Brian Denton

University of Michigan

Brian Denton is the Stephen M. Pollock Professor of Industrial and Operations Engineering and the Chair of the Department of Industrial and Operations Engineering at the University of Michigan. His research interests are in data analytics and data-driven optimization under uncertainty with applications to medicine, public health, and healthcare delivery. He is a Professor in the Department of Urology (by courtesy) at Michigan Medicine and a member of the Institute for Healthcare Policy and Innovation and the Cancer Center at the University of Michigan. His research has been funded by the National Science Foundation, the Agency for Healthcare Research and Quality, the National Institutes of Health, the U.S. Department of Veterans Affairs, and industry research contracts. He is past President of the Institute for Operations Research and the Management Sciences (INFORMS), and he is an elected Fellow of INFORMS.

Healthcare Analytics: Leveraging
Predictive and Prescriptive Methods
to Prevent and Treat Diseases

Brian Denton, Department of Industrial and
Operations Engineering, University of
Michigan, Ann Arbor, MI

Many of the world’s healthcare systems are amassing large amounts of patient observational data, including electronic health records, labs, insurance claims, and other sources. Moreover, the information technology infrastructure needed to connect the data has developed rapidly. However, despite the abundance of data, there are many pitfalls associated with using observational data that can lead to poor model performance if not carefully addressed. This presentation will describe opportunities for using a fusion of analytics and operations research approaches to improve medical decisions, drawing on case studies of chronic diseases such as cancer, diabetes, and heart disease. The case studies will be used to illustrate the use of machine learning, Markov decision processes, and stochastic programming to address decision-making for chronic disease prevention and treatment. Finally, the presentation will cover future opportunities for research and practice related to medicine..

Rene de Koster

Erasmus University Rotterdam

René (M.) B.M. de Koster is a professor of Logistics and Operations Management at Rotterdam School of Management, Erasmus University, and chairs the department Technology and Operations Management. He holds a PhD from Eindhoven University of Technology. He holds or has held guest professorships in various universities and is the 2018 honorary Francqui Professor at Hasselt University. His research interests are warehousing, material handling, terminal operations, and behavioral operations. He is the founder of the Material Handling Forum and is author / editor of 8 books and over 250 papers in academic journals and books. He was recently mentioned as “the most influential researcher” in material handling. He is associate editor of Transportation Science, Service Science, and Operations Research.

Warehouse Robotics. Insights from Research

René de Koster, Erasmus University Rotterdam

Many of the world’s healthcare systems are amassing large amounts of patient observational data, including electronic health records, labs, insurance claims, and other sources. Moreover, the information technology infrastructure needed to connect the data has developed rapidly. However, despite the abundance of data, there are many pitfalls associated with using observational data that can lead to poor model performance if not carefully addressed. This presentation will describe opportunities for using a fusion of analytics and operations research approaches to improve medical decisions, drawing on case studies of chronic diseases such as cancer, diabetes, and heart disease. The case studies will be used to illustrate the use of machine learning, Markov decision processes, and stochastic programming to address decision-making for chronic disease prevention and treatment. Finally, the presentation will cover future opportunities for research and practice related to medicine..

Andrea Lodi

Cornell Tech and the Technion

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 the Operations Research and Information Engineering field at Cornell University. He received his PhD in System Engineering from the University of Bologna in 2000 and he was a Herman Goldstine Fellow at the IBM Mathematical Sciences Department, NY in 2005-2006. He was a full professor of Operations Research at DEI, the University of Bologna between 2007 and 2015. Between 2015 and 2022, he has been the Canada Excellence Research Chair in “Data Science for Real-time Decision Making” at Polytechnique Montréal. His main research interests are in Mixed-Integer Linear and Nonlinear Programming and Data Science, having a significant publication record in the most prestigious journals and conferences of those fields. Andrea Lodi's work has received several recognitions including the IBM and Google faculty awards. Andrea is the recipient of the INFORMS Optimization Society 2021 Farkas Prize. He serves in leading editorial positions, most notably, he is a co-Editor for Mathematical Programming and an Area Editor for INFORMS Journal on Computing. In Europe, Andrea has been the network coordinator and principal investigator of two large EU projects/networks. In Canada, he has been the co-principal investigator of the project “Data Serving Canadians: Deep Learning and Optimization for the Knowledge Revolution,” and the scientific co-director of IVADO, the Montréal Institute for Data Valorization (2017-2022). Finally, Andrea has been part-time member of the IBM CPLEX research and development team, contributing to develop one of the world-wide leading Mixed-Integer Linear Programming in the period 2006-2021.

