UK AI Hubs Launch National Challenge for Early Career Researchers

Timothy Abrahams on 3 June, 2026

The Erlangen AI Hub is taking part in an exciting new initiative, the AI Hubs National Public Engagement Challenge with Expressions of Interests needed this month (June). The competition offers PhD students and Postdoctoral Researchers within the UKRI AI community a chance to present their work in a compelling three-minute video designed for a public audience, and to showcase the depth and diversity of UK AI research on a national stage.

For early career researchers looking to strengthen their communication profile and raise the visibility of their work, this is an excellent opportunity. Participants will:

  • Build valuable public engagement skills essential for grant applications and careers.
  • Receive expert training on how to translate complex research into engaging stories.
  • Connect with peers across the UK AI Hubs, expanding their network.
  • Gain visibility across the UK AI ecosystem.
  • Compete for a spot in the national final, where the top five videos will be presented at the UKAIRS 2026 conference dinner in November, before an audience of policymakers, funders, industry leaders and researchers.
  • Potentially take home the title of Overall Winner.

The challenge is open to researchers connected to a UK AI Hub through any of the following:

  • Postdoctoral researchers employed on a UKRI AI Research Hub
  • PhD students within a UKRI AI CDT or another Doctoral Training Programme aligned to AI research
  • PhD students or PDRAs within the research group of an AI Hub Co Investigator, Partner or Member

If you are connected to a UK AI Hub, this is your chance to get involved. Eligible researchers must submit an Expression of Interest by 12 June 2026 including:

  • Name
  • Role: PhD Student or Postdoctoral Research Associate
  • Name of CDT or AI Research Hub
  • A 300-word description of their research for a general audience
  • A 150-word statement on why they want to take part

Submit your Expression of Interest by email by 12 June 2026 to info@aichemy.ac.uk with the subject line: ‘AI Hubs: National Public Engagement Challenge’

Shortlisting will take place on 24 June 2026, with 20–30 researchers selected to progress. Selected researchers will attend an online training session on presenting to public audiences and structuring an effective three-minute pitch. The session will also offer opportunities to practise with peers from other Hubs. Participants will then create their own three-minute video, using one slide (animation permitted). The final criteria for judging will be confirmed beforehand. A panel of external reviewers, including EPSRC Project Officers, will select the five finalists. All videos will be shared online to highlight the breadth of AI research across the UK.

The five finalists will be invited to attend UKAIRS 2026 (to be held at John McIntyre Conference Centre, Edinburgh above) on 24–25 November and their videos will be showcased at the conference dinner. A judging panel external to the Hubs covering policy and communication specialists will select the overall winner. If you are an early career researcher associated with a UK AI Hub, don’t miss this chance to share your work, develop your skills, and connect with the wider AI community.

Start preparing your Expression of Interest and bring your research to life in three minutes!

Final speakers announced for conference

Timothy Abrahams on 28 May, 2026

A finalised list of speakers for the Mathematical Foundations of AI Conference 2026 has almost been arrived at. The experts we have already secured stand at the intersection of mathematics, machine learning, artificial intelligence, geometry and topology. From pioneers of geometric deep learning and graph neural networks to leading researchers in topology, optimisation, formal proof verification, and relational AI, the conference will showcase cutting-edge ideas shaping the future of intelligent systems.

A list of the major speakers is included below. By the very nature of their wide-ranging contributions the following biographies have been edited for brevity.

Erik Bekkers
Erik Bekkers is an associate professor in Geometric Deep Learning in the Machine Learning Lab of the University of Amsterdam. His work emanates from the belief that as nearly all data is rooted in our physical world it is thus inherently grounded in geometry and physics and representation learning should preserve this grounding. His current research focuses on developing generalizations and efficient implementations of group equivariant architectures. 

Michael Bronstein
The Erlangen AI Hub founder and co-director is a pioneer of geometric deep learning and graph neural networks. In addition, Michael Bronstein is DeepMind Professor of AI at the University of Oxford, Erlangen AI Hub Director and Founding Scientific Director of AI at AITHYRA. His work bridges academia and industry, shaping the future of non-Euclidean machine learning and AI innovation.

