Simon Frieder

ML Researcher. Mathematician. Scientist.

Researcher @ University of Oxford
PhD/DPhil under T. Lukasiewicz
Also affiliated with Tim Gowers’ group at Cambridge University
Publication list: Google Scholar
X: @friederrrr (four ‘r’s ;)

I want to benchmark & automate advanced mathematical problem-solving using deep learning.

This homepage is still a bit under construction! In time, it will replace my previous university website at Oxford, which I don’t maintain anymore.
Contact me at: simon.frieder (at) cs.ox.ac.uk

Talks I’ve given in 2025

• short talk about the AIMO & presenting AIMO winners at the IMO25 ceremony
• talk at the AI4MATH workshop in July 2025
• invited talk at the IHES for the LLM & math seminar
• invited talk at the NII in May 2025
• invited talk at Harvard CMSA in February 2025

I’m mentioned in the media (selection):

• Neue Zürcher Zeitung (NZZ, July 2025)
• Nature News (May 2025)
• Nature News (Feb. 2025)
• Nature News (Jan. 2025)
• podcast interview at Schweizer Radio und Fernsehen (SRF)
• mentioned in MIT Tech (Oct. 2024)
• report by AI Scholar (Jan. 2024)
• report by ZD Net (2023)
• report by ZD Net (2023)
• long report by Ars Technica on my NeurIPS paper (2023)
• report by Synced Review (2023)

Further roles, affiliations, contributions:

• Serving as area chair for the AI4MATH workshop at ICML25
• open-source theorem prover for Euclidean geometry: https://arxiv.org/abs/2411.11938
• The mathematical datasets we need to get “math copilots”: https://arxiv.org/abs/2412.15184
• AIMO Prize Manager. Working with Terence Tao, Tim Gowers, and the AIMO Advisors to stimulate research via progress prizes to devise AI models that solve IMO-level math problems.

Old:

• Paper published at ICML24: Language Models as Science Tutors.
• Two accepted papers (first-author) at ICLR 2024 (TP track).
• December 2023: Talk at Aizawa group from the University of Tokyo / National Institute of Informatics (NII) on math and language models.
• LLM vs ITP paper accepted for the MATH-AI workshop at NeurIPS 2023.
• Media reports on my research. My paper on Mathematical Capabilities ChatGPT has been covered by Ars Technica and Synced Review.
• Reviewer for the ICLR 2024 conference.
• Reviewer for NeurIPS MATH-AI and SoLaR workshops.
• Oral at the LOD 2023 conference on the mathematics of Predictive Coding Networks.
• Poster at the Neuromonster 2023 conference.
• March 2023: Invited Talk at Tim Gowers’ group on Mathematical Capabilities of ChatGPT.
• New article on ChatGPT published (accepted for publication at NeurIPS 2023 Datasets & Benchmarks). Can everyone’s favorite language model really do mathematics? Can it show that $\int |fg| d \mu \leq \left\Vert g\right\Vert_1 \left\Vert f \right\Vert_\infty$? How about proving that $A+B$ is closed, if $A$ is a compact subset of a topological space and $B$ a closed one?
  Read the arXiv preprint to find out.
• Reviewer for NeurIPS 2023 conference (Main Track and Datasets and Benchmark Track).
• IJCAI ‘23. I’m serving as the IJCAI 2023 Assistant Workflow Chair.

Teaching:

• Supervisor for master thesis on language models: Starting with winter 2024, I’m supervising Tobias Mathis Gallus’ thesis on contamination within math dataset. Previously, I supervised Philip Vonderlind’s master thesis on language models and formal games, who graduated with distinction.
• Deep Learning in Healthcare course: I’m a demonstrator for the Deep Learning in Healthcare course, offered in Hilary term 2023 at my department at the University of Oxford.
• Various earlier courses in mathematics and machine learning with varying responsibilities (from 100% teaching to marking grades): Analysis/calculus, linear algebra for physicists, machine learning.

Reviewing:

I have served as a reviewer for all major Machine Learning Conferences (NeurIPS, ICML, ICLR, AAAI) and some journals (PNAS, Frontiers in Computer Science)

About:

My interests are divided between (in no particular order):

• merging mathematics and machine learning;
• pure mathematics;
• rigorous evaluations of language models;
• interactive/automatic theorem proving in natural language;
• theoretical neuroscience and predictive coding networks.