Keynotes
Rose Yu is an associate professor at the University of California, San Diego, in the Department of Computer Science and Engineering. She is a primary faculty member with the AI Group and is affiliated with the Halıcıoğlu Data Science Institute. Her research focuses on machine learning, particularly for large-scale spatiotemporal data, and she is passionate about leveraging AI for scientific discovery.
Dr. Yu has received numerous awards, including the NSF CAREER Award, Hellman Fellowship, and Faculty Awards from JP Morgan, Meta, Google, Amazon, and Adobe. Before joining UC San Diego, she was an assistant professor at Northeastern University and a postdoctoral scholar at the California Institute of Technology.
Phillip Isola is the Class of 1948 Career Development associate professor in EECS at MIT. He studies computer vision, machine learning, robotics, and AI. He completed his Ph.D. in Brain & Cognitive Sciences at MIT, and has since spent time at UC Berkeley, OpenAI, and Google Research. His work has particularly impacted generative AI and self-supervised representation learning. Dr. Isola's research has been recognized by a Google Faculty Research Award, a PAMI Young Researcher Award, a Samsung AI Researcher of the Year Award, a Packard Fellowship, and a Sloan Fellowship. His teaching has been recognized by the Ruth and Joel Spira Award for Distinguished Teaching. His current research focuses on trying to scientifically understand human-like intelligence.
Inivited
Zahra Kadkhodaie has a background in solid state physics and is a Research Fellow at Center for Computational Neuroscience at the Flatiron Institute. She completed her PhD at NYU in Data Science advised by Eero Simoncelli. She has worked on learning high dimensional image densities from data, understanding and utilizing them. Specifically, she studied generalization versus memorization in densities embedded in diffusion models, low-dimensionality of conditional density models, interpreting effective dimensionality of image manifolds, and using learned image densities to solve inverse problems.
Joey Bose is a Post-Doctoral Fellow at the University of Oxford working with Michael Bronstein and an ML scientist at Dreamfold. He completed his PhD at McGill/Mila under the supervision of Will Hamilton, Gauthier Gidel, and Prakash Panagaden. His research interests span Generative Modelling, Differential Geometry for Machine Learning with a current emphasis on understanding symmetries, equivariances, and invariances in data.
Previously, he completed his Bachelor’s and Master’s degrees from the University of Toronto, working on adversarial attacks against face detection, and is the President and CEO of FaceShield Inc., an educational platform for digital privacy for facial data. His work has been featured in Forbes, CBC, VentureBeat, and other media outlets and is generously supported by the IVADO PhD Fellowship.
Nina Miolane is an Assistant Professor at the University of California, Santa Barbara. Her research aims to reveal the geometric signatures of natural and artificial intelligence and to build next–generation intelligent systems. Her lab co-develops the open-source packages: Geomstats and TopoX with the PyT-team. Dr. Miolane has received several awards, including an NSF Career Award, the L'Oréal-Unesco for Women in Science Award, the Hellman Fellow award and the UC Regent's Junior Faculty Award. Before UCSB, Dr. Miolane was a postdoctoral fellow at Stanford University.