Thanh Lam at his PhD graduation with his mom
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Hello! 😄

🎓 My PhD graduation, with my mom 💛

Hey there I'm

Thanh Lam

Machine Learning Researcher · Quantitative Researcher

I'm interested in studying and developing intelligent systems — in particular, where elegant theory meets algorithms that actually generalize.

Previously earned a PhD in Computer Science at NUS, where I was fortunate to be supervised by Prof. Bryan Low (NUS) and Prof. Patrick Jaillet (MIT). Since then I've been a Quantitative Researcher at Citadel Securities, building and evaluating predictive systems in real-world, high-noise environments.

I'm currently exploring foundation models, reasoning, and scalable training, with the goal of contributing to frontier AI. I'm also deeply enthusiastic about robotics, having worked briefly in the field earlier in my journey.

Read my papers Grab my CV ↗

01 Education & recognition

Where I studied

2019 — 2023

Ph.D., Computer Science

National University of Singapore · GPA 4.6/5

Fortunate to be supervised by Prof. Bryan Low (NUS) & Prof. Patrick Jaillet (MIT). Sole recipient of the SMART Fellowship (1 of 4 universities, 2019).

2022

Visiting Graduate Student

MIT · LIDS

Fall 2022 with Prof. Patrick Jaillet, funded by the SMART Fellowship.

2015 — 2019

B.Comp., CS (First Class Honours)

National University of Singapore · GPA 4.6/5

ASEAN Undergraduate Scholarship — 3 students nationwide, Vietnam 2015.

Honors 🏆

  • 2019 SMART Graduate Fellowship (sole recipient)
  • '20–'21 Research Achievement Award (×2)
  • 2021 Honor List for Teaching Excellence
  • 2019 Dean's List
  • 2015 ASEAN Undergraduate Scholarship

Service 🤝

  • Reviewer — ICML, NeurIPS, ICLR, AISTATS ('21–'23)
  • Journal — Neural Networks, IEEE RA-L
  • Volunteer — ICLR, ICML, NeurIPS ('20–'21)

02 Papers I'm proud of

Published at ICML & ICLR, with recent preprints. * = equal contribution · full list on Google Scholar.

  1. ↙ my favorite
    ICLR '23

    Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation

    Thanh Lam, Arun Verma, Bryan Low, Patrick Jaillet

    OpenReview ↗
  2. ICML '21

    Model Fusion for Personalized Learning

    Thanh Lam*, Trong Nghia Hoang*, Bryan K. H. Low, Patrick Jaillet

    PMLR ↗
  3. ICML '20

    Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion

    Trong Nghia Hoang*, Thanh Lam*, Bryan K. H. Low, Patrick Jaillet

    PMLR ↗
  4. arXiv

    On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature

    Xu Cai*, Thanh Lam*, Jonathan Scarlett

    arXiv:2202.10615 ↗
  5. arXiv

    Implicit Regularization via Spectral Neural Networks and Non-linear Matrix Sensing

    Hong T. M. Chu, Subhro Ghosh, Thanh Lam*, Soumendu Sundar Mukherjee (*co-first author)

    arXiv:2402.17595 ↗
  6. Working paper

    Online Data Procurement with Self-Interested Agents

    Thanh Lam, Bryan Low, Patrick Jaillet