AI4Science Colloquium

Previous colloquia

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1 December 2025 › Gábor Csányi

Title: Foundational models for materials chemistry
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11 June 2024 › Kieron Burke

Density functional theory and machine learning: The race to find the next best approximation
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14 March 2023 › Aaditya Ramdas

Conformal prediction under distribution shift
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25 October 2022 › Fredrik Lindsten

Deep Gaussian Markov Random Fields
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11 October 2022 › Guy Wolf

Geometry-based Data Exploration
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5 July 2022 › Michele Ceriotti

A unified theory of atom cloud representations for machine learning
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21 June 2022 › Peter Grünwald

E is the New P: Evidence and Optional Continuation
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7 June 2022 › Wujie Wang

Generative Coarse-Graining of Molecular Conformations
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24 May 2022 › Francesca Grisoni

Harnessing artificial intelligence for de novo drug design
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26 April 2022 › Maximilian Dax

Real-Time Gravitational Wave Science with Neural Posterior Estimation
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14 April 2022 › Gabriel Vivo-Truyols

On the use of Bayesian statistics for chromatography and mass spectrometry: dealing with big data
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29 March 2022 › Anna Scaife

Catalogue curation, likelihood misspecification and dataset shift: challenges for Bayesian deep-learning in radio astronomy
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15 March 2022 › Rajesh Ranganath

Interpretability and Out of Distribution Generalization in Deep Predictive Models
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1 March 2022 › Martin van Hecke

Machine Learning of Combinatiorial Rules in Mechanical Metamaterials
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7 February 2022 › Jan-Matthis Lückmann

Simulation-Based Inference for Neuroscience and Beyond
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18 January 2022 › Andrew Ferguson

Ultra-fast molecular simulators and data-driven protein design
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21 December 2021 › Atılım Güneş Baydin

Probabilistic Programming in Scientific Simulators
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23 November 2021 › Chris Rackauckas

The Interplay of Science-Guided AI and Differentiable Simulation
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9 November 2021 › Jan-Willem van de Meent

Compositional Inference in Probabilistic Programs
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31 August 2021 › Saer Samanipour

Machine Learning and High Resolution Mass Spectrometry
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22 June 2021 › Pratyush Tiwari

Can artificial intelligence help understand and predict molecular dynamics?
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25 May 2021 › Alexander Tkatchenko

On Electrons and Machine Learning Force Fields
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11 May 2021 › Chandler Squires

Predicting Gene Expression across Cell Types and Drugs
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13 April 2021 › Ferry Hooft

Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks
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30 March 2021 › Benjamin Miller

Constrained marginal likelihood-to-evidence ratio estimation
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2 March 2021 › Jakub Tomczak

All that glitters is not Deep Learning in Life Sciences (but sometimes it is!)
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16 February 2021 › Eliu Huerta Escudero

Towards Accelerated, Reproducible, Physics-informed AI-driven Discovery
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2 February 2021 › Mario Geiger

e3nn: Euclidean symmetry for neural networks
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19 January 2021 › Dim Coumou

Machine Learning in climate science: Finding causal connections and improving seasonal forecasts
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24 November 2020 › Luisa Lucie-Smith

Machine Learning the formation of dark matter halos in the Universe
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10 November 2020 › Frank Noé

Boltzmann-generating Flows
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27 October 2020 › Gábor Csányi

Representation and regression problems for molecular structure and dynamics
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13 October 2020 › David Fischer

Attributing variance in single-cell genomics
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29 September 2020 › Christoph Weniger

Precision analysis of gravitational strong lensing images with nested likelihood-free inference
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15 September 2020 › Erik Henning Thiede

Permutation-Equivariant neural networks for Molecular Generation
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30 June 2020 › Tristan Bereau

Physically-motivated machine learning for multiscale molecular simulations
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