Research Interests

I consider myself a generalist as opposed to a specialist. In short, a specialist tends to focus on a relatively narrow domain and become an expert in it. A generalist, however, broadens the domain at the expense of losing depth. While this might hinder a generalist from becoming a true expert in one domain, it allows him/her to connect and join ideas from different disciplines and act as a bridge between specialists. As a generalist, I try to be topic- and method-agnostic, but I have priorities in my research, nonetheless.

Since 2018, my advisors (Prof. Nick Street and Prof. Barrett Thomas) and I started exploring the application of Reinforcement Learning (a branch of machine learning that deals with finding the best course of action in sequential decision-making settings) in  Health Analytics. More specifically, we look at the dosing of a commonly used anticoagulant called warfarin.

In the first part of this work, we showed that one can achieve a better dosing protocol using Deep Q-Networks (DQN). However, the model acts as a black box and the user might not trust its recommendations. In the second phase, we implemented the work using a Policy Gradient method (PPO). The idea is to learn a limited set of doses and then try to make it easier to understand and use. The end goal is to have a dosing protocol that performs better and is explainable and individualized.

My other areas of interest include:

Current Research




For my dissertation, I developed a Reinforcement Learning package in Python called ReiL. It is not the best, the fastest, the most efficient, or even the right way of doing RL, but it is available for use under MIT License. You may install it from PyPI (pip install ReiL), or fork the source code from the repository here:

Most of the code is fully type-annotated and documented, and I am dedicated to continuing to improve and support it.