Abhishek Sharma

I am a fifth year Ph.D. student in the Data to Actionable Knowledge (DtAK) lab at Harvard University, where I am advised by Finale Doshi-Velez.

My research focuses on reinforcement learning (RL) and representation learning for decision-making. I create models that are not only predictive but also align with important properties, such as interpretability and safety.

I have collaborated with hospitals (Massachusetts General Hospital, Brigham and Women’s Hospital) and the Singapore government (Temasek Laboratories) on applying machine learning to healthcare challenges. During my internship at Google, I worked on developing interpretable foundational models for waveform data. My PhD projects include creating interpretable and safe batch RL methods to improve clinician decisions. I have also applied my work to depression subtype discovery and disease progression modeling.

Before Harvard, I earned a M.S. in Computer Science from the University of Massachusetts, Amherst, where I worked with Madalina Fiterau and Philip Thomas. Even prior to that, I obtained a bachelor’s and a master’s degree in Materials Science and Engineering at IIT Madras, India, where I worked with Nandan Sudarsanam.