My research focuses on the development of novel optimization methods and the application of those methods to solve complex decision-making problems primarily in healthcare and sports.

    Inverse optimization

    The goal of inverse optimization is to “reverse engineer” parameters of an optimization model that make a given, observed decision optimal. If it is not possible to make the decision exactly optimal, e.g., the data is noisy or the model is an approximation, then a measure of suboptimality is typically minimized. Viewed through the lens of model fitting, my main interest is to develop new approaches for inverse optimization that optimize and measure data-model fit. Given the increasing amounts of data that are generated as the result of a decision process, I am also interested in finding innovative applications for inverse optimization. Relevant publications:

    Robust optimization

    Robust optimization is used to optimize in the face of uncertainty. I am interested in theory, modeling, solution methods, and applications of robust optimization. The majority of my work to date has been in robust optimization, especially applied to radiation therapy treatment planning. Relevant publications:

    Radiation therapy

    Radiation therapy is one of the primary techniques used to treat cancer. Beams of radiation are delivered from different angles around a patient, targeting a tumor at their intersection while aiming to spare nearby critical organs. I deploy a combination of robust optimization, inverse optimization, and machine learning to improve the quality of treatments and the efficiency of the treatment planning process. Relevant publications:

    Cardiac arrest and public access defibrillator location

    The likelihood of survival from cardiac arrest is very time sensitive. One method to reset the heart’s normal rhythm is to deliver an electric shock using an automated external defibrillator (AED), which can be used by lay responders with little to no training. My research focuses on developing optimization methods for AED deployment, ranging from static deployment in public locations to delivery via drone. Accordingly, I am interested in approaches to estimate cardiac arrest risk and quantify accessibility. Relevant publications:

    Global health

    I am interested in bringing operations research methods to bear on important problems in global health. My main research project in this domain focuses on developing optimization models to guide ambulance deployment and routing in developing country settings, which are subject to major uncertainties in demand and travel time. Click here for a two-minute introduction to the project. Relevant publications:

    Sports analytics

    I am a passionate sports fan and enjoy analyzing interesting (decision) problems in sports. I have worked on topics in hockey, baseball, tennis, golf, football, and curling. Click here for a video of a talk I gave on sports analytics. Here is my TEDxUofT talk on baseball flexibility. A team of students and I developed an interactive NHL Expansion Draft optimization tool, which allows users to optimize protection and selection decisions in real time. Relevant publications:

    Healthcare operations

    I am interested in a variety of healthcare operations problems including scheduling and process flexibility. Relevant publications:


    Although I am not currently active in this area, I have previous experience applying optimization methods to problems in green building design and wind farm layout. Relevant publications:


    I enjoy developing innovative teaching methods using games and other interactive activities. Relevant publications:

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