CDC, AFCEMA, and MedShare International. Dr. Ergun served as a member of the National Academies Committee on Building Adaptable and Resilient Supply Chains after Hurricanes Harvey, Irma, and Maria and is currently serving in the Committee on Security of America's Medical Product Supply Chain. Within INFORMS, Dr. Ergun has been a leader in establishing a strong community of OR/MS professionals with an interest in public programs. She was the President of INFORMS Section on Public Programs, Service and Needs in 2013. She currently serves as the Area Editor at the Operations Research journal for Policy Modeling and the Public Sector Area and the Department Editor at MSOM journal for Environment, Health and Society Department. Dr. Ergun is also a founding co-chair of the Health and Humanitarian Logistics Conference, held annually since 2009. In addition, Dr. Ergun was the Vice President of Membership and Professional Recognition on the INFORMS Board of Directors, 2011-2015. Prior to joining Northeastern she was the Coca-Cola Associate Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology, where she also co-founded and co-directed the Health and Humanitarian Systems Research Center at the Supply Chain and Logistics Institute. She received a BS in Operations Research and Industrial Engineering from Cornell University in 1996 and a PhD in Operations Research from the Massachusetts Institute of Technology in 2001.
Dr. Galit Shmueli
National Tsing Hua University
Topic: "Improving" Prediction of Human Behavior Using Behavior Modification
Abstract: The fields of machine learning and statistics have invested great efforts into designing algorithms, models, and approaches that better predict future observations. Larger and richer data have also been shown to improve predictive power. This is especially true in the world of human behavioral big data, as is evident from recent advances in behavioral prediction technology. Large internet platforms that collect behavioral big data predict user behavior for their internal commercial purposes as well as for third parties, such as advertisers, insurers, security forces, and political consulting firms, who utilize the predictions for user-level personalization, targeting, and other decision-making. While machine learning algorithmic and data efforts are directed at improving predicted values, the internet platforms can minimize prediction error by "pushing" users' actions towards their predicted values using behavior modification techniques. The better the platform is able to make users conform to their predicted outcomes, the more it can boast both its predictive accuracy and its ability to induce behavior change. Hence, internet platforms have a strong incentive to "make the prediction true", that is, demonstrate small prediction error. This strategy is absent from the machine learning and statistics literature. Investigating the properties of this strategy requires incorporating causal terminology and notation into the correlation-based predictive environment. However, such an integration is currently lacking. To tackle this void, we integrate Pearl's causal do(.) operator to represent and integrate intentional behavior modification into the correlation-based predictive framework. We then derive the Expected Prediction Error given behavior modification, and identify the components impacting predictive power. Our formulation and derivation make transparent the impact and implications of such behavior modification to data scientists, internet platforms and their clients, and importantly, to the humans whose behavior is manipulated. Behavior modification can make users' behavior not only more predictable but also more homogeneous; yet this apparent predictability is not guaranteed to generalize when the predictions are used by platform clients outside of the platform environment. Outcomes pushed towards their predictions can also be at odds with the client's intention, and harmful to the manipulated users.
Bio: Galit Shmueli is Tsing Hua Distinguished Professor at the Institute of Service Science, and Institute Director at the College of Technology Management, National Tsing Hua University, Taiwan. Before joining NTHU, she was the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics & Information Systems at the Indian School of Business, and tenured Associate Professor at University of Maryland's Smith School of Business. She is the inaugural editor-in-chief of the INFORMS Journal on Data Science.
Dr. Andrea Lodi
Topic: On the Interplay Between Machine Learning and Discrete Optimization
Abstract: In this talk, I will present my personal experience on the interplay between Machine Learning (ML) and Discrete Optimization by discussing three fruitful interactions. Moving from methodological research to applications, the first use case concerns ML-based branching for Mixed-Integer Linear Programming solvers, the second addresses stochastic prediction for tactical planning (applied to railways operations) and the third involves, at the real applied level, demand prediction and resource allocation during Covid.
Bio: As Canada Excellence Research Chair in Data Science for Real-Time Decision-Making at Polytechnique Montréal, Dr. Andrea Lodi holds Canada’s main chair in operations research. Dr. Lodi received the PhD in System Engineering from the University of Bologna in 2000 and he has been Herman Goldstine Fellow at the IBM Mathematical Sciences Department, NY in 2005–2006. He is author of more than 80 publications in the top journals of the field of Mathematical Optimization. He serves as Associate Editor for several prestigious journals in the area.