Tai Dang
I am a researcher working on Generative Diffusion Models and Reinforcement Learning, with applications in AI for Science, particularly computational biology and molecular discovery.
My research focuses on developing principled generative models and reinforcement learning methods for scientific discovery at scale.
Experience
Stanford University — Visiting Researcher
2024 – Present
- Post-trained AlphaFold 3 via reinforcement learning, achieving SOTA structure fidelity.
- Optimized large-scale drug screening with Bayesian optimization.
University of Massachusetts Amherst — Research Assistant
2023
- Built multi-modal retrieval system for Outside-Knowledge Visual QA.
- Analyzed policy specialization in OLS Convex Coverage Set under varying discount factors.
Ontocord — Research Intern
2023
- Distilled a 7B LLM to 5× smaller size while maintaining performance.
- Built open-source Vietnamese LLM using 1TB processed data.
EOG Resources — Software Engineer Intern
2023
- Built graph-based visualization tools for complex data analysis.
- Migrated repositories to GitHub Actions with OIDC authentication.
VietAI — Research Intern
2022
- Developed SOTA English–Vietnamese translation model.
- Improved biomedical NMT by +6 BLEU and released Vi-MedNLI dataset.
Education
Stanford University
Visiting Researcher
Advisor: Thang Luong (Google DeepMind), Jeff Glenn (Stanford Medicine)
University of Massachusetts Amherst
B.S. in Computer Science, May 2024
Advisor: Bruno Castro da Silva
selected publications
- PreprintHigh-Fidelity Molecular Structure Prediction via Reinforcement Learning2026Preprint
- GEM Workshop 2025Preferential Multi-Objective Bayesian Optimization for Drug Discovery2025
- PreprintGathering Context that Supports Decisions via Entropy Search with Language Models2025
- EACL 2023Enriching Biomedical Knowledge for Low-resource Language Through Large-Scale Translation2023EACL 2023