Speakers

We are honored to have several leading experts who will provide keynote presentations. Each speaker brings unique insights into machine learning theory, weight space analysis, and neural network synthesis.

Stella X. Yu

Stella X. Yu
Talk Title TBD
Talk Abstract TBD
Affiliation University of Michigan
Biography Stella X. Yu is a professor of computer science at the University of Michigan, where she focuses on research in computer vision and machine learning. Prior to joining the University of Michigan, she was the Director of the Vision Group at the International Computer Science Institute (ICSI) in Berkeley and held various academic roles at UC Berkeley, including positions in computer science, vision science, and cognitive sciences. Stella earned her Ph.D. from Carnegie Mellon University, where she specialized in robotics and vision science. Her research explores visual perception through multiple lenses of representation learning aiming to develop models that can exceed human capabilities. She is particularly focused on actionable representation learning and structure-aware models, emphasizing that structure in visual data should naturally emerge or be reflected in the model structures.
   

Boris Knyazev

Boris Knyazev
Talk Title TBD
Talk Abstract TBD
Affiliation Samsung - SAIT AI Lab
Biography Boris Knyazev is a Research Scientist at Samsung - SAIT AI Lab, Montreal, Canada. He completed his PhD at the Machine Learning Research Group, University of Guelph and Vector Institute under supervision of Graham Taylor in 2022. His research interests lie at the intersection of graph neural networks (GNNs), computer vision and meta-learning. In the past, he interned at Facebook AI Research (FAIR) working with Adriana Romero and Michal Drozdal on parameter prediction for neural networks. He also interned at Mila working with Eugene Belilovsky and Aaron Courville on visual compositional generalization. He also interned at SRI International with Mohamed Amer, where he worked on training GNNs on image superpixels. Before starting his PhD, he worked on unsupervised learning and pretraining of neural networks, face, emotion and facial attributes recognition, and video recognition.

Haggai Maron

Haggai Maron
Talk Title TBD
Talk Abstract TBD
Affiliation Technion, NVIDIA
Biography Haggai Maron is an Assistant Professor at the Technion’s Faculty of Electrical and Computer Engineering and a senior research scientist at NVIDIA Research. He earned his PhD in Computer Science and Applied Mathematics from the Weizmann Institute of Science. His research focuses on machine learning, particularly deep learning for structured data such as sets, graphs, point clouds, surfaces, and weight spaces. Haggai’s work has been recognized with an outstanding paper award at ICML 2020. He co-organized the Israeli Geometric Deep Learning Workshops and co-organizes the Graph Learning Meets Theoretical Computer Science Workshop at the Simons Institute in 2025.

Yedid Hoshen

Yedid Hoshen
Talk Title TBD
Talk Abstract TBD
Affiliation HUJI
Biography Yedid Hoshen is an Associate Professor at the Hebrew University of Jerusalem, Israel (2019-present). He is also a visiting faculty researcher at Google. Prior to that he was a Postdoc and researcher scientist in Facebook AI Research, New York, New York and Tel Aviv. He earned his PhD at the Hebrew University and MPhys at Oxford University. Yedid’s research focuses on representation learning and its applications e.g., anomaly detection, disentanglement, cross-domain retrieval and generation. Yedid’s lab is currently very active in developing new ways to represent models and its real-world use cases. Yedid has served as an area chair in ECCV’22, CVPR’23,24, NeurIPS’23,24 and will do so in CVPR’25.