Plenaries & Keynotes

Cynthia Barnhart

Chancellor and Ford Foundation Professor of Engineering

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. Where do quantitative professionals want to work? What motivates them? 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, 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 current market.

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- IFORS Distinguished Lecturer

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

Quantum Computing – Why, What, When, and How

Yianni Gamvros

Head of Business Development 
QC Ware

Optimization for Machine Learning: Insights and Challenges

Elad Hazan

Professor of Computer Science
Princeton University

Sheldon Jacobsen

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

Simon Lee headshot
Simon Lee

Chief Analytics Officer
Waitr Inc.

Rebooting Simulation for Big Data, Big Computing and Big Consequences

Barry Nelson

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

Prepositioning Disaster Relief Supplies using Robust Optimization

Emergency disaster managers are concerned with responding to disasters in a timely and effective manner.  The effectiveness of response operations can be improved by prepositioning relief supplies in anticipation of disasters. We study the problem of determining the location and amount of disaster relief supplies to be prepositioned. These supplies are stocked when the locations of affected areas and the amount of relief items needed are uncertain. Furthermore, a proportion of the prepositioned inventory, which is also uncertain, might be damaged by the disaster.

We propose a two-stage robust optimization model. The location and amount of prepositioned relief supplies are decided in the first stage before a disaster occurs.  In the second stage, a limited amount of relief supplies can be procured post-disaster and prepositioned supplies are distributed to affected areas. The objective is to minimize the total cost of supplying disaster relief materials. We solve the proposed robust optimization model using a column-and-constraint generation algorithm. Two optimization criteria are considered: total absolute cost and regret. A case study of the hurricane season in the southeast US is used to gain insights on the effects of optimization criteria and critical model parameters to relief supply prepositioning strategy.

Maria Mayorga

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

Nancy Potok

former Deputy Director and COO
U.S. Census Bureau

Evolution of Retail Supply Chains – A Practitioners Perspective

Plenaries & Keynotes
Guru Pundoor

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

Statistical Learning in Operations: Theory and Practice

David Simchi-Levi

Professor of Engineering  Systems

Michael Watson

Opex Analytics