The AIMO Prize Manager and Advisory Committee

XTX Markets are pleased to announce the appointment of Simon Frieder to the role of AIMO Prize Manager.

Simon Frieder

Frieder is mathematician and computer scientist, investigating how mathematics can be automated using deep learning techniques. His doctoral thesis was written at the University of Oxford. He has two separate degrees in mathematics and computer science, and his research has been featured in popular science news outlets such as Ars Technica, ZDNet, and mentioned in AI-related reports of the German government. He has published at top conferences in machine learning, such as NeurIPS, ICML, and ICLR.

The Advisory Committee

XTX Markets is delighted to be joined by a group of prominent mathematicians, and AI and machine learning specialists, forming the AIMO Prize Advisory Committee.

Timothy Gowers

After winning a gold medal in the IMO in 1981, Timothy studied mathematics at Trinity College, Cambridge, where he did his PhD and was then a research fellow. After a period at University College London, he returned to Trinity, first as a lecturer and then as the Rouse Ball Professor of Mathematics. From 2009-2020 he was a Royal Society Research Professor, and since October 2020 has been Professor of Combinatorics at the Collège de France.

He discovered the first quantitative proof of Szemerédi's theorem and has subsequently worked in additive combinatorics, for which he was awarded a Fields Medal in 1998. In recent years, he has worked on automatic theorem proving and currently heads a research group in that field, concentrating on symbolic methods.

Po-Shen Loh

Po-Shen is a mathematics professor at Carnegie Mellon University, and a social entrepreneur who uses combinatorics and game theory to invent solutions ranging from education to pandemic control. He was an IMO silver medallist in his youth and then served a decade-long term as the Coach of the USA IMO Team, during which the team ranked #1 in the world four times. His lectures and events take him all over the world, reaching over 10,000 people in person and millions on YouTube each year. He received the United States Presidential Early Career Award for Scientists and Engineers, and is a Hertz Fellow.

Dan Roberts

Dan is an AI Fellow at Sequoia Capital and a researcher at MIT. Prior to joining Sequoia in 2023, he co-founded Diffeo, an AI company acquired by Salesforce, and was a research scientist at Facebook AI Research. As an AI researcher, he co-authored the book "The Principles of Deep Learning Theory," published by Cambridge University Press. He was a postdoc at the Institute for Advanced Study at Princeton, completed a PhD in theoretical physics from MIT funded by a Hertz Fellowship, and studied in the UK as a Marshall Scholar.

Geoff Smith

Geoff Smith MBE is an activist in the mathematics olympiad community and has been involved in mathematics enrichment since 1990 and mathematics competitions since 1999. He was the elected President of the IMO between 2014 and 2022. He is the Chair of the United Kingdom Mathematics Trust and he was instrumental in setting up the European Girls’ Mathematical Olympiad in 2012, an event which now prospers under female governance.

His academic career started in group theory, but later widened to include geometry and contributions to papers in the life sciences and social science. He is also an honorary reader in mathematics at the University of Bath.

Terence Tao

Terence Tao was born in Adelaide, Australia in 1975. He participated in three IMOs, culminating in a gold medal in 1987. He is a professor of mathematics at UCLA, having completed his PhD under Elias Stein at Princeton in 1996. His areas of research include harmonic analysis, PDE, combinatorics, and number theory.

He has received a number of awards, including the Salem Prize in 2000, the Fields Medal in 2006, the MacArthur Fellowship in 2007, the Crafoord prize in 2012 and the Breakthrough Prize in Mathematics in 2015. Terence also holds the James and Carol Collins chair in mathematics at UCLA, is a fellow of several national academies and also serves on the President's Council of Advisors on Science and Technology.

D. Sculley

D. is the CEO at Kaggle. Prior to joining Kaggle, he was a director at Google Brain, leading research teams working on robust, responsible, reliable and efficient ML and AI. In his career in ML, he has worked on nearly every aspect of machine learning, and has led both product and research teams including those on some of the most challenging business problems.

Some of his well-known work involves ML technical debt, ML education, ML robustness, production-critical ML, and ML for scientific applications such as protein design.

Kevin Buzzard

Kevin a professor of pure mathematics at Imperial College London, specialising in algebraic number theory. As well as his research and teaching, he has a wide range of interests, including being Deputy Head of Pure Mathematics, Co-Director of a CDT and the department's outreach champion. He is currently focusing on formal proof verification, including being an active participant in the Lean community. From October 2024, he will be leading a project to formalise a 21st century proof of Fermat's Last Theorem.

Before joining Imperial, some 20 years ago, he was a Junior Research Fellow at the University of Cambridge, where he had previously been named 'Senior Wrangler' (the highest scoring undergraduate mathematician). He was also a participant in the International Mathematical Olympiad, winning gold with a perfect score in 1987. He has been a visitor at the IAS in Princeton, a visiting lecturer at Harvard, has won several prizes both for research and teaching, and has given lectures all over the world.

Leo de Moura

Leo is a Senior Principal Applied Scientist in the Automated Reasoning Group at AWS. In his spare time, he dedicates himself to serving as the Chief Architect of the Lean FRO, a non-profit organization that he proudly co-founded alongside Sebastian Ullrich. He is also honoured to hold a position on the Board of Directors at the Lean FRO, where he actively contributes to its growth and development. Before joining AWS in 2023, he was a Senior Principal Researcher in the RiSE group at Microsoft Research, where he worked for 17 years starting in 2006. Prior to that, he worked as a Computer Scientist at SRI International. His research areas are automated reasoning, theorem proving, decision procedures, SAT and SMT.

He is the main architect of several automated reasoning tools: Lean, Z3, Yices 1.0 and SAL. Leo's work in automated reasoning has been acknowledged with a series of prestigious awards, including the CAV, Haifa, and Herbrand awards, as well as the Programming Languages Software Award by the ACM. Leo's work has also been reported in the New York Times and many popular science magazines such as Wired, Quanta, and Nature News.

Lester Mackey

Lester Mackey is a Principal Researcher at Microsoft Research, where he develops machine learning methods, models, and theory for large-scale learning tasks driven by applications from climate forecasting, healthcare, and the social good. Lester moved to Microsoft from Stanford University, where he was an assistant professor of Statistics and, by courtesy, of Computer Science. He earned his PhD in Computer Science and MA in Statistics from UC Berkeley and his BSE in Computer Science from Princeton University.

He co-organized the second place team in the Netflix Prize competition for collaborative filtering; won the Prize4Life ALS disease progression prediction challenge; won prizes for temperature and precipitation forecasting in the yearlong real-time Subseasonal Climate Forecast Rodeo; and received best paper, outstanding paper, and best student paper awards from the ACM Conference on Programming Language Design and Implementation, the Conference on Neural Information Processing Systems, and the International Conference on Machine Learning. He is a 2023 MacArthur Fellow, a Fellow of the Institute of Mathematical Statistics, an elected member of the COPSS Leadership Academy, and the recipient of the 2023 Ethel Newbold Prize.

Peter J. Liu

Peter J. Liu is a Research Scientist at Google DeepMind in the San Francisco Bay area, doing machine learning research with a specialisation in language models since 2015 starting in the Google Brain team. He has published and served as area chair in top machine learning and NLP conferences such as ICLR, ICML, NEURIPS, ACL and EMNLP.

He also has extensive production experience, including launching the first deep learning model for Gmail Anti-Spam, and using neural network models to detect financial fraud for top banks. He has degrees in Mathematics and Computer Science from the University of Toronto.