Rebekka Burkholz
Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security, researching relational machine learning. Her main goal is to gain a theoretical understanding of deep learning from a complex network perspective and improve contemporary algorithms based on these insights. Her current applications focus on molecular biology. Before joining CISPA in 2021, she worked at the Biostatistics Department of the Harvard TH Chan School of Public Health.

Kevin Buzzard
Professor of Pure Mathematics at Imperial, Kevin Buzzard won the London Mathematical Society’s Whitehead Prize in 2002 and the Senior Berwick Prize in 2008. In 2017 he started formalizing mathematics and in 2022 he gave a plenary lecture at the International Congress of Mathematicians on the topic. He is currently leading a project to formalize a proof of Fermat’s Last Theorem and will speak at the conference on what autoformalization means with regard to artificial intelligence.

Coralia Cartis
Coralia Cartis is Professor of Numerical Optimization, University of Oxford, an internationally recognised mathematician whose research focuses on the theory, complexity and implementation of optimisation algorithms central to modern machine learning. A SIAM and EUROPT Fellow, she has made influential contributions to non-convex optimisation, derivative-free methods and large-scale computational mathematics. Cartis’s research bridges optimisation theory and AI applications, with expertise in numerical optimisation and machine learning algorithms.

Peter Grindrod
Peter Grindrod CBE is Professor of Mathematics at the University of Oxford and a leading applied mathematician whose work has shaped the development of mathematical approaches to data science and artificial intelligence in the United Kingdom. A founding director of the Alan Turing Institute and co-investigator of the Erlangen AI Hub, he has championed the view that future advances in AI depend on deeper mathematical understanding rather than scale alone. His research connects theoretical mathematics with industrial and societal applications

Heather Harrington
Professor of Mathematics, University of Oxford and Director of the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Heather Harrington is an applied mathematician whose research sits at the intersection of topology, algebra, statistics, and machine learning. She is also Co-Director of the Centre for Topological Data Analysis and has pioneered mathematical methods for extracting structure from complex, high-dimensional data. A Royal Society Research Fellow and recipient of major awards including the Whitehead and Adams Prizes.

Kathryn Hess
Kathryn Hess is a leading mathematician known for her work in homotopy theory, category theory, and algebraic topology, as well as for applying topology to neuroscience, cancer biology, and materials science. Professor at École Polytechnique Fédérale de Lausanne, she is also a member of the Swiss Academy of Engineering Sciences, a Fellow of the American Mathematical Society and the Association for Women in Mathematics.

Stefanie Jegelka
Humboldt Professor at TU Munich and a Visiting Associate Professor at MIT EECS , Stefanie Jagielka’s research investigates the combinatorial, geometric, and algebraic foundations of machine learning. This includes learning with discrete objects, such as graphs or sets; learning with symmetries; discrete probability; learning with limited supervision, and the interplay of discrete and continuous optimization. She was a postdoc at UC Berkeley and obtained her PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems.

Roland Kwitt
Professor of Machine Learning within the Department of Artificial Intelligence and Human Interfaces (AIHI) at the University of Salzburg (PLUS), Roland Kwitt is also currently deputy head there. Prior to that, he was part of the medical imaging and computer vision group at Kitware Inc., North Carolina, USA. His research spans multiple areas, but mostly focusses on theoretical and practical aspects of learning methods that allow the leverage and control structural characteristics of data. He is also a member of the ELLIS society.

Yue Ren
Associate Professor, Durham University and UKRI Future Leaders Fellow, Yue Ren is a mathematician whose research connects algebraic geometry, tropical geometry and machine learning. He is a leading developer of mathematical software systems including Polymake, Singular, and OSCAR, and explores how geometric and algebraic structures can illuminate the behaviour of neural networks and learning algorithms. His work brings rigorous mathematical tools to problems in AI, polynomial system solving and scientific computing, with expertise in tropical geometry and geometric machine learning.

