[Colloquium] 3/31: Data Science/CS Candidate Talk - Udit Gupta (Harvard)

[Rob Mitchum]

*Data Science Institute/Computer Science Candidate Seminar*

*Udit Gupta*
*PhD Student*
*Harvard University*

*Thursday, March 31st*
*2:00 p.m. - 3:00 p.m.*
*In Person: John Crerar Library, Room 390*
*Remote: Live Stream <http://live.cs.uchicago.edu/uditgupta/> or Zoom
<https://uchicago.zoom.us/s/96156114717?pwd=UmdnRHhQQVJXaVVTbzA0OXlOeDF6Zz09>
(details
below)*


*Faster, Smarter, and Greener Systems for Data-Center Scale AI*
The modern Internet is driven by AI-centric services that determine how we
interact with technology and society on a daily basis. The exponential rise
in AI is largely fueled by the design, development, and deployment of
domain-specific software and hardware that have yielded orders of magnitude
improvements for deep learning. Despite these efforts, this talk focuses on
an important, yet under-studied area: systems for deep learning-based
personalized recommendation. Personalized recommendations form the backbone
of our interaction with the Internet including search, e-commerce,
streaming, and social media. Systems play a crucial role in enabling
accurate, efficient, and sustainable recommendation engines.

In this talk, I show how modern deep learning-based personalized
recommendation engines not only consume the majority of AI training and
inference cycles in production data centers, but also introduce unique
system design challenges to efficient execution. To tackle these
challenges, I design solutions across the software and hardware stack to
optimize inference efficiency by jointly considering application-level
characteristics, unique neural network model architectures, data-center
scale implications, and the underlying hardware. Given the rapidly growing
infrastructure demands posed by AI and recommendation engines, my work
highlights that systems must go beyond performance, power, and energy
efficiency to consider environmental footprint as a first order design
target to enable sustainable computing. Finally, I chart paths to designing
future systems that enable emerging AI-driven applications by balancing
performance, efficiency, sustainability, and privacy.

*Bio*: Udit Gupta <https://ugupta.com/> is a PhD student at Harvard
University and visiting research scientist at Facebook AI Research. His
research interests focus on enabling next-generation responsible AI
platforms by designing novel computer systems and hardware. His recent work
focuses on the optimization of data center-scale deep learning-based
personalized recommendation engines (HPCA 2020, ISCA 2020, MICRO 2021,
ASPLOS 2021) and enabling sustainable computing by considering the
environmental impact of end-to-end hardware life cycles (HPCA 2021, MLSys
2022). Udit’s work has been evaluated at-scale in production data centers
and incorporated into standardized benchmarks and infrastructure used by
the research community. His research has been recognized as an IEEE MICRO
Top Picks honorable mention in 2020 and received an IEEE MICRO Top Picks
award in 2021, as well as nominated for best paper at PACT 2019 and DAC
2018. In addition to research, Udit is passionate about building
interdisciplinary communities. He has co-founded the PeRSonAl (personalized
recommendation systems and algorithms) workshop and CLEAR (computing
landscapes with environmental accountability and responsibility) workshops
co-located at systems and machine learning conferences like ASPLOS, ISCA,
and MLSys. He is also the co-chair of the Computer Architecture Student
Association.

*Host*: Andrew Chien

*Zoom Info:*
https://uchicago.zoom.us/s/96156114717?pwd=UmdnRHhQQVJXaVVTbzA0OXlOeDF6Zz09
Meeting ID: 961 5611 4717
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*
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cs.uchicago.edu/pipermail/colloquium/attachments/20220329/8e4025f8/attachment-0001.html>