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CIS Seminar: “Building Planetary-Scale Collaborative Intelligence”

March 21 at 3:30 PM - 4:30 PM

Today, access to high-quality data has become the key bottleneck to deploying Machine Learning (ML). Often, data that is most valuable is locked away in inaccessible silos due to unfavorable incentives and ethical-legal restrictions. This is starkly evident in healthcare, where such barriers have led to highly biased and underperforming tools.

In my talk, we will dive into my collaborations with public health organizations facing such issues, and see how collaborative systems (such as federated learning) prove a natural solution. Collaborative learning can remove barriers to data sharing by respecting the privacy and interests of the data providers. Yet, for these systems to truly succeed, we must confront three fundamental challenges. These systems need to i) be efficient and scale to large networks, ii) provide reliable and trustworthy training and predictions, and iii) manage the divergent goals and interests of the participants. We discuss how tools from optimization, statistics, and economics can be leveraged to address these challenges.

Sai Praneeth Karimireddy

Department of Computer Science, University of California, Berkeley

Sai Praneeth Karimireddy is a postdoc at UC Berkeley with Mike I. Jordan, obtained his PhD at EPFL with Martin Jaggi, and his undergraduate from IIT Delhi. 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 i.e. edge, federated, and decentralized learning. He also closely engages with industry and public health organizations (Doctors Without BordersRed CrossCancer Registry of Norway) to translate his research into practice.
His work has previously been deployed across industry by FacebookGoogleOpen AIOwkin, and has been awarded with the Patrick Denantes Memorial Prize for the best computer science thesis at EPFL, the Chorafas Foundation Prize for exceptional applied research, an EPFL thesis distinction award, an SNSF fellowship, and best paper awards at FL-ICML 2021 and FL-NeurIPS 2022.


March 21
3:30 PM - 4:30 PM
Event Tags:


Computer and Information Science
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Wu and Chen Auditorium (Room 101), Levine Hall
3330 Walnut Street
Philadelphia, PA 19104 United States
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