01
AI/HPC science at scale
Research on scientific workflows that combine machine learning, molecular simulation, supercomputing, and large screening campaigns for COVID therapeutics and oncology.
AI for science / Harvard STS / high-performance computing
AI for science, supercomputing, and accountable algorithmic systems.
I am a computer scientist whose work connects award-winning AI/HPC drug discovery, Harvard STS research on science and democracy, and the public legitimacy of algorithmic systems. I am also a former quantitative developer with a background in performance-sensitive infrastructure.
- Recognition
- 2x ACM Gordon Bell Special Prize awardee
- STS
- Visiting Research Fellow, Harvard Kennedy School
- Research leadership
- PI on 2 CACHE computational hit-finding challenges
data-sync
OpenAlex verified
$ publications.sync --source openalex
- Curated records
- 45
- OpenAlex works
- 53
- Citations
- 1,617
- h-index
- 19
- ORCID
- 0000-0002-3697-7070
- Last source update
- 2026-06-04
Selected work
Systems work with measurable stakes.
02
Gordon Bell Prize research
Contributor to two ACM Gordon Bell Special Prize winning teams and 2021 finalist work using AI and supercomputing for urgent COVID-19 science.
03
Drug discovery leadership
Ph.D. work and CACHE PI projects focused on scalable structure-based drug discovery, including 100x faster protein-ligand docking and reinforcement-learning molecular modeling.
View RLMM04
Science, democracy, and AI
Harvard STS fellowship, Pozen Lectureship teaching, and public writing on algorithmic scrutiny, human rights, democratic participation, and institutional accountability.
Research
From scientific scale to institutional consequence.
Scientific AI and HPC
My dissertation work focused on AI and high-performance computing for structure-based drug discovery: accelerating docking, designing scalable screening workflows, and making those workflows useful to domain scientists. That work includes 100x faster SARS-CoV-2 protein-ligand docking and COVID-19 research recognized by DOE and ACM.
Drug-discovery programs
As PI on two CACHE computational hit-finding challenges, I worked on ligand discovery for SARS-CoV-2 NSP13 and LRRK2, connecting model performance to usable molecular-design decisions.
Interpretability, law, and democracy
My STS and human-rights work asks how model interpretation, evidence, public explanation, and institutional power meet when algorithmic systems shape civic life, scientific authority, and democratic participation.
Publications
Checked, structured, and easy to update.
Automated publication sync
The publication archive is generated from structured data. Running npm run sync:publications fetches OpenAlex author records, collapses duplicate preprint/final-paper entries, applies curated overrides, and rebuilds the archive page.
AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection
Scientific Reports
GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics
International Journal of High Performance Computing Applications
Large Language Models for Science
Artificial Intelligence for Science
Writing and teaching
Computing as infrastructure, evidence, and public power.
AI, Algorithms, and Human Rights
University of Chicago course awarded the Pozen Family Center for Human Rights Graduate Lectureship, cross-listed in Human Rights, Computer Science, and Media Arts and Design.
Human-in-the-loop accountability
Public writing in Tech Policy Press on why human review is not enough for democratic accountability in automated decisions.
Read essaySocial media and democracy
Writing on platform legitimacy, online public spheres, polarization, and how algorithmic systems reshape political authority.
Read essayWeb CV
Short version.
Ph.D., M.S., and B.A. from the University of Chicago
Ph.D. thesis: Artificial Intelligence and High-Performance Computing for Accelerating Structure-Based Drug Discovery.
Two ACM Gordon Bell Special Prize winning teams
AI/HPC research recognized for COVID-19 spike dynamics and genome-scale language models, plus 2021 finalist work and DOE Secretary's Honor Award recognition.
Drug-discovery grants, talks, teaching, and mentoring
PI on two CACHE challenges, 24 presentations and invited talks, UChicago instructor, and mentor for students working across LLMs, genomics, docking, and graph neural networks.
Contact