Suvrit Sra
Suvrit Sra is Career Development Associate Professor of EECS MIT and Professor for Resource Aware Machine Learning at TU Munich. A main component of his research on the mathematics of AI is optimization for machine learning, especially non-convex optimization including non-Euclidean and geometric optimization. Other key topics of interest include: discrete probability, theory of deep learning, theory of sampling, convex geometry, polynomials, combinatorics, etc.

Marika Taylor
Marika Taylor is Head of the College of Engineering and Physical Sciences and Pro Vice Chanceller at the University of Birmingham. She is also Professor of Theoretical Physics whose recent work extends into the mathematical foundations of artificial intelligence. Originally renowned for contributions to string theory, holography and geometry, she has increasingly focused on geometric AI and mathematically principled approaches to machine learning. As a Fellow of the Alan Turing Institute, she promotes interdisciplinary research linking advanced mathematics, physics and data science.

Register now to reserve your place at the conference and follow us for further speaker announcements over the coming weeks.

Erlangen AI Hub Industry Day: Connecting Academia and Industry

erlangenaihub on 28 April, 2026

On Tuesday 21 April, the Erlangen AI Hub hosted an Industry Collaboration Day at the Department of Computer Science, University of Oxford. The event brought together academics from across the Hub network alongside leading industry partners, creating a valuable space for discussion, knowledge exchange and future collaboration.

The day opened with a welcome from Jeff Giansiracusa, Professor of Mathematics at Durham University. We were joined by an outstanding group of industry speakers, including Dr Carl Hunter (Durham Institute of Research, Development and Invention), Andreas Haggman (Ofcom), Danica Greetham (Capgemini Engineering), Francis Bursa (Oxford Nanopore Technologies) and Marco Albanese (Oxford Drug Design).

Academic perspectives were provided by Tom Coates, Professor of Mathematics at Imperial College London, and Pete Grindrod, Professor of Mathematics, University of Oxford.

The morning sessions highlighted the breadth of challenges and opportunities at the intersection of AI and industry. Topics ranged from online safety and the societal risks of AI, to the importance of sovereign AI capabilities in the UK and the role of collaboration between government, science and industry. Speakers also explored how AI is being applied to analyse complex data, identify patterns and accelerate progress in science and engineering.

Discussions continued over lunch, with participants exchanging ideas on future collaborations and exploring how partnerships between academia and industry can drive innovation and real-world impact.

In the afternoon, attendees worked in groups to develop ideas inspired by the morning sessions. These discussions generated a number of thought-provoking themes, including:

  • The evolving relationship between AI and philosophy, and whether the field is returning to its conceptual roots
  • Challenges in modelling complex, noisy and temporal data, and improving interpretability
  • The economics of benchmarking AI models and the risks of overfitting to established benchmarks
  • The role of domain expertise in mitigating risks for less-informed users of AI systems
  • Advances in machine learning for recognising complex patterns and improving sequencing accuracy

The day concluded with a panel discussion featuring Marika Taylor (University of Southampton), Ran Levi (University of Aberdeen) and Yue Ren (Durham University), who reflected on the future of AI and industry. The panel explored emerging challenges, key research questions, and how collaboration can deepen understanding of both mathematics and AI, while shaping their applications in industry.

The event received excellent feedback from participants. As Danica Greetham (Capgemini Engineering) noted:

“What stood out was the quality of the discussion afterwards—sharp questions and open exchanges with academics keen to dive quickly into the essence of problems. It was great to hear from other industrial partners about the challenges they’re tackling. A recurring theme: observational data doesn’t lie—but it rarely speaks plainly. Interpretation is where the real work happens.”

We look forward to our next major event where we can continue the conversations, Mathematical Foundations of AI: The Erlangen Hub Conference 2026, taking place from 1–3 September 2026 at the Mathematical Institute, University of Oxford. Early bird tickets are now available.

