Hello! I am a masters student at Harvard Computational Science. I am working on the application of inverse reinforcement learning and interpretable machine learning in healthcare. My advisor is Finale Doshi-velez.
My research focuses on the batch, off-policy, model-free inverse reinforcement learning and imitation learning in healthcare.
I have written peer-reviewed papers and a thesis on the problem of imitating and explaining experts in healthcare using Inverse Reinforcement Learning.
- Donghun Lee, Srivatsan Srinivasan, Finale Doshi-Velez, Truly Batch Apprenticeship Learning with Deep Successor Features (IJCAI, 2019) Link
Other research projects
I worked on a number of small research projects. For other technical projects, check this page out.
- Fairness and interpretation of black-box algorithmic decisions for peer-to-peer loan lending (done in collaboration with Square Capital).
- Evaluating the effect of adding Gradient noise on the learning performance of deep neural networks.
- Empirical Evaluation of Input-Convex Neural Network.
- Empirical Evaluation of Kernel-based Imitation Learning.
- Development of a Deep RL Poker Agent using Neural Fictious Self-Play.
- Tutorials on Least-Squares-Temporal-Difference (LSTD) and other reinforcement learning algorithms.
- Digital Silo (Google Summer of Code, 2017).