Hi, I’m a computer scientist working on AI for science and the public life of algorithms.
I build methods for molecular discovery and study how computational systems shape scientific and public decisions.
01 / Scientific research
Molecular discovery, AI-guided sampling, and representations of chemical space.
Computational drug discovery
We screened over 6.5 million compounds and identified a SARS-CoV-2 main-protease (3CLpro/Mpro) inhibitor, validated by biochemical assays and crystallography. A separate AI-docking study reported 100× acceleration with no significant change in detection.
First & corresponding author · Mpro study, JCIM 2022
Coauthor · Docking study, Scientific Reports 2023
AI-guided sampling & exploration
My work spans reinforcement learning for molecular modeling with RLMM and AI-driven protein simulations. I also contributed to GenSLMs, which uses genome-scale language models to study viral evolution.
Coauthor on 2020 & 2022 ACM Gordon Bell Special Prize research
Chemical-space representations
I use graphs and hypergraphs to connect fragments, scaffolds, and compounds. With ChemoGraph, we built an interactive tool to explore related molecules and generate candidates beyond an existing database.
First author · Scaffold Embeddings & SIMG
Coauthor · ChemoGraph, 2023
02 / Policy & science and technology studies
I study how algorithmic systems are governed and what institutions owe the public.
Can human reviewers challenge automated decisions—and who bears responsibility for the systems they oversee?
Cited in the FTC’s 2022 report to Congress, Combatting Online Harms Through Innovation (p. 50, n. 282).
How platform design shapes democratic life, human rights, and public legitimacy.
A course and conference on the social consequences of AI, profiled in 2024.
The human work behind AI: how researchers collaborate and who can participate in producing scientific knowledge.
The hidden human effort behind AI systems.
