Automated decision making via optimization and machine learning

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This research program will develop a new automated decision making platform at the intersection of inverse optimization, robust optimization and machine learning. Many decision making problems combine optimization and machine learning. For example, an optimization model that generates decisions may use auxiliary parameters predicted via machine learning. To train the machine learning model, one needs data on the parameters. However, it is often easier and more relevant to obtain data on the desired solutions directly. Thus, the goal of my Discovery program is to develop new computational tools for this predict-then-reconstruct paradigm. In particular, I will use machine learning to predict desirable characteristics of an optimal solution. Then, I will develop new inverse optimization methods to learn parameters of a robust optimization model that can generate a solution with the desired properties. The framework will be automated in the sense all that one needs are basic covariates that relate to solution quality; with this data, machine learning, then inverse optimization, then robust optimization can be implemented without human intervention. The main application in this research program is radiation therapy treatment planning. Radiation therapy is one of the primary ways to treat cancer. The current process to design a treatment is inefficient, relying on manual trial-and-error effort. The challenge stems from the difficulty in determining appropriate parameters in the planning software that will produce an acceptable treatment. Using my predict-then-reconstruct approach, I will demonstrate automated treatment plan generation. A deep learning model trained on historical treatments will predict a clinically acceptable dose distribution for a patient, given only imaging data. Then, an inverse optimization model will learn parameters of a robust optimization model that will create a deliverable treatment plan with clinically desirable characteristics. My automated planning approach will simultaneously improve efficiency and process standardization without sacrificing treatment personalization. While the example application is radiation therapy, the general methodology will be broadly applicable. Optimization and machine learning are fast-growing fields that are in short supply of graduates. My trainees will develop highly employable skills that will be valued in data-driven industries like healthcare, transportation, manufacturing, finance, supply chain management, energy and defense. The application of my research to radiation therapy also has major economic potential. Radiation therapy is used to treat half of all cancer patients, but there is a looming shortage of skilled personnel to design and deliver treatments. My research helps close the demand-supply gap and allows radiation therapy to be delivered at scale, particularly important in developing countries. Thus, my research represents a made-in-Canada innovation with global impact.