Predictive Modelling » Seminars

18/05 1pm-2pm: WCPM, Paddy Royall, Warwick

James Kermode — Thu, 14 May 2026 12:59:23 GMT

When: 1pm - 2pm, Mon, 18 May '26

Where: L5, Science Concourse

Networking Lunch: Outside L5, from 12:30pm - 1pm.

Title: Predicting Nucleation: is it the end of the road for the second-largest discrepancy in physics?

Abstract (with images):

Crystallisation is among the most familiar physical phenomena of everyday life. One would quite reasonably imagine that it has been long understood. And yet there are many basic unanswered questions. Here we will tackle two. Firstly, we enquire how quickly materials crystallise. Secondly we will consider which crystal lattice structure they form. Prediction of nucleation rates in familiar materials such as water remains very challenging. In such general problems, model systems play a crucial role, as a benchmark of theory and computer simulation. Alas, the most studied material, colloidal hard spheres, exhibits a wild disagreement between experiment and numerical prediction of 20 orders of magnitude, which has been dubbed “the second-largest discrepancy in physics” [1].

Since the experimental work relevant to this discrepancy was carried out in the last millennium, we expect that developments, such as particle-resolved studies may shed light on this phenomenon. Such experiments are amenable to analysis with higher—order structural measures. This allows us to directly investigate whether Sir Charles Frank’s conjecture that “five-fold symmetry is abhorrent to crystallisation”. It is [2]. We then investigate whether fivefold symmetry might explain the hard sphere nucleation discrepancy. It doesn’t [1]. But with careful matching of state point between experiments and simulation, we do find agreement for the energy barriers to form pre-critical nuclei [3].

The second problem, of which crystal lattice (polymorph) a system chooses has important real-world consequences. For example, following clinical trials and release onto the market of the anti-AIDS drug Ritonavir, an unknown polymorph formed, which was not effective, leading to withdrawal of the drug from the market. We have deduced the mechanism of polymorph selection in simple model systems [4,5]. We then show how our method can be used to understand polymorph selection in more complex systems with many competing polymorphs [6,7] and 3d active colloids [8,9].

[1] Royall CP et al, Rev. Mod. Phys. 96 045003 (2024).

[2] Taffs J. and Royall CP, Nature Commun. 7 13225 (2016).

[3] Kürten L, Castagnède A, Smallenburg F, Royall CP, Science Advances 12, eaec8906 .

[4] Gispen W, Coli GM.,van Damme R, Royall CP and Dijkstra M, ACS Nano 17 8807–8814 (2023).

[5] Royall CP, J. Chem. Phys. 164 024907 (2026).

[6] Skipper K, Moore FJ and Royall CP, J. Chem. Phys. 161 144308 (2024).

[7] Wu X, Skipper K, Yang Y, Moore FJ, Meldrum FC, and Royall CP, Soft Matter 21 2787-2802 (2025).

[8] Sakaï N, Skipper K, Moore FJ, Russo J and Royall CP, Soft Matter, 21 5204-5213 (2025).

[9] Chao X, Skipper K, Royall CP, Henkes S, Liverpool TB, Phys. Rev. Lett. 134 018302 (2025).

Bio:Professor Paddy Royall is a leading researcher in soft condensed matter physics and a CNRS Director of Research at ESPCI Paris, and is currently a Leverhulme Visiting Professor at the University of Warwick. His work focuses on understanding the microscopic origins of complex material behaviour, particularly in areas such as the glass transition, crystallisation, and amorphous materials, combining advanced experiments with computational approaches.

01/06 1pm-2pm: WCPM, Thomasina Ball, Warwick

James Kermode — Tue, 05 May 2026 11:27:26 GMT

When: 1pm - 2pm, Mon, 01 Jun '26

Where: L5, Science Concourse

Networking Lunch: Outside L5, from 12:30pm - 1pm.

