Speakers
Invited Speakers
We are happy to feature the following invited speakers:
Gintare Karolina Dziugaite
Affiliation: Google DeepMind, Mila, McGill University
Biography: Gintare Karolina Dziugaite is a senior research scientist at Google DeepMind, based in Toronto, an adjunct professor in the McGill University School of Computer Science, and an associate industry member of Mila, the Quebec AI Institute. Prior to joining Google, she led the Trustworthy AI program at Element AI/ServiceNow. Her research combines theory and empiricism on deep learning generalization and compression, including PAC-Bayes perspectives that connect weight-space properties to generalization. She has co-authored many papers on understanding linear mode connectivity, model merging, and symmetries.
Damian Borth
Affiliation: University of St. Gallen
Biography: Prof. Dr. Damian Borth is the director of the Institute of Computer Science at the University of St. Gallen, where he holds a full professorship in Artificial Intelligence and Machine Learning (AIML). Previously, Damian was the founding director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, where he was also PI of the NVIDIA AI Lab at the DFKI. His research focuses on representation learning of neural networks’ weight spaces. His work has been awarded the ACM SIGMM Test of Time Award 2023, the Google Research Scholar Award 2022, the NVIDIA AI Lab at GTC 2016, the Best Paper Award at ACM ICMR 2012, and the McKinsey Business Technology Award in 2011. Damian conducted his postdoctoral research at UC Berkeley and the International Computer Science Institute (ICSI), where he worked on projects at Lawrence Livermore National Laboratory. He received his PhD from the University of Kaiserslautern and the German Research Center for Artificial Intelligence (DFKI). During that time, Damian stayed as a visiting researcher at the Digital Video and Multimedia Lab at Columbia University, New York City, USA.
Zechun Liu
Biography: Zechun Liu is a Staff Research Scientist and Tech Lead at Meta. Her research focuses on improving the efficiency and deployability of foundation models through architectural optimization, low-bit quantization, and sparsity. Specifically, she is interested in using deep learning to solve practical industry problems such as resource constraints and the trade-off between compute and accuracy. Her recent work focused on leveraging weight-space symmetries for quantization (SpinQuant), a state-of-the-art method for LLM quantization.
Sidak Pal Singh
Biography: Sidak Pal Singh is a Research Scientist at Google DeepMind whose research investigates weight-space symmetries and their role in model merging. His research develops principled alignment methods based on optimal transport and permutation symmetries, enabling effective merging by accounting for symmetry-induced redundancies in parameter space. His recent work extends linear mode connectivity to transformers, demonstrating how symmetry-aware alignment enables merging and interpolation of large-scale foundation models.
Melanie Weber
Affiliation: Harvard University
Biography: Melanie is an Assistant Professor of Applied Mathematics and of Computer Science at Harvard University, where she leads the Geometric Machine Learning Group. Her research studies geometric structure in data and models and how to leverage such information for the design of new, efficient machine learning methods with provable guarantees. Before joing Harvard, she was a Hooke Research Fellow at the Mathematical Institute in Oxford and received her PhD from Princeton University. She is a recipient of the IMA Leslie Fox Prize in Numerical Analysis, an Alfred P. Sloan Research Fellowship in Mathematics, a Schmidt Sciences AI2050 Fellowship, and a COLM Outstanding Paper Award.