Keynote Speakers
Piotr Indyk
Professor of Electrical Engineering and Computer Science at MIT
Title: Graph-based algorithms for similarity search: challenges and opportunities
Abstract:
Over the last few years, graph-based approaches to nearest neighbor search have gained renewed interest. Algorithms such as HNSW, NSG, and DiskANN have become popular tools in practice. These algorithms are highly versatile and come equipped with efficient implementations. At the same time, the theoretical guarantees of these algorithms are relatively limited. For instance, it has been observed (Indyk, Xu '23) that there exist simple low-dimensional datasets for which most of these algorithms exhibit query times that scale linearly with the dataset size. In this talk, I will discuss some of the challenges and opportunities presented by this class of algorithms.
Bio:
Piotr Indyk is the Thomas D. and Virginia W. Cabot Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, where he has been on the faculty since 2000. He graduated from the University of Warsaw in 1995 and received his Ph.D. in Computer Science from Stanford University in 2001. He received the Packard Fellowship in 2003 and the Simons Investigator Award in 2013. He is also a co-winner of the 2012 Paris Kanellakis Theory and Practice Award for his work on Locality-Sensitive Hashing. Piotr Indyk is a fellow of the Association for Computing Machinery and a member of the American Academy of Arts and Sciences and the National Academy of Sciences.
Bradley C. Love
Professor of Cognitive and Decision Sciences at University College London
Title: Embeddings of and for the mind
Abstract:
Coming soon...
Bio:
Bradley C. Love is a Professor of Cognitive and Decision Sciences in Experimental Psychology at University College London (UCL). He is also a distinguished fellow at The Alan Turing Institute for data science and AI, as well as the European Lab for Learning \& Intelligent Systems (ELLIS). His research lab is dedicated to advancing the understanding of human learning and decision-making by integrating behavioral, computational, and neuroscience perspectives. Currently, his team is pioneering efforts in large-scale modeling of brain and behavior using deep learning techniques. Additionally, they are developing BrainGPT, an innovative tool designed to assist neuroscience researchers by leveraging large language models.
Sanjiv Kumar
Google Fellow and VP at Google Research
Title: New learning objectives for massive scale similarity search
Abstract:
Coming soon...
Bio:
Sanjiv Kumar is a Google Fellow and VP at Google Research, where he leads a team focused on the theory and applications of large ML Foundational Models and Generative AI. His recent research interests include rethinking existing modeling and computing paradigms in LLMs, with a focus on developing alternative techniques that allow fast training and inference. He also leads the development of massive-scale similarity search techniques, which are widely adopted in Google and the open-source community. He has published more than 100 papers and holds 60+ patents in the area of ML and Computer Vision. His work on the convergence of Adam received the Best Paper Award at ICLR 2018. He is an action editor of JMLR and holds a Ph.D. from the School of Computer Science at Carnegie Mellon University.