Cynthia Rudin is a professor of computer science, electrical and computer engineering, statistical science, mathematics, and biostatistics & bioinformatics at Duke University. She directs the Interpretable Machine Learning Lab, whose goal is to design predictive models with reasoning processes that are understandable to humans. Her lab applies machine learning in many areas, such as healthcare, criminal justice, and energy reliability. She holds an undergraduate degree from the University at Buffalo, and a Ph.D. from Princeton University. She is the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (the “Nobel Prize of AI”). She is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the Association for the Advancement of Artificial Intelligence. Her work has been featured in many news outlets including the NY Times, Washington Post, Wall Street Journal, and Boston Globe.
The Life and Death of Your Jeans
Maxine Bédat, founder and Director of think and do tank, New Standard Institute and author of Unraveled: The Life and Death of a Garment, an FT Business Book of the Year, will share the hidden world behind our clothing, highlighting key problems within our long supply chains, research that needs to be undertaken, and systems solutions that management science can champion.
Maxine Bédat is the founder and director of The New Standard Institute. Prior to NSI, Maxine co-founded and was the CEO of the fashion company Zady. For its work in sustainability, Zady was named one of the world’s “Most Innovative Companies” in retail by Fast Company. Bédat has been recognized by Oprah in her Super Soul 100, for leaders elevating humanity, serves as an ambassador for Rainforest Alliance and is on the Council of NationSwell. She has spoken at some of the world’s leading conferences, including the WWD Apparel/Retail CEO Summit, and has been regularly featured as an expert by Bloomberg, Forbes, Business of Fashion, CNN and the Huffington Post. Bédat began her career in international law working at the Rwandan Criminal Tribunal and received a Juris Doctor from Columbia Law School.
Professor Way Kuo, President and University Distinguished Professor of City University of Hong Kong, is a Member of US National Academy of Engineering, Academia Sinica in Taiwan, a Foreign Member of Chinese Academy of Engineering, and an International Fellow of the Canadian Academy of Engineering.
He is renowned for his work in design for reliability, including electronics and energy systems. He was previously on the Senior Management team of Oak Ridge National Laboratory and served as Dean of Engineering at the University of Tennessee.
Professor Kuo was the first invited foreign expert to conduct post-accident assessment on the Fukushima Power Plant after the 2011 earthquakes. He has co-authored seven academic books. In addition, his popular science book Critical Reflections on Nuclear and Renewable Energy, Wiley-Scrivener, 2013, was translated to Japanese, French and Russian, published in Tokyo, Paris and Moscow, respectively.
Global Health Security and Healthcare Supply Chains: Perspectives and Opportunities for Operations Research
As has become abundantly obvious in the last two years, infectious diseases have the potential to endanger lives, disrupt economies, travel, trade, and have a significant impact on our mental health. The need for robust prevention, preparedness, and response mechanisms is also widely recognized and accepted. However, achieving global health security also requires coordinated actions across countries, regions, and other forms of administrative geographical units. Operations research plays a key role in many parts of global health security such as modelling and simulation of disease outbreaks, analytical models of different policy responses, and design and operation of supply chains for medical counter measures. While all aspects of global health security depend on global coordination, the supply chains for global health technologies such as vaccines, diagnostics, and therapeutics require special kinds of coordination across multiple manufacturers, purchasers, payers, and delivery partners. This talk will focus on the evolution of the field of global health supply chains, which had it origins in global health security, but over time focused more on health systems building. It will highlight areas where health care supply chain research has contributed at the forefronts of policy making regarding global pandemic response and preparedness. It will also present opportunities for OR/OM researchers to contribute more effectively to achieve direct policy impact in this area.
Prashant Yadav is a globally recognized scholar in the area of healthcare supply chains. He is an Affiliate Professor at INSEAD, Senior Fellow at the Center for Global Development, and Lecturer at Harvard Medical School. He is the author of many peer-reviewed scientific publications and his work has also been featured in prominent print and broadcast media such as the BBC, New York Times, CNN, Financial Times, WSJ and NPR. In addition to his roles in academia and think-tank, Prashant serves on the boards of many health and development focused companies. In his previous roles Prashant has worked as Strategy Leader-Supply Chain at the Bill & Melinda Gates Foundation; Vice President of Healthcare at the William Davidson Institute and Faculty at the Ross School of Business at the University of Michigan; Professor of Supply Chain Management at the MIT-Zaragoza International Logistics Program and Research Affiliate at the MIT Center for Transportation and Logistics. Yadav has been asked for expert testimony on medicine supply chains in the US Congress and Parliaments/Legislative bodies of many countries He works closely with and advises many country governments, and philanthropic organizations on healthcare supply chain strategy. Yadav trained as a Chemical Engineer and obtained his PhD in Management Science & Operations Research.
