Our Organizers

Tara Akhound-Sadegh is a PhD student at McGill University and Mila. She is interested in Sampling and Generative Modelling, particularly with applications in physics and biology, as well as Geometric Deep Learning (symmetries and equivariant models). Previously, she was a research scientist in residence DreamFold. She is also a lead organizer of the Geometric Deep Learning Reading Group at Mila and has experience in organizing events as a member of the EDI committee at Mila, and as a previous volunteer member of the non-profit organization initiasciences.

Marta Skreta is a PhD student at the University of Toronto and Vector Institute working with Alán Aspuru-Guzik under the Canada Graduate Scholarship. Her research focuses on molecular discovery using generative modelling and natural language, as well as automating chemistry experiments in self-driving labs. Previously, Marta completed internships at Apple and Mila AI for Humanity. She has experience organizing large-scale events, including coding workshops for 1000+ Canadian highschool women (HER CODE CAMP). Marta is a founding member of the AI4Materials workshop and has organized it at NeurIPS in 2022, 2023, and 2024 (upcoming).

Yuanqi Du is a PhD student at Cornell. His research focuses on probabilistic models and geometric deep learning for physical sciences and sustainability. He has been the lead organizer and (co)founder for a series of events related to probabilistic machine learning (DGM4HSD-ICLR22, SPIGM-ICML23, SPIGM-ICML24 workshops), AI for Science (workshops, online seminars and symposiums), and geometric deep learning (Learning on Graphs conference and local meetups). 

Sarthak Mittal is a PhD student at Université de Montréal and Mila, and a student researcher at Meta. His prior research focused on neuro-inspired deep learning architectures and the investigation of inductive biases to enhance generalization capabilities. Currently, he is exploring generative models, including diffusion and flow matching, and their applications in Bayesian posterior estimation to facilitate efficient and scalable inference.

Joey Bose is a Post-Doctoral fellow at the University of Oxford, and a Mila Affiliate member, and holds a Phd from in Computer Science from McGill/Mila. His research interests span Generative Modelling, Differential Geometry for Machine Learning, and Equivariant Machine Learning with a current emphasis on the foundations of geometry-aware generative models. In addition, he was the lead organizer for the Differential Geometry meets Deep Learning (DiffGeo4DL) workshop at NeurIPS2020 and also the primary instructor for a course on Geometric Generative Models given at McGill University in the Fall of 2022. 

Alex Tong is an incoming assistant professor at Duke University, a Post-Doctoral fellow at Université de Montréal, and holds a PhD in computer science from Yale University. His research interests are in generative modeling, deep learning, and optimal transport. He is working on applying ideas from generative modeling, causal discovery, optimal transport, and graph signal processing to understand how cells develop and respond to changing conditions. I’m also interested in generative models for protein design. He coorganized a Banff workshop on Deep Exploration of non-Euclidean Data with Geometric and Topological Representation Learning in 2022.

Kirill Neklyudov is an Assistant Professor at the University of Montreal and a Core Academic Member at Mila - Quebec AI Institute. His research focuses on developing novel methods in generative modelling, Monte Carlo methods, and Optimal Transport, and applications of these methods to fundamental problems in natural sciences, e.g. finding eigenstates of the many-body Schrodinger equation, simulating molecular dynamics, predicting the development of biological cells, conformational sampling, and protein folding. His community service includes the organization of SPIGM-ICML24 workshop and service as an area chair at ICLR 2025.

Michael Bronstein is the DeepMind Professor of AI at the University of Oxford and Scientific Director at Aithyra. He was previously Head of Graph Learning Research at Twitter, a professor at Imperial College London and held visiting appointments at Stanford, MIT, and Harvard. He has been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-time scholar (2020)). Michael received his PhD from the Technion in 2007. He is the recipient of the EPSRC Turing AI World Leading Research Fellowship, Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019).

Max Welling is a full professor and research chair in machine learning at the University of Amsterdam and a Merkin distinguished visiting professor at Caltech. He is co-founder and CAIO of the startup CuspAI in Materials Design. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he served on the founding board until 2021. His previous appointments include Partner and VP at Microsoft Research, VP at Qualcomm Technologies, professor at UC Irvine, postdoc at UCL & U. Toronto under the supervision of prof. Geoffrey Hinton, and postdoc at Caltech under the supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under the supervision of Nobel laureate prof. Gerard ‘t Hooft. Max Welling has served as associate editor-in-chief of IEEE TPAMI 2011-2015, and on the advisory board of the Neurips Foundation since 2015. He is co-founder of ELLIS and was the program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS 2009 and ECCV 2016, and the general chair and cofounder of MIDL 2018. Max Welling received the ECCV Koenderink Prize in 2010, and the 10-year Test of Time awards at ICML in 2021 and ICLR in 2024.

Arnaud Doucet is a full-time Senior Staff Research Scientist at Google DeepMind. Previously, he was a professor of statistics at Oxford University. He has also held academic positions at Cambridge University, Melbourne University, The Institute of Statistical Mathematics in Tokyo and the University of British Columbia where her was a Canada Research Chair in Stochastic Computation. He was also an Institute of Mathematical Statistics Medallion (IMS) Lecturer in 2016, was elected IMS Fellow in 2017 and was awarded the Guy Silver Medal from the Royal Statistical Society in 2020.

Aapo Hyvärinen studied undergraduate mathematics at the universities of Helsinki (Finland), Vienna (Austria), and Paris (France), and obtained a Ph.D. degree in Information Science at the Helsinki University of Technology in 1997.  After post-doctoral work at the Helsinki University of Technology, he moved to the University of Helsinki, where he was appointed Professor in 2008, at the Department of Computer Science. From 2016 to 2019, he was a Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London, UK.  Aapo Hyvarinen is the main author of the books "Independent Component Analysis" (2001), "Natural Image Statistics" (2009), and "Painful Intelligence" (2022). He is Action Editor at the Journal of Machine Learning Research and Neural Computation and has worked as Area Chair at ICML, ICLR, AISTATS, UAI, ACML and NeurIPS.