Virginia Mason Health System
Plenary: The Parametric Self-Dual Simplex Method — A Modern Perspective
Omega Rho Distinguished Lecture
The parametric self-dual simplex method (PSD) dates back to George Dantzig’s classic book published in 1963. Other variants of the simplex method have dominated the world of optimization over the years but the PSD variant has lots of attractive features both from an educational perspective and in the world of applications. In the talk I will define the method, discuss how it helps one understand more deeply duality theory and algorithm complexity and I will discuss some application areas including portfolio selection problems and LAD-Lasso problems that arise in modern machine learning problems.
Keynote: Data Science in Online Marketplaces: An OR Perspective
Online marketplaces have seen immense growth in the past decade, opening the door to novel problems in prediction, control and optimization under the umbrella of Data Science. In this talk, we will focus our attention on pricing and matching problems in online marketplaces, drawing on examples at Uber Freight — an online marketplace for freight transportation. We will highlight the connection of these problems to operations research and economics.
Keynote: Analytics at the University of Cincinnati: A History of Innovation and Practical Impact
UPS George D. Smith Prize
The Operations, Business Analytics & Information Systems (OBAIS) Department in the Carl H. Lindner College of Business at the University of Cincinnati has a proud history of impact on the practice of analytics. For more than 50 years, the OBAIS Department has partnered with industry, produced thought-leading research, and influenced the teaching of analytics. In this presentation, we will provide an overview of the historical and current innovative activities undertaken by the OBAIS Department that culminated in its receiving the 2019 INFORMS UPS George D. Smith Prize.
University of Cincinnati
University of Cincinnati
London Business School
Keynote: The New Age of Healthcare Delivery
The U.S. healthcare system is undergoing one of the most rapid periods of change ever. The confluence of new payment systems, technological innovations, greater data availability and higher consumer expectations is resulting in a new focus on increasing access and convenience, particularly for high-risk, high-needs patients, while controlling costs through greater reliance on evidence-based care and increasing productivity. While hospitals and physicians have dominated the healthcare delivery system in the past, new models of healthcare delivery employ a variety of locations and types of providers, raising questions about the best means to deliver cost-effective care. In this talk I’ll describe these operational and organizational innovations and the critical research questions that must be addressed to create higher quality, lower cost healthcare systems in the future.
Columbia Business School
Keynote: Zero Carbon Analytics
IFORS Distinguished Lecturer
Climate change from global warming is the most critical problem currently facing mankind. The deleterious effects of global warming have already been felt in 2019 in terms of storms, heatwaves and forest fires. Climate-based natural disasters in the longer term threaten to be much worse. Policies to deal with this problem must combine efforts to reduce global warming by eliminating greenhouse gas emissions with actions that adapt to worsening climate outcomes. Determining the best policy settings is a highly complex decision problem, involving complex interacting systems and markets, many stakeholders, many decision criteria, and huge uncertainty. In other words, it is a major opportunity for operations researchers.
This talk will illustrate some of the analytics challenges in planning a net-zero carbon economy with a focus on electricity systems. Models range from short-term operations (like dealing with intermittent renewable energy) to long-term investment planning to ensure security of energy supply in an uncertain future. Planning models are complemented with equilibrium models that represent the effects of risk-averse agent behaviour. The models will be illustrated with a New Zealand case study based on our recent work for the New Zealand Interim Climate Change Committee in planning a 100% renewable electricity system.
University of Auckland
Stanford Business School
Keynote: Predictive Data Science for Physical Systems: From Physics-based Models to Scientific Machine Learning
For high-consequence decisions in science, engineering and medicine–big decisions–we need more than just big data, we need big models too. These problems are characterized by complex multi-scale multi-physics dynamics, where small changes in parameters can lead to drastic changes in system behavior. They are also typically characterized by high-dimensional uncertain parameters that cannot be observed directly, and by a need to issue predictions that go well beyond the specific conditions where data may be available. For these reasons, a purely data-focused perspective will fall short. Achieving predictive data science for these complex physical systems requires a synergistic combination of data and physics-based models. Learning from data through the lens of models is a way to bring structure to an otherwise intractable problem: it is a way to respect physical constraints, to embed domain knowledge, to bring interpretability to results, and to endow the resulting predictions with quantified uncertainties. As one example, this talk shows how formulations from projection-based model reduction can be combined with machine learning methods to achieve this. Case studies in aerospace engineering applications demonstrate the importance of embedding physical constraints within learned models, and also highlight the important point that the amount of model training data available in an engineering setting is often much less than it is in other machine learning applications, making it essential to incorporate knowledge from physical models.
University of Texas Austin
Keynote: Edelman Reprise – Protecting Community Waterways: Applying Analytics and Optimization for Wastewater Management
The Louisville and Jefferson County Metropolitan Sewer District (Louisville MSD) in Kentucky, USA, uses operations research, advanced analytics and innovative engineering concepts to protect local community waterways by optimizing the collection, transport and treatment of wastewater. In partnership with Tetra Tech, Louisville MSD pioneered the application of real time control using Csoft®, which relies on sewer monitoring data, weather forecasting, and data analytics for system-wide optimization. In operation since 2006, the solution has reduced more than 2 billion gallons of sewer overflows per typical year and saved the community over $200 million of capital cost.
Louisville Metropolitan Sewer District
Louisville Metropolitan Sewer District
Diana Qing Tao
Keynote: Wagner Prize Winner
The Daniel H. Wagner Prize is awarded for a paper and presentation that describe a real-world, successful application of operations research or advanced analytics. The prize criteria emphasize innovative, elegant mathematical modeling and clear exposition.
Keynote: Marketplace Modeling: Managing Scale and Accuracy
Marketplace modeling is a research area that has had widespread real-world impact within the OR/MS community. Focusing on scale and accuracy, we will discuss some techniques we have been using to enable better decision-making for concrete applications, such as matching jobs and applicants, blood donation recommendations, friend suggestions, rendering feeds, and online advertising. These techniques include using competitive equilibria to compute allocations, finding compact representations of marketplaces, and using Bayesian optimization coupled with simulation to improve model accuracy.
Bill and Melinda Gates Foundation