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<i class="">Department of Computer Science Seminar</i>
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<div class=""><span class=""><b class="">Ce Zhang</b></span></div>
<div class=""><b class="">Assistant Professor, Department of Computer Science </b></div>
<div class=""><b class="">ETH Zurich </b></div>
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<div class=""><b class="">Thursday, January 26th </b></div>
<div class=""><b class="">2:00pm - 3:00pm</b></div>
<div class=""><b class="">In Person: John Crerar Library 390</b></div>
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<div class=""><b class="">Zoom:</b></div>
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<div class=""><a href="https://uchicagogroup.zoom.us/j/97363936385?pwd=MGNJZmc5NlZkZ2xpaW5yVkJ6V2R0Zz09" class="">https://uchicagogroup.zoom.us/j/97363936385?pwd=MGNJZmc5NlZkZ2xpaW5yVkJ6V2R0Zz09</a></div>
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<div class="">Meeting ID: 973 6393 6385</div>
<div class="">Passcode: 605683</div>
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<div class=""><b class="">Title: Optimizing Communications and Data for Distributed and Decentralized Learning</b></div>
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<div class=""><b class="">Abstract:</b></div>
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<div class="">The rapid progress of machine learning in the last decade has been fueled by the increasing scale of data and compute. Today's training algorithms are often communication heavy, as a result, large-scale models are trained dominantly in a centralized
environment such as data centers with fast network connections. This strong dependency on fast interconnections is becoming the limiting factor of further scaling, not only for the data center setting but also for alternative decentralized infrastructures
such as spot instances and geo-distributed volunteer computes. In this talk, I will discuss our research in communication-efficient distributed learning and our current effort in training large language models in a decentralized way. </div>
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<div class=""><b class="">Bio:</b></div>
<div class=""><a href="https://zhangce.github.io/docs/bio.html" class="">Ce Zhang</a><span class="" style="caret-color: rgb(92, 89, 98); background-color: rgb(255, 255, 255);"> is an Assistant Professor in Computer Science at ETH Zurich. The mission of his
research is to make machine learning techniques widely accessible---while being cost-efficient and trustworthy---to everyone who wants to use them to make our world a better place. He believes in a system approach to enabling this goal, and his current
research focuses on building next-generation machine learning platforms and systems that are data-centric, human-centric, and declaratively scalable. Before joining ETH, Ce finished his PhD at the University of Wisconsin-Madison and spent another year
as a postdoctoral researcher at Stanford, both advised by Christopher Ré. His work has received recognitions such as the SIGMOD Best Paper Award, SIGMOD Research Highlight Award, Google Focused Research Award, an ERC Starting Grant, and has been featured and
reported by Science, Nature, the Communications of the ACM, and a various media outlets such as Atlantic, WIRED, Quanta Magazine, etc.</span></div>
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<div class=""><span class="" style="caret-color: rgb(92, 89, 98); background-color: rgb(255, 255, 255);"><img id="<5A26BBAA-80F3-41CD-8759-34C5436DE925>" alt="ce_2022.png" apple-inline="yes" class="Apple-web-attachment" style="opacity: 1;" src="cid:5A26BBAA-80F3-41CD-8759-34C5436DE925"></span></div>
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<div>---</div>
<div>Holly Santos<br class="">
Executive Assistant to Michael J. Franklin, Chairman<br class="">
Department of Computer Science<br class="">
The University of Chicago<br class="">
5730 S Ellis Ave-217 Chicago, IL 60637<br class="">
P: 773-834-8977<br class="">
<a href="mailto:hsantos@uchicago.edu" class="">hsantos@uchicago.edu</a><br class="">
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