Organizers

Yutong Bai (Google Scholar) is a Postdoctoral Researcher at UC Berkeley (BAIR), advised by Alexei A. Efros, Jitendra Malik, and Trevor Darrell. She received her PhD in Computer Science from Johns Hopkins and is a 2023 Apple Scholar in AI/ML and MIT EECS Rising Star. Her research focuses on robust, self-supervised, and generative visual learning.

Adrien Corenflos (Google Scholar) is an Assistant Professor in the Department of Statistics at the University of Warwick. His research focuses on computational statistics and probabilistic machine learning, with a marked focus on computational and statistical parallelism, with application to Monte Carlo methodology and state-space models.

Mónika Farsang (Google Scholar) is a PhD student at TU Wien, focusing on interpretable and biologically inspired neural architectures, particularly liquid neural networks, exploring the trade-off between biological interpretability and scalable long-sequence modeling.

Xavier Gonzalez (Google Scholar) is a final-year PhD student at Stanford. He has published work parallelizing nonlinear state space models like RNNs, MCMC, and more. Previously, he read for an MSc in Statistics at Oxford as a Rhodes Scholar. Contact organizer.

Leo Kozachkov (Google Scholar) is an Assistant Professor in the School of Engineering at Brown University and the Carney Institute for Brain Science. His research focuses on understanding dynamics, control, and computation in both natural and artificial systems.

Scott W. Linderman (Google Scholar) is an Assistant Professor at Stanford University in the Statistics Department and the Wu Tsai Neurosciences Institute, and the Co-Director of the Stanford Center for Neural Data Science. His research focuses on machine learning and neuroscience, and he has made important contributions in parallelizing sequential algorithms. Contact organizer.

Korbinian Pöppel (Google Scholar) is a post-doctoral researcher at the ELLIS Institute Tübingen. He works on hardware-efficient, scalable novel architectures for sequence modeling, combining gating mechanisms with associative memory for advanced recurrent models.

David M. Zoltowski (Google Scholar) is a Postdoctoral Scholar in the Department of Statistics at Stanford University. His research broadly focuses on probabilistic machine learning and computational neuroscience. He has developed algorithms to parallelize the evaluation of nonlinear dynamical systems.