Trustworthy
World Models
for Life Science
Understand biological systems well enough to predict and act reliably.
We develop AI systems that learn representations of biological states, predict their responses to perturbations, and guide interventions across scales, from molecules to patients.
Hao Xu, Ph.D.
Investigator, Brigham and Women's Hospital
Instructor in Medicine, Harvard Medical School
Across biological scales Molecule→ Interaction→ Network→ Cell→ Organism
01News
News
| Jul 31, 2026 | How Well Can Off-the-Shelf LLMs Elucidate Molecular Structures from Mass Spectra Using Chain-of-Thought Reasoning? published in Journal of Cheminformatics. |
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| Jun 24, 2026 | Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs accepted to ECCV 2026. |
| Jun 09, 2026 | ExplainBind: Explainable Physicochemical Determinants of Protein-Ligand Binding via Non-Covalent Interactions posted as a preprint on bioRxiv. |
| May 02, 2026 | EvoRMD: Integrating Biological Context and Evolutionary RNA Language Models for Interpretable Prediction of RNA Modifications published in Genome Biology. |
| Apr 04, 2026 | Not All Directions Matter: Toward Structured and Task-Aware Low-Rank Adaptation accepted to ACL 2026. |
| Oct 03, 2025 | HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing Performance accepted to Findings of ACL 2026. |