Sarah Rathnam

About Me

I am a PhD candidate in Applied Mathematics at Harvard University. I am co-advised by Finale Doshi-Velez of the Data to Actionable Knowledge (DtAK) Lab and Susan Murphy of the Statistical Reinforcement Learning Lab. My research focuses on reinforcement learning and machine learning for healthcare.

Before my return to academia, I worked for nearly a decade in quantitative finance at various hedge funds and trading firms.

Publications

Sarah Rathnam, Sonali Parbhoo, Siddharth Swaroop, Weiwei Pan, Susan A. Murphy, and Finale Doshi-Velez. Rethinking Discount Regularization. JMLR 2024.

Sarah Rathnam, Abhishek Sharma, Kamber L. Hart, Pilar F. Verhaak, Thomas H. McCoy, Roy H. Perlis and Finale Doshi-Velez. Association between prescriber practices and major depression treatment outcomes. Journal of Mood & Anxiety Disorders.

Sarah Rathnam, Kamber L. Hart, Abhishek Sharma, Pilar F. Verhaak, Thomas H. McCoy, Finale Doshi-Velez, and Roy H. Perlis. Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians. JAMA Psychiatry.

Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan A. Murphy, and Finale Doshi-Velez. The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning. ICML 2023.