Speakers CWI Lectures on Decision Making under Uncertainty

- Dick den Hertog (University of Amsterdam)
Analytics for a better world

In this talk I will describe two Analytics applications that contribute to one or more of the 17 Sustainable Development Goals (SDGs) of the United Nations. The first application is an optimization model to optimize the food supply chain for the World Food Programme. This application received the Franz Edelman award in 2021. The second application is an optimization model to optimize health care facility locations in Timor-Leste and stroke centers in Vietnam. This project is carried out in collaboration with the World Bank. We show that uncertainty plays an important role in such practical problems, and we argue that Robust Optimization is an effective methodology to obtain robust solutions. We finally discuss the new movement 'Analytics for a Better World', which was recently initiated by Dimitris Bertsimas (MIT) and the speaker.

Dick den Hertog is professor of Operations Research at University of Amsterdam. His research interests cover linear and nonlinear optimization. In recent years his main focus has been on robust optimization, and recently he started research on optimization with constraint learning. He is also active in applying the theory in real-life applications. In particular, he is interested in applications that contribute to a better society. For many years he has been involved in research for optimal flood protection, which was awarded by the INFORMS Franz Edelman Award in 2013. He is involved in research to optimize the food supply chain for the UN World Food Programme, which was awarded by the INFORMS Franz Edelman Award in 2021.  He is associate editor of "Operations Research" and "INFORMS Journal on Optimization", and editor of "Analytics for a Better World". He has been visiting professor at MIT since 2019.


- Pinar Keskinocak (Georgia Institute of Technology)
Modeling Infectious Diseases: Projecting Spread, Evaluating Interventions, and Resource Allocation

Our research team at Georgia Tech has worked on modeling a number of infectious diseases over the years, including pandemic flu, cholera, malaria, polio, Guinea worm, and Covid-19. We utilized different modeling approaches, such as SEIR, agent-based, integer programming, depending on the research questions or decision-support needs in practice. This presentation will provide an overview of some of our work in infectious disease modeling, and briefly discuss some dashboards we developed.

Pinar Keskinocak is the William W. George Professor and Chair in the School of Industrial and Systems Engineering at Georgia Tech and an Adjunct Professor in the Department of Environmental Health, Rollins School of Public Health at Emory University. She is the co-founder and Director of the Center for Health and Humanitarian Systems at Georgia Tech. Her main research areas include infectious disease modeling (including Covid-19, malaria, Guinea worm, pandemic flu, polio), evaluating intervention strategies, and resource distribution; process improvement for healthcare delivery; disaster preparedness, response, recovery; logistic and supply chain management. She has collaborated on projects with companies, governmental and non-governmental organizations, and healthcare providers, including American Red Cross, CARE, Carter Center, CDC, Children’s Healthcare of Atlanta, Emory University, and Task Force for Global Health.
She is an INFORMS Fellow and served as the 2020 president of INFORMS. She has also served on the editorial boards of several journals.


- Gergely Neu (Universitat Pompeu Fabra Barcelona)
Reinforcement Learning via Linear Programming

Reinforcement Learning (RL) is one of the leading frameworks for sequential decision-making under uncertainty, underlying many of the recent breakthroughs of artificial intelligence research such as
achieving human-level performance in the game of Go or Starcraft. Most modern RL methods are based on solving a high-dimensional system of nonlinear fixed-point equations called the Bellman equations, first formulated in the 1950s in the context of optimal control by Richard Bellman. Despite having inspired decades of productive work in
reinforcement learning, methods based on approximate solutions of the Bellman equations remain challenging to formally analyze due to the conceptual difficulty of working with nonlinear fixed-point equations.
In this talk, I highlight an equivalent alternative formulation of sequential decision making based on the conceptually simpler framework of Linear Programming (LP). While this formulation has been proposed as early as the 1960s by Alan Manne, it has received considerably less attention in the RL literature until recently, when it has finally started to gain popularity due to its simplicity and elegance. This talk aims to present some of the numerous recent advances rooted in the LP framework, highlighting how the key limitations associated with past LP-based approaches have been eliminated and how the LP perspective enabled a deeper formal understanding of empirically successful RL methods.

Gergely Neu is a research assistant professor at the Pompeu Fabra University, Barcelona, Spain. He has previously worked with the SequeL team of INRIA Lille, France and the RLAI group at the University of Alberta, Edmonton, Canada. He obtained his PhD degree in 2013 from the Budapest University of Technology and Economics, where his advisors were András György, Csaba Szepesvári and László Györfi. His main research interests are in machine learning theory, including reinforcement learning and online learning with limited feedback and/or very large action sets. Dr. Neu was the recipient of a Google Faculty Research award in 2018, the Bosch Young AI Researcher Award in 2019, and an ERC Starting Grant in 2020.

- Leonard Smith (Virginia Tech)
Opacity and Insight of Science in Support of Decision Making


Professor Leonard A Smith received his PhD in Physics at Columbia University. From 1992-2020 he was a Senior Research Fellow (mathematics) at Pembroke College, Oxford (UK), he was Director of the LSE’s Centre for the Analysis of Time Series for the last two decades. In 2020 he joined Virginia Tech as a Professor in SPACE@VT. He is active in the formation, interpretation, evaluation and attempted application of probability forecasts in all areas, with a general interest in improving simulation modelling and scientific support for decision making, with aspirations of relevant uncertainty quantification and the escape from model land.

Professor Smith was awarded a Selby Fellowship from the Australian Academy of Sciences, his bestselling book A Very Short Introduction to Chaos is available in over eight languages; he was active in the original experimental design(s) of climateprediction.net. He holds the Royal Meteorological Society’s Fitzroy Prize and is an AGU Charney Lecturer and a Visiting Scientist at the European Centre for Medium-range Weather Forecasts. He tweets as @lynyrdsmyth.