Part of the TPC Seminar Series
Speaker: Kexin Huang, Stanford University
Date: Friday, March 6, 2026
Time: 1 p.m. (CT)
Location: Virtual
Abstract:
Biomedical research drives advances in human health, drug discovery, and clinical care, yet it is increasingly hindered by fragmented workflows across complex experiments, massive datasets, and vast literature. We introduce Biomni, a general-purpose biomedical AI agent that autonomously executes diverse research tasks. Biomni first employs an action discovery agent to map the biomedical action space, mining tools, databases, and protocols from tens of thousands of publications across 25 domains. Built on this foundation, its generalist architecture integrates LLM reasoning with retrieval-augmented planning and code execution, enabling dynamic composition of complex workflows without predefined templates. Benchmarking shows strong generalization across tasks such as gene prioritization, drug repurposing, rare disease diagnosis, microbiome analysis, and molecular cloning, all without task-specific tuning. Case studies further demonstrate Biomni’s ability to analyze multi-modal data and generate experimentally testable protocols. Biomni envisions AI biologists working alongside humans to accelerate biomedical discovery, clinical insight, and healthcare.
Biography:
Kexin Huang is a final-year PhD student in Computer Science at Stanford University, advised by Prof. Jure Leskovec. His research focuses on leveraging AI to drive novel, deployable, and interpretable biomedical discoveries, while also tackling fundamental AI challenges such as multi-modal modelling, uncertainty quantification, and agentic reasoning. His work has been published in Nature Medicine, Nature Biotechnology, Nature Chemical Biology, Nature Biomedical Engineering, Nature, and machine learning conferences including NeurIPS, ICML, and ICLR. He has received 6 best paper awards at NeurIPS/ICML workshops, ISMB, and ASHG, with cover article in Nature Biotechnology and Cell Patterns. His research has been featured in major media outlets such as Forbes, WIRED, and MIT Technology Review. He has also contributed to machine learning research at leading companies and institutions, including Genentech, GSK, Pfizer, IQVIA, Flatiron Health, Dana-Farber Cancer Institute, and Rockefeller University.