Siyuan Guo

I am a machine learning researcher trying to understand intelligence. I am currently an AI scientist at Prior Labs, where I work on scaling laws and foundation models for causal inference. In 2026 I was named to the Forbes 30 Under 30 Europe list for Science & Healthcare. I also serve as an Area Chair for ICLR 2027. I did my PhD at the University of Cambridge with Ferenc Huszár and at the Max Planck Institute for Intelligent Systems with Bernhard Schölkopf, and interned at Meta FAIR in New York. My long-term interest is AI + Science: building a physics-inspired mathematical theory of intelligence, and using AI to accelerate scientific discovery.

Research

1. How can principles from nature guide the design of learning systems? This underpins my physics of learning programme: learning, too, follows a least-action principle, and classical learning algorithms are derivable from it.
2. How can we identify fundamental laws of nature directly from data? This includes the causal de Finetti line of work on the Bayesian foundations of causality, and foundation models for in-context causal inference in scientific applications.

News

• Oct 2026Keynote at the AI in Science Summit 2026, Dublin — Europe's flagship conference on AI for scientific discovery, an official event of the Irish EU Presidency. Speaking in the Science for AI strand.
• Jul 2026Invited Speaker at the ICML AI4Math workshop in Seoul on the physics of learning.
• May 2026Prior Labs was acquired — an exciting time at the intersection of academia and industry.
• Apr 2026Named to the Forbes 30 Under 30 Europe list for Science & Healthcare. [LinkedIn]
• Feb 2026Invited talk at CISPA Helmholtz Center: Physics of Learning and Structure.
• Feb 2026Invited talk at MBZUAI: Physics of Learning and Structure.
• Jan 2026Invited talk at Lawrence Berkeley National Laboratory: Physics of Learning and Structure.

Students & alumni

• Yuche Gao Co-advised with J. M. Hernández-Lobato PerturbPFN for Drug Perturbation, FMSD@NeurIPS 2026
• Anna Kerekes Co-mentored with B. Schölkopf
• Michelle Chao Chen Co-advised with B. Schölkopf Structural Information in LLMs, ACL 2026
• Bálint Mucsányi Co-mentored with P. Reizinger Identifiable Representations for RL, ICLR 2026
• Moritz Miller Co-advised with J. Peters and B. Schölkopf In-context Counterfactual Reasoning, NeurIPS 2025
• Szilvia Ujváry Co-mentored with F. Huszár

Selected publications

• Physics of Learning: A Lagrangian Perspective to Different Learning Paradigms Siyuan Guo, Bernhard Schölkopf arXiv:2509.21049, 2025 Cited in Forbes 30 Under 30
• Do-PFN: In-Context Learning for Causal Effect Estimation Jake Robertson*, Arik Reuter*, Siyuan Guo, Noah Hollmann, Frank Hutter, Bernhard Schölkopf NeurIPS, 2025 Spotlight · top 3.2%
• Do Finetti: On Causal Effects for Exchangeable Data Siyuan Guo, Chi Zhang, Karthika Mohan, Ferenc Huszár*, Bernhard Schölkopf* NeurIPS, 2024 Oral · top 0.5%
• Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data Siyuan Guo*, Viktor Tóth*, Bernhard Schölkopf, Ferenc Huszár NeurIPS, 2023 · code

* equal contribution  ·  † equal supervision  ·  full list on Google Scholar