Meet the team Q&A: Marika Taylor

erlangenaihub on 27 March, 2026

In this Erlangen Hub Q&A we spoke to Marika Taylor, Co-Investigator and Theme C Deputy Lead. Marika is a Professor of Mathematics, Physics and AI at the University of Southampton. She trained in theoretical physics under Stephen Hawking, and is currently interested in geometric ML for fundamental physics applications and physics-inspired methods for ML. Marika was a Turing Institute Fellow and recipient of the “Dutch ERC” Vidi. She has a long track record with start-ups, including in encryption and fintech.  

Can you share a bit about your background and your current research focus?

My main research background is in mathematical and theoretical physics – particularly string theory, quantum theory and gravity. In parallel with this work in fundamental science, I have always been involved in mathematical modeling for real world problems, particularly finance, and have used neural nets for many years in that context. In recent years I’ve seen a convergence between my fundamental science research and my applied work: concepts from my areas of physics are being used within AI, while AI is increasingly being applied within fundamental physics too. A nice example would be graph neural networks in non-Euclidean geometry. Non-Euclidean geometry underpins our understanding of Einstein’s theory of general relativity (gravity). Many of the physical insights obtained from studying particularly geometries for gravity lead to insights into GNNs embedded into such geometries. Another example would be around symmetries. Physicists always build in their understanding of the underlying symmetries (exact or approximate) into their modelling of a system. One can similarly build symmetry equivariance into neural networks – for example, if you are classifying images of 3d objects and are agnostic about the orientation of the objects, then a network with rotational equivariance built into it will be more efficient in classification. My group is currently exploring more general symmetry equivariant networks, drawing from physics insights; this enables us to reduce substantially the number of parameters that need to be learned, and also to understand conceptually patterns found in previous algorithms. We are also interested in using physics understanding of time dependent systems to develop spiking neural networks; the latter are nature inspired, in that neurons only fire when a threshold is met, making them much more energy efficient.

What inspired you to pursue this area?

Throughout my career I have always worked on the frontiers of fundamental science. String theory is a “theory of everything”. It uses concepts from right across mathematics and also leads to new insights and ideas in mathematics – topological quantum field theory, for which Ed Witten won the Fields Medal, is a notable example. In parallel I’ve always enjoyed using the breadth of my knowledge in mathematical sciences for real world applications and I’ve often found that I get new insights into fundamental science from the applied work I’ve done. Over the last few years I’ve gradually moved more and more into AI, for both fundamental science and applied work, because there are so many exciting developments.

Which themes are you connected to within the Erlangen AI Hub and how does your work within the hub intersect with your research background?

The main theme that I am connected with is “Understanding Learning”, but I link with all the themes. Much of what I do relates to understanding conceptually hidden structures in data, and how geometry and topology can be used to characterize these. My physics insights into geometry also facilitate relating geometric and topological insights to real world phenomena.

What attracted you to the Erlangen AI Hub and what do you hope to see it achieve?

The hub is exploring the mathematical foundations of intelligence – this is essential to develop better, more efficient and safer models, which in turn will allow us to use AI in more contexts.

What’s been the most surprising or exciting finding in your work so far?

I don’t think that I would have predicted ten years ago that my two parallel streams of research would become so closely connected, with physics giving insights into developing AI and AI started to be used more in physics. (For AI to really be adopted more widely in physics, we will need robustness and accuracy.)

What challenges have you faced in your research, and how did you overcome them?

I like to take on research problems that are quite open ended and conceptually challenging. Inevitably that means that at times I get stuck or can’t quite see where to go next! Then I take a break, think about something else, and also talk to others, to get new ideas on where to go next.

What advice would you give to someone just starting out in your field?

I would advise somebody to follow their interests, and see where these take them!

Meet the team Q&A: Yue Ren

Kevin Childs on 16 February, 2026

In this Erlangen Hub Q&A we spoke to Yue Ren, Co-Investigator and Theme B lead at Durham University. Yue is a UKRI Future Leaders Fellow and leading expert in tropical geometry, mathematical software, and the application of both to neural networks and problems in industry and sciences. He is a core developer of the computer algebra systems Polymake, Singular, and OSCAR. 

What is your name?

Yue Ren

Can you share a bit about your background and your current research focus?

