Plenaries & Keynotes

Plenaries

Sunday, November 8

12:15-1:15pm

Operations Research and Public Policy: Making a Difference

Our nation and the world have undergone an enormous number of societal changes in 2020 that will impact our lives well into the future. This has created an even greater need for data-driven analysis and modeling to address a growing list of new challenges. Operations research has a long history of impacting public policy, in such diverse areas as the military, homeland security, government, and public health. Several of these issues have been central themes throughout my academic career, including risk-based aviation security, election forecasting, and computational redistricting. This talk discusses a number of these research problems, focusing on how operations research has made a difference, and how the operations research community can provide valuable service to our nation by using our expertise to address emerging opportunities in this new world.

Sheldon Jacobson headshot
Sheldon Jacobson
Omega Rho Distinguished Lecturer

Founder Professor of Computer Science
University of Illinois at Urbana-Champaign

Monday, November 9

11am-12pm
Panel Discussion

Forecasting Models for the COVID-19 Pandemic

Since the early days of the outbreak of the COVID-19 virus, advanced analytics have been applied to forecast the outbreak of infections, deaths, and hospitalizations. This distinguished panel includes four leading researchers in the areas of modeling the disease, and they will discuss different modeling approaches, how these approaches quickly evolved, the advantages and disadvantages of these approaches, and a retrospective of how the models and their results have been communicated outside the profession. The panel will also touch on opportunities for future modeling to support decision making.

Moderator

Anne Robinson headshot
Anne Robinson

Chief Strategy Officer
Kinaxis

Panelists

Julie Swann headshot

Julie Swann

North Carolina State University
Nicoleta Serban headshot

Nicoleta Serban

Georgia Tech
Retsef Levi headshot

Retsef Levi

MIT
Nicholas Reich headshot

Nicholas G. Reich

University of Massachusetts, Amherst

Tuesday, November 10

11am-12pm

The Future of Federal Statistics and the Role of the Chief Statistician

Technology and the availability of data has significantly changed inputs to federal statistical data. Users want reliable, timely, and more granular data but statistical agencies face ongoing budget limitations as they attempt to modernize. Maintaining data quality while achieving efficiencies in data collection, such as those built into the 2020 Census field operations, presents many challenges The Chief Statistician of the US is charged with coordinating the federal statistical system, setting quality standards, and leading change. Explore what change might look like with the former Chief Statistician, who presents a vision of how far we have come recently and what lies ahead.

Nancy Potok headshot
Nancy Potok

former Deputy Director and COO
U.S. Census Bureau

Wednesday, November 11

11am-12pm

AI is the Right Term for Our INFORMS Profession

Ever since I went to grad school in the mid 90s, I’ve seen how this profession has had trouble describing what it is we studied and what we do. Around 2011 (or so), we slowly tried to fit into the term Analytics.

I’m now convinced we should proudly say that the INFORMS profession is part of the AI movement– we should say that we do AI.

In this talk, I’ll explain how we came to embrace the term Analytics, how the term AI has evolved, and why we can now lay claim to using AI. I’ll even give my opinion on why the term AI is better than the term Analytics for us.

A term like AI will never be clean. In this talk, I’ll try to give the right nuances and caveats. My goal is that you can explain your involvement with AI to someone at a cocktail party. Also, my goal is to explain it so that if that person at the cocktail party works on self-driving cars or works for OpenAI, you can confidently explain how you fit in to their definition of AI.

I might not convince you, but hopefully I’ll help you shape your own definition, and you’ll better understand how others are using the term.

Michael Watson headshot
Michael Watson

Co-founder
Opex Analytics

Keynotes

Monday, November 9

3:30-4:30pm

Optimization for Machine Learning: Insights and Challenges

What is the mathematical optimization viewpoint on machine learning, and how does it scale to modern applications?

The origins of linear programming stem from military resource allocation over tens of variables and constraints. Recently, techniques from mathematical optimization were used to optimize 175 billion parameters of a highly non-linear language model.

In this talk we’ll survey the algorithms arising from early developments in optimization theory, to giga-scale modern problems that lie at the heart of artificial intelligence research. We’ll describe some recent developments, insights and challenges facing researchers in our field.

