[Colloquium] TODAY, 2pm: Data Science/CS Candidate Yuan Tian (University of Virginia)

[Rob Mitchum]

*Data Science Institute/Computer Science Candidate Seminar*

*Yuan Tian*
*Assistant Professor*

*University of Virginia*

*Thursday, February 17th*
*2:00 p.m. - 3:00 p.m.*
*In-Person: John Crerar Library, Room 390*
*Remote: Live Stream <http://live.cs.uchicago.edu/yuantian/> or Zoom
<https://uchicago.zoom.us/j/99495380796?pwd=VFRQNEltOUlsUDBaaGd0VFRBUDMwUT09>
(details
below)*


*When AI Meets Privacy: Building Privacy-preserving AI Systems*

Computing is undergoing a significant shift. First, the explosive growth of
the Internet of Things (IoT) enables users to interact with computing
systems and physical environments in novel ways through perceptual
interfaces (e.g., microphones and cameras). Second, machine learning
algorithms collect huge amounts of data and make critical decisions on new
computing systems. While these trends bring unprecedented functionality,
they also drastically increase the number of untrusted algorithms,
implementations, interfaces, and the amount of private data processed by
them, endangering user privacy. The pressing question is how to protect
user privacy with utility/performance preserved in machine learning? The
challenges are two folds: (1) how to improve user privacy in light of the
rapid advancement of AI; (2) how to prevent the existing AI from leaking
private user data.

In this talk, I’ll introduce my work on protecting user privacy in machine
learning systems from practical applications to theoretical frameworks.
First, I will use conversational AI systems to show our research in
identifying privacy violations with machine learning. Second, I will talk
about our research for building scalable and accurate privacy-preserving
machine learning systems. I will present our work CryptGPU, the first
privacy-preserving secure Multi-Party Computation framework fully
implemented on the GPU, which scales to modern large models and datasets.

*Bio*: Yuan Tian <https://engineering.virginia.edu/faculty/yuan-tian> is an
Assistant Professor of Computer Science at the University of Virginia.
Before joining UVA, she obtained her Ph.D. from Carnegie Mellon University
in 2017 and interned at Microsoft Research, Facebook, and Samsung Research.
Her research focuses on developing robust, privacy-preserving, and fair
machine learning algorithms and systems, as well as using machine learning
to solve security and privacy problems in emerging computing platforms. Her
work has real-world impacts as countermeasures and design changes have been
integrated into platforms (such as Android, Chrome, Azure, and iOS), and
also impacted the security recommendations of standard organizations such
as the Internet Engineering Task Force (IETF). She is a recipient of Google
Research Scholar Award 2021, Facebook Research Award 2021, NSF CAREER award
2020, NSF CRII award 2019, Amazon AI Faculty Fellowship 2019, CSAW Best
Security Paper Award 2019, and Rising Stars in EECS 2016. Her research has
appeared in top-tier venues in machine learning, security, and systems. Her
projects have been covered by media outlets such as IEEE Spectrum, Forbes,
Fortune, Wired, and Telegraph.

*Host*: Nick Feamster

*Zoom Info:*
https://uchicago.zoom.us/j/99495380796?pwd=VFRQNEltOUlsUDBaaGd0VFRBUDMwUT09
Meeting ID: 994 9538 0796
Password: ds2022



-- 
*Rob Mitchum*

*Associate Director of Communications for Data Science and Computing*
*University of Chicago*
*rmitchum at uchicago.edu <rmitchum at ci.uchicago.edu>*
*773-484-9890*
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