My background is in algebraic and tropical geometry.  I did my PhD in Germany, and spent some time in the US, South Africa, Israel, and Sweden before moving to the UK. My current focus is on applications of the latter to polynomial system solving and machine learning.

What inspired you to pursue this area?

Mathematically, I’ve always been fascinated by the concrete interplay between algebra, geometry, and combinatorics in tropical geometry.  However, I was always prone to making mistakes in hand calculations, so I decided to specialize in teaching a computer to do them for me instead.  Professionally, I wanted a path that combined my mathematical interests with practical skills like software development.

Which themes are you connected to within the Erlangen AI Hub and how does your work within the hub intersect with your research background?

I am mainly connected to Theme B, though my research touches upon other themes as well.  My work within the hub revolves around taking theoretical techniques from pure mathematics and turning them into practical algorithms. It’s a way to expand the machine learning toolbox with some interesting new tools.

What attracted you to the Erlangen AI Hub and what do you hope to see it achieve?

The Erlangen AI Hub brings together researchers from a wide range of backgrounds who are all pursuing a common goal. I’m really looking forward to the mathematical theories and practical tools that will come out of this unique mix of expertise.

What’s been the most surprising or exciting finding in your work so far?

I’ve found it really surprising that p-adic numbers, an abstract number system developed by pure mathematicians for number theory, can be so useful for data analysis. Their distance prioritizes structural relationships over physical proximity, which is perfect for hierarchical data.

What challenges have you faced in your research, and how did you overcome them?

Most researchers face the same problems: getting stuck on a proof, getting unstuck only to realize you’ve built a suspiciously complicated proof for a simple statement, and Reviewer 2.  Research is an endless chain of challenges, and the best strategy for overcoming them is to ask for advice, ask for feedback, and act on it.  Not only are advice and feedback valuable, but asking for and acting on them is also a valuable skill that needs to be trained.

What advice would you give to someone just starting out in your field?

Don’t just focus on doing things, reflect on how you do them. Finding better work habits will help you spend more time on the things you enjoy and less time on the things you don’t.

What’s something people might be surprised to learn about you outside of research?

I’ve participated in the Cape Argus Cycle Tour, a 110 km race around the Cape of Good Hope with 35k entrants. I finished in the middle of my age group while taking 300 pictures along the way. I was beaten by a group dressed up as the Power Rangers under the ruthless South African sun, but at least I managed to beat that one bloke who rode a unicycle.

Mathematics and the Future of AI

Kevin Childs on 12 February, 2026

Intellectual leadership through deep foundations

In a new University of Oxford Expert Comment article, Erlangen AI Hub Co-Investigator Professor Peter Grindrod CBE, argues that mathematics is not peripheral to artificial intelligence, but central to solving its core challenges.

As AI systems increase in scale and complexity, concerns around reliability, bias, interpretability, and formal guarantees cannot be addressed by engineering alone. Mathematics provides the structure to reason rigorously about uncertainty, optimisation, stability, and limits. Through probability, geometry, topology, dynamical systems, and information theory, maths enables AI systems that are interpretable by design and grounded in provable principles.

At the Erlangen AI Hub, Professor Grindrod and colleagues are working precisely in this space: bringing deep mathematical ideas into direct engagement with real-world AI challenges. Maths provides the foundation to build systems that are more robust, transparent, and intellectually grounded, helping position the UK as a leader through intellectual depth rather than scale. Read the full article below.

Expert Comment: How and why mathematics will both underpin and lead the next generation of AI | University of Oxford

Erlangen Hub Co-Investigators elected inaugural Fellows of the Academy for the Mathematical Sciences

Kevin Childs on 29 January, 2026

Erlangen Hub Co-Investigators Rama Cont and Christoph Reisinger have been elected to the inaugural cohort of Fellows of the newly founded Academy for the Mathematical Sciences. Their election recognises their leadership and contributions to the mathematical sciences and marks a significant milestone for both individuals and the Hub.

