About Me

I am a final year PhD student at the MIT Institute for Data Systems and Society (IDSS), co-advised by Marzyeh Ghassemi and Nikhil Agarwal. Broadly, I am passionate about developing data-driven methods to improve health equity. My recent work primarily pursues this goal in the context of randomized experimentation, studying the design, execution, and analysis of clinical trials with heterogeneous populations. While my research has largely focused on healthcare, I am increasingly interested in digital experimentation and A/B testing for online platforms.

I interned at Amazon in Summer 2024, developing novel methods to detect heterogeneity in A/B tests for Amazon’s internal experimentation platform (Weblab). I also previously interned at Microsoft Research New England (Summer 2022), hosted by Allison Koenecke and Lester Mackey. Prior to starting my PhD at MIT, I earned a Bachelor’s in Applied Mathematics at Yale and a Master’s in Data Science at Columbia. I also spent two years working in strategy consulting at Altman Vilandrie & Company.

Outside of work, I’m a huge sports fan, and spend a little too much time watching Liverpool FC and the Boston Celtics. I’ve also recently fallen in love with curling, the perfect way to get through the Boston winter.

Working Papers

Scalable Heterogeneity Detection in Online Experiments
Presented at CODE 2024
Hammaad Adam, Merlin Heidemanns, Doug Hains, James McQueen.

Data-Driven Recruitment for Generalizable Experiments
Presented at CODE 2024
Hammaad Adam, Lars Hulstaert, Yu Mao, Hans Verstraete, Marzyeh Ghassemi.

Improving Organ Procurement for Health Equity
In preparation. Received MIT Racism Research Award, 2023.
Hammaad Adam, Nikhil Agarwal, Marzyeh Ghassemi.

Publications

Clinical Information Extraction with Large Language Models: A Case Study on Organ Procurement
AMIA, 2024.
Hammaad Adam, Junjing Lin, Jianchang Lin, Hillary Keenan, Ashia Wilson, Marzyeh Ghassemi.

Should I Stop or Should I Go? Early Stopping with Heterogeneous Populations
NeurIPS, 2023. Spotlight Presentation (top ~3% of submitted papers)
Hammaad Adam, Fan Yin, Mary Hu, Neil Tenenholtz, Lorin Crawford, Lester Mackey, Allison Koenecke.
Presented at CoDE@MIT 2023 and IC2S2 2023

Machine Learning for Demand Estimation in Long Tail Markets
Management Science, 2023 Hammaad Adam, Pu He, Fanyin Zheng.
Talks: Presented at INFORMS in 2021 and 2020

Mitigating the impact of biased artificial intelligence in emergency decision-making
Communications Medicine, 2022
Also presented at two NeurIPS 2022 workshops: Trustworthy and Socially Responsible ML and Deploy and Monitor ML
Hammaad Adam, Aparna Balagopalan, Emily Alsentzer, Fotini Christia, Marzyeh Ghassemi.
Featured in MIT News.

Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations
AIES, 2022
Hammaad Adam, Ming Ying Yang, Kenrick Cato, Ioana Baldini, Charles Senteio, Leo Anthony Celi, Jiaming Zeng, Moninder Singh, Marzyeh Ghassemi.
Featured in STAT and MIT News.

Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
Nature Biotechnology, 2022
H Tomas Rube, Chaitanya Rastogi, Siqian Feng, Judith F Kribelbauer, Allyson Li, Basheer Becerra, Lucas AN Melo, Bach Viet Do, Xiaoting Li, Hammaad Adam, Neel H Shah, Richard S Mann, Harmen J Bussemaker.

CV

Curriculum Vitae (updated 9/2024)