Past talk · AI/ML Seminar Series
Building Planetary-Scale Collaborative Intelligence
Postdoctoral Researcher, University of California, Berkeley
- Date & time
- Tuesday, March 19, 2024 · 11:00 AM
- Location
- Donald Bren Hall 4011
Abstract
Today, access to high-quality data has become the key bottleneck to deploying machine learning. Often, the data that is most valuable is locked away in inaccessible silos due to unfavorable incentives and ethical or legal restrictions. This is starkly evident in health care, where such barriers have led to highly biased and underperforming tools. Using my collaborations with Doctors Without Borders and the Cancer Registry of Norway as case studies, I will describe how collaborative learning systems, such as federated learning, provide a natural solution. Yet for these systems to truly succeed, three fundamental challenges must be confronted: they need to 1) be efficient and scale to massive networks, 2) manage the divergent goals of the participants, and 3) provide resilient training and trustworthy predictions. I will discuss how tools from optimization, statistics, and economics can be leveraged to address these challenges.
About the speaker
Sai Praneeth Karimireddy is a postdoctoral researcher at the University of California, Berkeley with Mike I. Jordan. Karimireddy obtained his undergraduate degree from the Indian Institute of Technology Delhi and his PhD at the Swiss Federal Institute of Technology Lausanne (EPFL) with Martin Jaggi. His research builds large-scale machine learning systems for equitable and collaborative intelligence and designs novel algorithms that can robustly and privately learn over distributed data. His work has seen widespread real-world adoption through close collaborations with public health organizations and industries such as Meta, Google, OpenAI, and Owkin.