Machine Learning for Combinatorial Optimization

The last decade has witnessed the impressive development of machine learning (ML) techniques. These techniques have been successfully applied to traditional statistical learning tasks as image recognition and led to breakthroughs like the famous AlphaGo system. Motivated by those successes, many scientific disciplines have started to investigate the potential of the use of large amount of data crunched by ML techniques in their context. Combinatorial optimization (CO) has been no exception to this trend and the ML use in CO has been analyzed from many different angles with various levels of success. In this talk, we will review the state of the art of such scientific path, interpreting the level of maturity reached by the integration of ML techniques in CO and discussing the challenges. We will finish by presenting one particular area in which we consider this integration having a remarkable potential, i.e., repeatedly solving CO problems with little data variations.

Anna Nagurney

University of Massachusetts

Anna Nagurney is the Eugene M. Isenberg Chair in Integrative Studies at the Isenberg School of Management at the University of Massachusetts Amherst and the Director of the Virtual Center for Supernetworks, which she founded in 2001. She holds ScB, AB, ScM and PhD degrees from Brown University in Providence, RI. She is the author/editor of 15 books, more than 220 refereed journal articles, and over 50 book chapters. Professor Nagurney has been a Fulbrighter twice (in Austria and Italy), was a Visiting Professor at the School of Business, Economics and Law at the University of Gothenburg in Sweden, and was a Distinguished Guest Visiting Professor at the Royal Institute of Technology (KTH) in Stockholm. She was a Visiting Fellow at All Souls College at Oxford University during the 2016 Trinity Term and a Summer Fellow at the Radcliffe Institute for Advanced Study at Harvard in 2017 and 2018. Anna has held visiting appointments at MIT and at Brown University and was a Science Fellow at the Radcliffe Institute for Advanced Study at Harvard University in 2005-2006. She has been recognized for her research on networks with the Kempe Prize from the University of Umea, the Faculty Award for Women from the US National Science Foundation, the University Medal from the University of Catania in Italy, the Constantin Caratheodory Prize, and the Harold Larnder Prize, and was elected a Fellow of the RSAI (Regional Science Association International) as well as INFORMS (Institute for Operations Research and the Management Sciences) and the Network Science Society, among other awards. She is a 2022 IFORS Distinguished Lecturer. Anna has also been recognized with several awards for her mentorship of students and her female leadership with the WORMS Award, for example. Her research has garnered support from the AT&T Foundation, the Rockefeller Foundation through its Bellagio Center programs, the Institute for International Education, and the National Science Foundation. She has given plenary/keynote talks and tutorials on 5 continents. She is an active member of professional societies, including INFORMS, POMS, and RSAI.

Anna's research focuses on network systems from transportation and logistical ones, including supply chains, to financial, economic, social networks and their integration, along with the Internet. She studies and models complex behaviors on networks with a goal towards providing frameworks and tools for understanding their structure, performance, and resilience and has contributed also to the understanding of the Braess paradox in transportation networks and the Internet. She has also been researching sustainability and quality issues with applications ranging from pharmaceutical and blood supply chains to perishable food products and fast fashion to humanitarian logistics. She has advanced methodological tools used in game theory, network theory, equilibrium analysis, and dynamical systems. She was a Co-PI on a recent multi-university NSF grant with UMass Amherst as the lead: Network Innovation Through Choice, which was part of the Future Internet Architecture (FIA) program and an NSF EAGER grant.

Agricultural Supply Chain Networks:
From Trade to Resilience and How OR
Can Help

Anna Nagurney, University of Massachusetts

Agricultural supply chain networks are essential to food security and are a major component of gobal trade. In this talk, I will describe how OR is being utilized to model and solve agricultural supply chain problems subject to a spectrum of trade instruments; how to capture fresh produce quality in multitiered supply chains, and how to measure resilience in order to mitigate against disruptions under conditions such as the pandemic, climate change, and wars. I will also discuss how we can better promote our research contributions in order to influence policy and effect positive change.

