BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Building Planetary-Scale Collaborative Intelligence X-WR-TIMEZONE:America/Los_Angeles BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2024-03-19-sai-praneeth-karimireddy@cml.ics.uci.edu DTSTAMP:20240319T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20240319T110000 DTEND;TZID=America/Los_Angeles:20240319T120000 SUMMARY:[CML Seminar] Sai Praneeth Karimireddy: Building Planetary-Scale Co llaborative Intelligence LOCATION:Donald Bren Hall 4011 DESCRIPTION:Sai Praneeth Karimireddy\, Postdoctoral Researcher\, University of California\, Berkeley\n\nTitle: Building Planetary-Scale Collaborative Intelligence\n\nAbstract: 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 inc entives and ethical or legal restrictions. This is starkly evident in heal th care\, where such barriers have led to highly biased and underperformin g tools. Using my collaborations with Doctors Without Borders and the Canc er 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 mu st be confronted: they need to 1) be efficient and scale to massive networ ks\, 2) manage the divergent goals of the participants\, and 3) provide re silient training and trustworthy predictions. I will discuss how tools fro m optimization\, statistics\, and economics can be leveraged to address th ese challenges.\n\nhttps://cml.ics.uci.edu/seminars/2024-03-19-sai-praneet h-karimireddy X-ALT-DESC;FMTTYPE=text/html:Sai Praneeth Karimireddy\, Postdoctoral Researcher\, University of California\, Berkeley

Ti tle: Building Planetary-Scale Collaborative Intelligence

Abs tract: Today\, access to high-quality data has become the key bottlene ck to deploying machine learning. Often\, the data that is most valuable i s locked away in inaccessible silos due to unfavorable incentives and ethi cal or legal restrictions. This is starkly evident in health care\, where such barriers have led to highly biased and underperforming tools. Using m y collaborations with Doctors Without Borders and the Cancer Registry of N orway 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 t he 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.< br>
https://cml.ics.uci.edu/seminars/2024-03-19-sai-praneeth-karim ireddy URL:https://cml.ics.uci.edu/seminars/2024-03-19-sai-praneeth-karimireddy END:VEVENT END:VCALENDAR