Elad Hazan headshot
Elad Hazan

Professor of Computer Science
Princeton University

Evolution of Retail Supply Chains – A Practitioners Perspective

Retail supply chains a few decades ago were about getting products from a local manufacturer to a local seller. The focus was on physical flow. It then evolved to a global scale where the winners extracted value by focusing on scale and efficiency. This is where the physical flow combined with financial angle gained significance. Then the focus shifted to efficiency with real-time visibility and control. Information flow took its place along with financial and physical flow. Still in the retail world, supply chain teams played a secondary role; merchants and store operations organizations ran the show. With the evolution of eCommerce, supply chain is taking its place in the boardroom. Now supply chain is defining the flow of retailer’s strategy; on how to balance cost with service, how to provide innovative options to the shoppers and how to get the right product to the right customer at the right time. What does that mean for the retail industry, what does it mean for operations research and data science practitioners?

Guru Pundoor headshot
Guru Pundoor

Vice President: Supply Chain Strategy, Planning, and Execution
American Eagle Outfitters

Tuesday, November 10

3:30-4:30pm

Statistics, Stochastics, and Service Operations

In this talk, we will discuss how statistical modeling, stochastic analysis, and numerical methods can be fruitfully used in combination with one another to improve decision-making, as illustrated by service operations applications. In particular, the rich stochastic modeling literature clarifies the key statistical features in the underlying observed data that drive performance in such systems, and this impacts the types of statistical models that one should adopt. In addition, considerations related to computational, analytical, and statistical tractability shape one’s modeling choices. This talk will discuss this modeling perspective, and some of the recent theoretical, modeling, and computational tools that support this framework.

Peter Glynn headshot
Peter W. Glynn 
Philip McCord Morse Lecturer

Thomas W. Ford Professor in the School of Engineering Professor, Department of Management Science and Engineering, Institute for Computational and Mathematical Engineering Professor (by courtesy), Department of Electrical Engineering 
Stanford University

What We Wish Application Engineers Knew About Analytics

Software engineering and analytics form the core technical groups in many organizations. However, the two groups often have a different perspective regarding application data. Application engineers rightly focus on creating correctly functioning systems with just enough enabling data. Those in analytics, however, have an endless thirst for data and seem to constantly be asking about information that the applications simply don’t capture. In this talk, I will explore some of the ways we can dialog with our software engineering colleagues to fulfill the goals of both groups.

Simon Lee headshot
Simon Lee

Chief Analytics Officer
Waitr Inc.

Wednesday, November 11

3:30-4:30pm

From Patient to Population: Integrating Personalized Medicine and Public Health

While personalized, or precision, medicine deals with individuals, the choices and behaivors of these individuals affect population level health. Public policy makers need to consider individual behaivor when proposing interventions. This has become painfully obvious with the current pandemic, and if results are available I will discuss the impact of compliance with quarantine, travel recommendations, and face coverings on the spread of the SARS-CoV-2. In addition, the individual plays an important role in a much broader range of health policy problems. I will discuss earlier work related to smoking cessation and colorectal cancer screening. In these cases, patients have choices not only in treatment/screening type but also in adherence to treatment. By considering patient choice and behavior, we can not only better assess the true effectiveness of interventions but also design more targeted interventions. Lastly, our recommendations regarding who should be targeted for treatment impacts system resources, and ultimately the broader patient population as resources become scarce.

Maria Mayorga headshot
Maria Mayorga

Professor of Personalized Medicine in the Edward P. Fitts Department of Industrial and Systems Engineering
North Carolina State University

Quantum Computing and Optimization

Quantum Computing has the potential to significantly disrupt everything we do in optimization and advanced analytics. In this talk, I will provide an introduction to quantum computing and illustrate some use cases that are already showing promise today. Then I will address some questions related to optimization and advanced analytics. When will real-world tasks move from classical computers to quantum computers? What are the use cases that can be addressed by Quantum Computing, in the short and medium term? What are current industry and thought leaders working on today? How do Quantum Computers solve optimization problems?