Title: Modelling mountain building: Wrinkles and creases in thin layers of viscoplastic fluid

Abstract: Wrinkles or creases in the surface of a material are indicative of compression. For example, on Earth, mountain ranges formed due to the plate tectonics exhibit regular spaced folds on the surface. In this talk I will discuss some of the theoretical and experimental approaches we have taken to describe the process of mountain building by modelling it as a viscoplastic fluid (a material with solid and fluid-like properties).

Bio: Research Interests:

Thomasina's research interests lie in mathematical modelling of fluid dynamical phenomena from observations of laboratory experiments and the natural world around us. In particular she is interested in the areas of: non-Newtonian rheologies, yield stress fluids, gravity-driven flow, geophysical flows, instabilities that arise from rheology contrasts, fluid-structure interactions.

Most relevant recent publications:

Ball, T. V. & Balmforth, N. J. (2025) Non-axisymmetric patterns in floating viscoplastic films. J. Fluid Mech. 1007.

Ribinskas, E., Ball, T. V., Penney, C. E., & Neufeld, J. A. (2024) Scraping of a viscoplastic fluid to model mountain building. J. Fluid Mech.

See her Publications page for a full list with preprints: https://warwick.ac.uk/fac/sci/maths/people/staff/tball/

08/06 1pm-2pm: WCPM, Kevin Huang, Warwick

James Kermode — Mon, 20 Apr 2026 08:58:28 GMT

When: 1pm - 2pm, Mon, 08 Jun '26

Networking Lunch: Outside L5, from 12:30pm - 1pm.

Title: TBC

Abstract: TBC

Bio: Kevin is a postdoctoral research fellow funded by the Engineering and Physical Sciences Research Council (EPSRC) through the ProbAI Hub. They are currently based at the University of Warwick, working with Gareth Roberts, and collaborate with Boris Hanin at Princeton University.

They completed a PhD in machine learning at the Gatsby Computational Neuroscience Unit, University College London, under the supervision of Peter Orbanz and Morgane Austern. During this time, they were also a visiting researcher with the LIPS group at Princeton Computer Science, hosted by Ryan P. Adams. Prior to this, they completed both their undergraduate and master’s degrees in mathematics at the University of Cambridge.

Their research lies at the intersection of machine learning theory, probability, and statistics. They study the emergence of universal structures in large-scale stochastic systems, drawing on tools from random matrix theory, high-dimensional statistics, symmetry-based inference, and stochastic optimisation. Alongside this theoretical work, they increasingly engage with applied challenges, particularly around scaling laws in neural networks, AI for scientific discovery, and the robustness and safety of machine learning models.

For the 2025–2026 academic year, he is co-organising the ProbAI online seminar series and will lead the ProbAI Theory of Scaling Laws Workshop at Warwick in summer 2026.

15/06 1pm-2pm: WCPM, Sam Livingstone, UCL

James Kermode — Mon, 20 Apr 2026 09:02:17 GMT

When: 1pm - 2pm, Mon, 15 Jun '26

Networking Lunch: Outside L5, from 12:30pm - 1pm.

Title: TBC

Abstract: TBC

Bio: Sam is an associate professor at University College London (UCL). In his research he uses tools from probability and mathematical analysis to study algorithms in Statistics and Machine Learning. The field is often called CSML. He can also do some more methodological work in probabilistic modelling, particularly for health applications (see his research page for more).https://samueljlivingstone.wixsite.com/webpage

He currently serves as associate editor for the journal Biometrika. Until Oct 2024 he held an EPSRC New Investigator Award entitled 'Robust and scalable Markov chain Monte Carlo for heterogeneous models'. In 2021he became the first UK recipient of the Blackwell--Rosenbluth award from the International Society for Bayesian Analysis.

He joined the department of Statistical Science in January 2018, after a postdoc at the University of Bristol, under Christophe Andrieu, as part of the i-like project, and before that a PhD at UCL, supervised by Mark Girolami and Alex Beskos.