The Academy for the Mathematical Sciences brings mathematics to the centre of UK research, policy, and public life, advancing the discipline across multiple domains, including policy, education, research, and innovation. Sitting alongside other national academies, including the British Academy and the Academy of Medical Sciences, it will use its convening power to bring together experts to collaborate on major global challenges. These include climate change, national security, financial systems, and artificial intelligence.

The Fellowship comprises leading mathematicians from across academia, education, industry, business, and government, and includes Fields Medallists, senior figures in national security, and pioneers in computing and AI. The election of Rama and Christoph recognises their achievements and expertise within this distinguished community.

Rama Cont is Professor of Mathematics at the University of Oxford’s Mathematical Institute, Head of the Mathematical and Computational Finance Group, and a Fellow of St Hugh’s College. He also directs the Centre for Doctoral Training in Mathematics of Random Systems. His research spans stochastic computational methods, the mathematical foundations of AI, generative models, and data-driven modelling in finance. Alongside his academic work, Rama advises several AI-focused start-ups, including InstaDeep, 73Strings, and Synthera.AI. Through the Erlangen Hub, he contributes to strengthening the mathematical foundations that underpin modern AI systems and their applications. On knowledge of the election Rama said:

The Academy for the Mathematical Sciences’ ambitions are to represent and promote the full spectrum of mathematical sciences and their applications. As a mathematician with research activities spanning theory and applications, I am delighted to join the Academy as a Fellow.’

Christoph Reisinger is Professor of Applied Mathematics at the University of Oxford and specialises in stochastic simulation and control, mean-field models, and the mathematical foundations of deep learning. He collaborates closely with industry and government partners on challenges in AI security, air traffic control, and financial market microstructure. Within the Erlangen Hub, Christoph advances fundamental research at the interface of control theory and reinforcement learning, supporting the development of robust and reliable AI decision-making.

Hub PDRA Thom Badings receives AAAI doctoral award honourable mention

Kevin Childs on 27 January, 2026

Erlangen Hub researcher Thom Badings has received an honourable mention in the AAAI and ACM SIGAI Doctoral Dissertation Award; a prestigious international award recognising outstanding PhD research in artificial intelligence. As part of this recognition, Thom was invited to attend AAAI 2026 in Singapore, where he received the award and delivered an award talk on his doctoral research.

The AAAI and ACM SIGAI Doctoral Dissertation Award is jointly presented by the Association for the Advancement of Artificial Intelligence, and is regarded as one of the most significant distinctions for early-career researchers in the field. Honourable mentions are awarded to dissertations that demonstrate exceptional originality, technical depth, and potential impact.

In addition to the award presentation, Thom and Hub PDRA Francesco Fabiano also presented their joint research paper on robust decision-making, developed in collaboration with Co-investigators Alessandro Abate and Giuseppe De Giacomo.

Thom will be leaving the Erlangen AI Hub in March. His recognition at AAAI 2026 reflects both the strength of his individual research contributions and the broader impact of the Erlangen Hub’s work in artificial intelligence.

Over 20 Hub papers accepted at ICLR 2026

Kevin Childs on 27 January, 2026

The Erlangen Hub has achieved a significant international research milestone, with over 20 papers accepted at ICLR 2026, one of the world’s leading conferences in artificial intelligence and machine learning.

The International Conference on Learning Representations, known as ICLR, is a premier global venue for research in areas such as deep learning, reinforcement learning, and the theoretical foundations of modern AI, and will be held in Rio de Janeiro, Brazil, from Thursday 23 April to Monday 27 April.

ICLR 2026 had over 19,000 paper submissions from researchers worldwide, with an acceptance rate of only around 30 percent. For Erlangen, securing over 20 papers in a single year is an excellent outcome. This success ensures the Hub remains a productive contributor to the conference internationally and the wider AI research conversation.

The accepted papers are diverse. They span a wide range of topics at the forefront of AI research, reflecting both the breadth and depth of expertise within the Hub. They include work on reinforcement learning, causal inference, diffusion models, and the theoretical analysis of machine learning systems, alongside several high-profile collaborative projects.

Hub Director Michael Bronstein and colleagues contributed an exceptional 17 papers.