Tava Olsen

University of Melbourne

Dr. Tava Olsen is Professor of Operations and Supply Chain Management, Director of the Centre for Supply Chain Management, and Head of two departments at the University of Auckland Business School. In November 2022 she will be joining Melbourne Business School as their new Deputy Dean, Academic. Prior to joining Auckland, she was Professor of Operations and Manufacturing Management in the Olin Business School at Washington University in St. Louis, which she joined after serving as an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan, Ann Arbor. Tava received her B.Sc. (honours) in Mathematics from the University of Auckland and her Masters in Statistics in 1992 and Ph.D. in Operations Research in 1994, both from Stanford University. Tava’s research interests include supply-chain management; pricing and inventory control; and stochastic modelling of manufacturing, service, agricultural, and healthcare systems.

Tava is currently the Area Editor for Operations and Supply Chain at Operations Research and a senior editor of Production and Operations Management (POM). She has previously served as an Associate Editor for Management Science and Manufacturing and Services Operations Management (M&SOM), and was the Topical Editor for Supply-Chain Management for the Wiley Encyclopaedia of Operations Research and Management Science. She is a past president of the Manufacturing and Service Operations (MSOM) society and has thrice been awarded the Auckland Business School’s research excellence award. Among other journals, her publications have appeared in Management Science, Operations Research, M&SOM, and POM. Tava has been awarded over $3 million dollars of external funding over her career, including being a recipient of the prestigious NSF CAREER award and RSNZ Marsden grant.

Agriculture 4.0 and its Opportunities for OR

Tava Olsen, University of Melbourne

Agriculture is changing in many ways. This talk gives an overview of these changes, with a particular focus on agricultural supply chains. Like many supply chains around the world, agricultural supply chains are subject to digital disruption in a variety of interesting ways. I will outline what these disruptions mean for agriculture today and make some projections for the future. New Zealand case studies will be presented. Further, I will also discuss new agricultural technologies and precision agriculture and what they mean for research in this important area. Ideas for future research will be discussed throughout the talk.

Dolores Romero

Copenhagen Business School

Dolores Romero Morales is a Professor in Operations Research at Copenhagen Business School. Her areas of expertise include Data Science, Supply Chain Optimization and Revenue Management. In Data Science she investigates interpretability and visualization matters. In Supply Chain Optimization she works on environmental issues and robustness. In Revenue Management she works on large-scale network models. Her work has appeared in a variety of leading scholarly journals, including European Journal of Operational Research, Management Science, Mathematical Programming and Operations Research, and has received various distinctions. Currently, she is Editor-in-Chief to TOP, the Operations Research journal of the Spanish Society of Statistics and Operations Research, and an Associate Editor of Journal of the Operational Research Society, Omega and the INFORMS Journal on Data Science.

She has worked with and advised various companies on these topics, including IBM, SAS, KLM and Radisson Edwardian Hotels, as a result of which these companies managed to improve some of their practices. SAS named her an Honorary SAS Fellow and member of the SAS Academic Advisory Board. She currently leads the EU H2020-MSCA-RISE NeEDS project, which has a total of 15 participants and a budget of more than €1.000.000 for intersectoral and international mobility, with the aim to improve the state of the art in Data Driven Decision Making..

Dolores joined Copenhagen Business School in 2014. Prior to coming to Copenhagen Business School, she was a Full Professor at University of Oxford (2003-2014) and an Assistant Professor at Maastricht University (2000-2003). She has a BSc and an MSc in Mathematics from Universidad de Sevilla and a PhD in Operations Research from Erasmus University Rotterdam.

OR and the fight against biases in Machine Learning

Dolores Romero Morales
Copenhagen Business School
Denmark

Machine Learning and Artificial Intelligence aid data driven decision making and the benefits are ubiquitous. However, the dangers of making decisions based on biased data are well reported in the academic literature as well as in mass media. The so- called black boxes, while showing state-of-the-art accuracies, may hide unfair outcomes for risk groups in high stakes decision making such as medical diagnosis, allocation of social benefits, or approvals in parole hearings. In this presentation, we will navigate through some novel mathematical optimization formulations to enhance the explainability and fairness of Machine Learning models, and thus allowing the user to understand and correct biases in these models.