AI-Fun & ELLIS Invited Speaker Series | Nikolay Malkin
| Dates: | 13 May 2026 |
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| Times: | 11:00 - 12:00 |
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| What is it: | Seminar |
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| Organiser: | Faculty of Science and Engineering |
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| Who is it for: | University staff, External researchers, Alumni, Current University students |
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| Speaker: | Nikolay Malkin |
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For May's AI-Fun and ELLIS invited speaker series, we will have Nikolay Malkin from the University of Edinburgh.
Title: Inferring stochastic dynamics without data: from diffusion samplers to discrete Schrödinger bridges
Abstract:
Probabilistic models that approximate a distribution by transporting particles from a source distribution to the target following a learnt dynamics model have seen rapid development and adoption in recent years: indeed, diffusion models and continuous normalising flows show success in generative modelling for various domains. I will describe the less-known use of such dynamics-based models as variational families: fitting their parameters to sample distributions from which no samples are available but an unnormalised target density can be queried. This problem has many algorithmic faces, with connections to entropic reinforcement learning, optimal transport, stochastic control, and sequential Monte Carlo. Our recent work has extended algorithms for diffusion sampling to the discrete-space case and to learning bridge dynamics between two distributions without access to samples from both. Applications include sampling Boltzmann densities of molecular conformations, inverse problems and conditional generation under pretrained generative model priors, and accelerating particle-based algorithms for Bayesian inference (in the continuous case) and inference over probabilistic model structure (e.g., Bayesian program induction and symbolic regression) and alignment of discrete-latent image generative models (in the discrete case).
Bio:
Nikolay Malkin is a Chancellor's Fellow in Informatics at the University of Edinburgh and a fellow of CIFAR's Learning in Machines and Brains programme. Their research focuses on algorithms for probabilistic inference and Bayesian machine learning, with applications in generative modelling, neurosymbolic AI, and machine reasoning. Within machine learning, their work explores modelling of Bayesian posteriors over high-dimensional and structured variables, induction and discovery of compositional structure in generative models, and uncertainty-aware reasoning in language and formal systems. Their work has found applications in pure and applied sciences, including inverse imaging, remote sensing, discovery of novel biological and chemical structures, and, most recently, robot control. Dr Malkin holds a PhD in mathematics from Yale University (2021) and was previously a postdoctoral researcher at Mila – Québec AI Institute in Montréal (2021 to 2024).
If you are unable to attend in person, please follow the ticketsource link provided to register and then check the registration confirmation for the Teams link.
Speaker
Nikolay Malkin
Role: Chancellor's Fellow
Organisation: University of Edinburgh
Travel and Contact Information
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Lecture Theatre 1.3
Kilburn Building
Manchester