Modeling Systemic Risk in Supply-Demand Networks
David D. Yao is the Piyasombatkul Family Professor of Industrial Engineering and Operations Research at Columbia University, where he is a co-chair of the Financial and Business Analytics Center at Columbia Data Science Institute, and more recently, co-director of the Columbia Fintech, AI and Business Analytics (FABULYS) Initiative. His research and teaching interests are in applied probability and stochastic networks, focusing on resource control and risk analysis. He has been a Guggenheim Fellow, IEEE Fellow, INFORMS Fellow, and a Member of the National Academy of Engineering.
From the Battlefield to the Gig Economy: How Hybrid Optimization Can Guide Decision Making in Highly Dynamic and Unpredictable Settings
Karla Hoffman is a Professor in the Systems Engineering and Operations Research Department at George Mason University (GMU) where she served as Chair for five years. She received her D.Sc. from George Washington University in operations research. Prior to her position at GMU, she worked as a mathematician in the Center for Applied Mathematics of the National Institute of Standards and Technology (NIST) where, in1984, she was awarded the Applied Research Award. Her other awards include George Mason University’s Distinguished Faculty Award, the INFORMS Fellows Award, the INFORMS George E. Kimball Medal and the INFORMS Edelman Prize. She served as President of INFORMS in 1999 and has also served on the Executive Committees of ORSA, IFORS, and the Mathematical Programming Society. Dr. Hoffman’s primary area of research is auction design and testing, and combinatorial optimization. Her research focuses on the development of new algorithms for solving complex problems arising in industry and government. She serves as a consultant to the FCC on spectrum auctions and has previously consulted to a variety of government agencies. Her industrial consulting has been in dynamic and real-time routing and scheduling, and in capital budgeting.
Online Optimization and Learning for Dynamic Matching Markets
Dr. Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science and a member of the Laboratory for Information and Decision Systems at MIT. He holds a joint appointment in the Operation Research and Statistics Group at MIT Sloan. He is also co-Director of the MIT Operations Research Center and the Faculty Director of the MIT-France program. He was Head of Civil and Environmental Engineering at MIT from 2002 to 2009, where he currently holds a joint appointment. From 1991 to 2002, he was a professor at the University of Texas in Austin, the last five years as the Chair of the Department of Management Science and Information Systems within the McCombs School of Business School. He co-founded and was Director of UT Austin’s Center for Computational Finance. Before his appointment in Austin, he was a faculty and a member of the Center for Applied Mathematics at the Ecole Nationale des Ponts et Chaussée in Paris. He received a Diplôme d’Ingénieur from France (1981), and then came to MIT where he received an SM in transportation (1982) followed by a Ph.D. in operations research (1985).
Dr. Jaillet’s research interests include online optimization and learning, machine learning, and decision making under uncertainty. His research has been supported by U.S. federal sources, such as NSF, ONR, AFOSR; the private sector, such as IBM, Microsoft, Google; and internationally by Singapore NRF. Professor Jaillet’s teaching covers subjects such as machine learning, algorithms, optimization, network science and models, and probability. Dr. Jaillet’s consulting activities primarily focus on the development of optimization-based analytic solutions in various industries, including defense, financial, electronic marketplace, and information technology.
Dr. Jaillet was a fulbright scholar in 1990 and the recipient of many research and teaching awards. He is a Fellow of INFORMS, a member of the Mathematical Optimization Society (MOS), and a member of the Society for Industrial and Applied Mathematics (SIAM). He is currently an Associate Editor for INFORMS Journal on Optimization, Networks, and Naval Research Logistics, and has been an Associate Editor for Operations Research from 1994 until 2005 and for Transportation Science from 2002 until 2017.
Dr. Jane L. Snowdon is Deputy Chief Science Officer, Science Operations, responsible for assisting the Chief Science Officer in developing the scientific strategy, integrating scientific expertise and evaluation into the product design and development process, leading and managing team personnel in executing the scientific strategy, engaging clients and academic partners to develop and execute scientific collaborations to support IBM and Watson Health business goals, and representing IBM Watson Health as a scientific leader in scientific and academic forums. Prior to this role, Jane was responsible for managing global efforts to define strategy, design, and implement peer-reviewed research evaluation studies for IBM’s life sciences, genomics, and government health and human services solutions.
Dr. Snowdon is an IBM Industry Academy Member, Fellow of the American Medical Informatics Association, and received the 2019 American Public Health Association Achievement in Industry Award. Dr. Snowdon received the 1Q2021 Watson Health General Manager’s Award for “Tackling Challenges through Collaboration, Diversity & Inclusion” for analysis of variants in SARS-CoV-2, 2020 Health Tech Hero Award from the Women of IBM Watson Health and is a 2020 YWCA Greenwich Women Who Inspire Award honoree.
