PeopleofSuperintelligence

Researcher

Jen Ning Lim

Jen Ning Lim is a machine learning researcher whose work on kernel hypothesis tests, energy-based models and particle methods for latent variable models appears at NeurIPS, AISTATS and ICML. He earned a BSc in mathematics and computer science at the University of Bristol in 2018 and an MSc in data science and machine learning at University College London in 2020. His papers of 2019 and 2020, written with Makoto Yamada, Wittawat Jitkrittum and collaborators, built kernel-based statistical tests for comparing models and selecting features. During his statistics PhD at the University of Warwick, supported by the Feuer International Scholarship in Artificial Intelligence, he coauthored energy-based model papers with Sebastian Vollmer and Andrew Duncan in 2023, and particle-method papers for latent variable models and semi-implicit variational inference with Adam Johansen in 2023 and 2024. In February 2026, he coauthored a paper on LLM agents that refine scientific hypotheses, with Relation Therapeutics in London as his affiliation.

Now at
Relation Therapeutics
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Timeline

  1. 2018

    Mathematics and computing at Bristol

    He earns a BSc in Mathematics and Computer Science at the University of Bristol in 2018.

  2. 2019

    Kernel Stein tests at NeurIPS

    In 2019, he coauthors Kernel Stein Tests for Multiple Model Comparison at NeurIPS with Makoto Yamada, Bernhard Schölkopf and Wittawat Jitkrittum. The paper proposes two statistical tests for deciding which of several candidate models are as good as the best one.

  3. 2020

    UCL master's and selective kernel tests

    He completes an MSc in Data Science and Machine Learning at University College London in 2020. At AISTATS in 2020, he coauthors More Powerful Selective Kernel Tests for Feature Selection with Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui and Hidetoshi Shimodaira, extending two feature-selection tests to condition on the minimal conditioning event.

  4. 2023

    PhD work on energy-based models

    He is a PhD student in Statistics at the University of Warwick, supervised by Sebastian Vollmer at DFKI and Andrew Duncan at Imperial College London, and supported by the Feuer International Scholarship in Artificial Intelligence. In 2023, he coauthors Energy-Based Models for Functional Data using Path Measure Tilting at AISTATS with Vollmer, Lorenz Wolf and Duncan, and Energy Discrepancies, a loss for energy-based models that avoids computing scores and expensive Markov chain Monte Carlo.

  5. 2023

    Particle algorithms for latent variables

    In 2023, he coauthors Particle algorithms for maximum likelihood training of latent variable models at AISTATS with Juan Kuntz and Adam Johansen. The paper identifies gradient flows of a free energy functional and discretizes them into particle-based algorithms for maximum likelihood estimation.

  6. 2024

    Momentum particle maximum likelihood

    At ICML in 2024, he coauthors Momentum Particle Maximum Likelihood with Juan Kuntz, Samuel Power and Adam Johansen; the paper blends Nesterov's Accelerated Gradient method, the underdamped Langevin diffusion and particle methods into an algorithm for latent variable models. At the University of Warwick, he is supervised by Johansen in Statistics and works on optimization problems on probability spaces.

  7. 2024

    Particle semi-implicit variational inference

    With Adam Johansen, he coauthors Particle Semi-Implicit Variational Inference for NeurIPS 2024. The paper introduces Particle Variational Inference, a method for semi-implicit variational inference that uses empirical measures to approximate optimal mixing distributions.

  8. February 2026

    Hypothesis refinement at Relation Therapeutics

    In February 2026, he coauthors Tiny Moves: Game-based Hypothesis Refinement with Sabine Reichert, Thomas Charman, Lindsay Edwards and Anna Gogleva, with Relation Therapeutics in London as their affiliation. It proposes The Hypothesis Game, in which LLM agents refine a shared hypothesis state using a fixed grammar of reasoning moves, and tests it on pathway-level mechanistic refinement tasks.

Career

  1. From 2026Relation Therapeutics

Education

  1. 2020University College LondonMSc
  2. 2018University of BristolBSc
  3. University of WarwickPhD

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University College London. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes University College London. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.
  3. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University College London. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  4. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes University College London. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Jen Ning Lim — Data Science and its Applications dsa.dfki.de. Checked 22 October 2026.
  2. Kernel Stein Tests for Multiple Model Comparison papers.nips.cc. Checked 22 October 2026.
  3. More Powerful Selective Kernel Tests for Feature Selection proceedings.mlr.press. Checked 22 October 2026.
  4. Energy-Based Models for Functional Data using Path Measure Tilting proceedings.mlr.press. Checked 22 October 2026.
  5. Energy Discrepancies: A Score-Independent Loss for Energy-Based Models arxiv.org. Checked 22 October 2026.
  6. Particle algorithms for maximum likelihood training of latent variable models proceedings.mlr.press. Checked 22 October 2026.
  7. Momentum Particle Maximum Likelihood proceedings.mlr.press. Checked 22 October 2026.
  8. About me - Jen Ning Lim jenninglim.github.io. Checked 22 October 2026.
  9. Particle Semi-Implicit Variational Inference arxiv.org. Checked 22 October 2026.
  10. Tiny Moves: Game-based Hypothesis Refinement arxiv.org. Checked 22 October 2026.