He believes in Stigler's law of eponymy, and that the concept of multiple discovery/simultaneous invention is more closely aligned with the reality of scientific research than the heroic theory of invention that is dominant in popular culture. That being said, I still have a romantic view of academic life and strive for originality in his work. You can understand some of his thoughts on research from this podcast episode: Betancourting Disaster Round 13: Transitioning Between Statistical Theory and Practice | Patreon.

22/06 1pm-2pm: WCPM, Ludovic Berthier, ESPCI

James Kermode — Mon, 20 Apr 2026 09:05:12 GMT

When: 1pm - 2pm, Mon, 22 Jun '26

Networking Lunch: Outside L5, from 12:30pm - 1pm.

Title: TBC

Abstract: TBC

Bio: Ludovic Berthier is a Directeur de Recherche at the CNRS, based at the Laboratoire Gulliver at ESPCI Paris. He is an internationally recognised leader in statistical physics, specialising in the theory and simulation of complex, disordered systems.

His research spans a wide range of topics at the intersection of physics and materials science, including non-equilibrium statistical mechanics, soft matter and complex fluids, and the physics of supercooled liquids and glasses. He has made particularly influential contributions to understanding the glass transition, amorphous solids, and jamming phenomena, as well as emerging areas such as active and biological matter.

Ludovic’s work combines theoretical insight with advanced computational methods to uncover universal behaviours in high-dimensional and disordered systems. He has authored numerous high-impact publications in leading journals such as Nature Materials, Physical Review Letters, Physical Review X, and PNAS, and has contributed to major review articles shaping the field, including on yielding in amorphous solids and machine learning approaches to glassy systems.

Through his research, he continues to push the boundaries of how we understand and design complex materials, both in and out of equilibrium. Find out more here: https://ludovicberthier.github.io/

29/06 1pm-2pm: WCPM, Loïc Lannelongue, Cambridge

James Kermode — Wed, 22 Apr 2026 08:54:17 GMT

When: 1pm - 2pm, Mon, 29 Jun '26

Networking Lunch: Outside L5, from 12:30pm - 1pm.

Title: The (environmental) sustainability challenge of modern computing & AI

Abstract: From genetic studies and astrophysics simulations to AI, scientific computing has enabled amazing discoveries—and there's no doubt it will continue to do so. At the same time, the resource usage (energy, water) and environmental impacts of digital (research) infrastructures are becoming impossible to ignore given the urgency of the climate crisis. So what can we all do about it? And as scientists, should we even be thinking about this? We'll break down how computing activities impact the environment, debate our collective responsibility to tackle it, and discuss the latest efforts of the Cambridge Sustainable Computing Lab to empower researchers to understand and mitigate their environmental impacts. Through the lens of the GREENER principles for environmentally sustainable science, we'll explore the challenges the research community needs to overcome to create real change in this space. It will also be a chance to highlight how the Green DiSC certification framework can support scientists and institutions in making their research more sustainable.

Bio: Dr Loïc Lannelongue is an Assistant Research Professor in Computer Science at the University of Cambridge, where he also serves as Bye-Fellow and Director of Studies in Computer Science (Part II) at Jesus College Cambridge. His work sits at the intersection of computing, sustainability, and responsible innovation.

Dr Lannelongue specialises in environmentally sustainable computing, with a particular focus on understanding and reducing the environmental impact of modern computational practices, including artificial intelligence. His research takes a multi-faceted approach, combining technical development, behavioural insights, and policy engagement to drive more sustainable scientific workflows.

His academic interests include developing tools to monitor and reduce the carbon footprint of scientific computing, contributing to sustainability frameworks and policy, and exploring the ethical implications of modern science and AI. In parallel, he works in radiogenomics, applying machine learning to integrate genomics and medical imaging data to improve understanding of cardiovascular disease.

Through his research and teaching, Dr Lannelongue is committed to advancing a more sustainable and responsible future for computational science.

Webpage: https://www.jesus.cam.ac.uk/people/loic-lannelongueLink opens in a new window