Previously, Jane was Chief Innovation Officer, IBM Federal and served as Director of the IBM Federal Cloud Innovation Center in Washington, DC. Jane conducted research for two decades in modeling, simulating, and optimizing manufacturing systems, airline crew scheduling and flight routing systems, energy efficient buildings, and high performance computing. Jane was a senior manager in the Department of Strategy and Worldwide Technical Operations at the IBM T. J. Watson Research Center where she jointly led the direction of IBM’s overall Research strategy across twelve global labs and the Global Technology Outlook which become IBM’s became IBM’s cloud, analytics, mobile, social and security (CAMSS) strategy.
Jane has a proven record of successfully consulting with CxOs to drive enterprise transformation and building ecosystems with IBM’s clients, universities, business partners, and government to enable multi-disciplinary collaboration in the development and deployment of new methods, processes, tools, and services.
Jane received her Ph.D. from the Georgia Institute of Technology, M.S. degree from the University of Michigan, and B.S. degree with honors from the Pennsylvania State University, all in industrial engineering. Jane has published more than 100 articles and holds 1 patent. She is a senior member of IIE and IEEE, and a member of AMIA, APHA, CASE, HIMSS, INFORMS, ISPOR, NYAS, Sigma Xi, and SWE.
Parallel Computing and OR/MS
Jonathan Eckstein is a professor in the department of Management Science and Information Systems at Rutgers University. His principle research interests are in numerical optimization algorithms, both continuous and discrete, and especially their implementation on parallel computing platforms. Areas of particular focus include augmented Lagrangian/proximal methods, branch-and-bound algorithms, and stochastic programming. He has also worked on risk-averse optimization modeling and on applying O.R. techniques to managing information systems. He completed his Ph.D. in operations research at M.I.T. in 1989, and then taught at Harvard Business School for two years. He then spent four years in the Mathematical Sciences Research Group of Thinking Machines, Inc. before joining Rutgers. At Rutgers, he led an effort establishing a new undergraduate major in Business Analytics and Information Technology (BAIT). In 2014, he was elected a fellow of INFORMS.In 2019, he became editor-in-chief of the journal Mathematical Programming Computation.
Peter Frazier received a B.S. in physics and engineering/applied science from the California Institute of Technology in 2000, after which he spent several years in industry as a software engineer, working for two different start-up companies and for the Teradata division of NCR. In 2005, he entered graduate school in the Department of Operations Research & Financial Engineering at Princeton University, and received an M.A. in 2007 and a Ph.D. in 2009. He joined the faculty at Cornell in 2009 as an Assistant Professor in the School of Operations Research & Information Engineering, where he is now an Associate Professor. His research is in sequential decision-making under uncertainty, optimal methods for collecting information, and machine learning, focusing on applications in simulation, e-commerce, medicine, and biology. He is the recipient of a CAREER Award from the National Science Foundation and a Young Investigator Award from the Air Force Office of Scientific Research. He is currently on leave at Uber, where he is a Staff Data Scientist and Data Science Manager. At Uber, he worked on UberPOOL from 2015-17, and on broader pricing efforts from 2016-17. He now leads a data science team focused on pricing.
Shane G. Henderson
Shane G. Henderson is the Charles W. Lake, Jr. Professor in Productivity in the School of Operations Research and Information Engineering at Cornell University. His overall professional goal is to contribute to both research and learning in the theory and application of stochastic simulation and applied probability, with emphasis on the interface between these areas and optimization. He is greatly interested in and motivated by applications with strong societal relevance, including bike sharing, medical scheduling, and ambulance planning.
David B. Shmoys
David Shmoys is the Laibe/Acheson Professor and Director of the Center for Data Science for Enterprise & Society at Cornell University. He obtained his Ph.D. in computer science from the University of California at Berkeley in 1984, and held postdoctoral positions at MSRI in Berkeley and Harvard University, and a faculty position at MIT before joining the faculty at Cornell University. He was Chair of the Cornell Provost’s “Radical Collaborations” Task Force on Data Science and was co-Chair of the Academic Planning Committee for Cornell Tech. His research has focused on the design and analysis of efficient algorithms for discrete optimization problems, with applications including scheduling, inventory theory, computational biology, computational sustainability, and most recently, data-driven decision-making in the sharing economy. His work has highlighted the central role that linear programming plays in the design of approximation algorithms for NP-hard problems. His book (co-authored with David Williamson), The Design of Approximation Algorithms, was awarded the 2013 INFORMS Lanchester Prize and his work on bike-sharing (joint with Daniel Freund, Shane Henderson, and Eoin O’Mahony) was awarded the 2018 INFORMS Wagner Prize. David is a Fellow of the ACM, INFORMS, and SIAM, and was an NSF Presidential Young Investigator.