Yanni Gamvros headshot
Yianni Gamvros

Head of Business Development 
QC Ware

Thursday, November 12

11am-12pm

Stop Chasing Unicorns and Start Hiring the Data Scientists You Need

We’re almost a decade into the data science hiring frenzy, but despite the plethora of quantitative degree programs and bootcamps popping up to train professionals flooding into the field – the demand for analytics talent has continued to greatly outpace the supply. And, as the field continues to evolve, your recruiting strategies must adjust to new shifts in the market, including industry disruption and work from home strategies due to the COVID-19 crisis. What motivates quantitative professionals in today’s market? How much do they earn? How can you secure the talent you need? This session contains Burtch Works’ latest research on today’s analytical talent and how the analytics hiring market has changed over the past year, as well as actionable insights from data science recruiting expert, Linda Burtch, on how to use this data to adapt your recruiting strategy for the today’s market.

Linda Burtch headshot
Linda Burtch

Founder & Managing Director
Burtch Works

Mathematical Optimization for Social Distancing

The spread of viruses such as SARS-CoV-2 brought new challenges to our society, including a stronger focus on safety across all businesses. In particular, many countries have imposed a minimum social distance between people in order to ensure their safety. This brings new challenges to many customer-related businesses, such as restaurants, offices, etc., on how to located their facilities under distancing constraints.  In this talk we propose a parallelism between this problem and the one of locating wind turbines in an offshore area. Even if the two problems may seems very different, there are many analogies between them. In particular, both problems require fitting facilities (turbines or customers) in a given area while ensuring a minimum distance between them. Similarly to nearby customers who can infect each other, also nearby turbines “infect” each other by casting wind shadows (the so-called “wake effect”) that cause production losses. In both problems we want to minimize the overall interference/infection, hence optimal solutions will favor layouts where facilities are as spread as possible. The discovery of this parallelism between the two applications allowed us to apply Mathematical Optimization algorithms originally designed for wind farms, to produce optimized facility layouts subject to social distancing constraints as those arising in the time of COVID-19 pandemic. These methods allow us to challenge the current (manual) layouts and provide new insights on how to improve them. In particular we show that optimized layouts are far from trivial to design and that Mathematical Optimization can make an impact, helping businesses while ensuring safety.

Matteo Fischetti headshot
Matteo Fischetti
IFORS Distinguished Lecturer

Professor of Operation Research at the Department of Information Engineering
University of Padua

Thursday, November 12

3:30-4:30pm

Rebooting Simulation for Big Data, Big Computing and Big Consequences

Lurking in a track or two at the INFORMS Annual Meeting is the “simulation” crowd. Are they still generating random numbers, reducing variance and writing code? Like many fields of study, simulation has been greatly influenced by its history, particularly Conway’s 1963 Management Science paper on “tactical problems in digital simulation” and early languages like GPSS. However, the following statements, none of which were true in 1963, are hard to dispute: (a) There exists, and we can store, lots of data; (b) parallel computing capacity can be rented, and is cheap; and (c) critical societal decisions are based on large-scale computer models. This talk argues that these facts compel a reboot of computer simulation thinking in operations research and management science, and will explore the consequences.

Barry Nelson headshot
Barry Nelson

Walter P. Murphy Professor of the Department of Industrial Engineering and Management Sciences
Northwestern University
Distinguished Visiting Scholar
Lancaster University

Friday, November 13

11am-12pm

Statistical Learning in Operations: The Interplay between Online and Offline Learning

Traditionally, statistical learning is focused on either (i) online learning where data is generated online according to some unknown model; or (ii) offline learning where the entire data is available at the beginning of the process. In this talk we show that combining both approaches can accelerate learning. First, we show the impact of pre-existing offline data on online learning and characterize conditions under which offline data helps (does not help) improve online learning. Second, we show how difficult online learning problems can be reduced to well-understood offline regression problems. We demonstrate the impact of our work in the context of dynamic pricing.

David Simchi-Levi headshot
David Simchi-Levi

Professor of Engineering Systems
MIT

Friday, November 13

3:30-4:40pm

Underrepresentation in STEM: A Danger to the Health of the Nation

Richard Tapia headshot
Richard Tapia
Diversity, Equity, and Inclusion Speaker

University Professor, Maxfield-Oshman Professor in Engineering, Department of Computational and Applied Mathematics (CAAM)
Rice University