Other contributors include Ran Levi, whose collaborative project paper develops new topological neural network models for learning from complex, higher-order relational data. Alessandro Abate also co-authored an accepted paper with L. Carvalho Melo and Yarin Gal, on challenges in reinforcement learning for large language model reasoning.

The Erlangen Hub is further represented in foundational work on causality and learning, with Marta Kwiatkowska co-authoring an accepted paper on causal imitation learning in the presence of hidden confounders, while Patrick Rebeschini co-authored a paper offering new theoretical insights into diffusion models, an increasingly important class of generative models in modern AI.

In other conference news, Hub PDRAs Francesco Fabiano and Thom Badings presented the paper “Best-Effort Policies for Robust Markov Decision Processes”, a collaboration with Co-Investigators Alessandro Abate and Giuseppe De Giacomo, at the AAAI 2026 conference in Singapore. Thom also received an honourable mention in the AAAI and ACM SIGAI Doctoral Dissertation Award and delivered his own talk at AAAI 2026. Hub Co-I Gesine Reinert has contributed two papers this year to the AIStats conference, taking place later this year in Morocco.

Taken together, these achievements highlight the Erlangen Hub’s growing international profile and its impact across the most active and influential areas of artificial intelligence research. They reflect both individual research excellence and a strong culture of collaboration and high-quality scholarship within the Hub.

Meet the Team Q&A: Tom Coates

Kevin Childs on 22 January, 2026

I was blown away by how powerful AI tools can be for theorem discovery. It’s impossible to spend a lot of time using these tools, and their more powerful LLM cousins, without becoming intensely curious about how they work and how to reason about them.

In this edition of our Meet the Team Q&A we sat down with Tom Coates. Tom is a Professor of Pure Mathematics at Imperial College London. He is also an Erlangen Hub Co-Investigator and Theme D Deputy Lead focusing on the development of autonomous, self-adaptive AI that uses formal methods to understand its limits, and act safely and ethically.

Can you share a bit about your background and your current research focus?

For the past decade or more, I have been involved in a large-scale collaboration to find and classify algebraic varieties called Fano manifolds — one can think of this as building a “Periodic Table” for shapes. This collaboration involves a lot of large-scale computation, data mining, and machine learning, which is how I got interested in AI as a tool for scientific discovery. More recently I have also been on part-time secondment to the Office of the Chief Scientific Adviser, which has led to my current focus on the interface between AI and policy.

What inspired you to pursue this area?

Originally I was attracted by a purely theoretical, purely mathematical question: how to find the “basic pieces” from which more complex geometries are made. But as we began to deploy machine learning and AI-powered pipelines to tackle this question, I was blown away by how powerful AI tools can be for theorem discovery. And it is not possible to spend a lot of time using these tools, and their more powerful LLM cousins, without becoming intensely curious about how they work and how to reason about them.

Which themes are you connected to within the Erlangen AI Hub and how does your work within the hub intersect with your research background?

I lead on the Government Engagement and Theme D.

What attracted you to the Erlangen AI Hub and what do you hope to see it achieve?

There is such a need for more mathematicians with the skills to work at the interface between AI and policy. The Erlangen Hub is a perfect opportunity to pilot interventions in this space, and to support and grow more early career researchers who are “bilingual” — communicating fluently with both policymakers and scientists in AI and adjacent fields.

What’s been the most surprising or exciting finding in your work so far?

I’ve been enormously proud of the work that Sara Veneziale and I have done developing and delivering training courses on the fundamentals of AI to civil servants. More than 250 civil servants in at least 15 departments to date! This is having a demonstrable impact on policy formulation and delivery, in an area that is critical to both security and economic growth.

What challenges have you faced in your research, and how did you overcome them?

I have been very fortunate to work with an incredible group of collaborators across my career. There have been challenges here and there, but also amazingly creative colleagues to help overcome them.

What advice would you give to someone just starting out in your field?

Get stuck into the practicalities of AI: training large models, fighting the data-cleaning pipeline, wrangling the GPUs. There is no substitute for practical experience — and it helps you to choose and formulate the correct research questions too.