[CMU AI Seminar] Special! May 13 at 1:45pm (Hybrid) -- Eric Xing (CMU) -- From Learning, to Meta-Learning, to "Lego-Learning" -- theory, system, and engineering -- AI Seminar sponsored by Morgan Stanley

[Asher Trockman]

Hi all,

Just a reminder that Eric Xing will be giving a talk today at 1:45pm in GHC
8102. You can also join on Zoom:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher

On Tue, May 10, 2022 at 1:41 PM Asher Trockman <ashert at cs.cmu.edu> wrote:

> Dear all,
>
> We invite you to a special installment of our CMU AI Seminar Series* this
> Friday (5/13)* in *GHC 8102 *and on Zoom from *1:45 - 2:45** PM (U.S.
> Eastern time)*, sponsored by Morgan Stanley
> <https://www.morganstanley.com/about-us/technology/>.
>
> To learn more about the seminar series or see the future schedule, please
> visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
>
> On this Friday (5/13), *Eric Xing *(CMU & MBZUAI) will be giving a talk
> titled *"From Learning, to Meta-Learning, to "Lego-Learning" -- theory,
> system, and engineering"* to share his work towards building machine
> learning pipelines and systems that meet highly-demanding industrial
> standards.
>
> *Title*: From Learning, to Meta-Learning, to "Lego-Learning" -- theory,
> system, and engineering
>
> *Talk Abstract*: Software systems for complex tasks - such as controlling
> manufacturing processes in real-time; or writing radiological case reports
> within a clinical workflow — are becoming increasingly sophisticated and
> consist of a large number of data, model, algorithm, and system elements
> and modules. Traditional benchmark/leaderboard-driven bespoke approaches in
> the Machine Learning community are not suited to meet the highly demanding
> industrial standards beyond algorithmic performance, such as
> cost-effectiveness, safety, scalability, and automatability, typically
> expected in production systems. In this talk, I discuss some technical
> issues toward addressing these challenges: 1) a theoretical framework for
> panoramic learning with all experiences; 2) optimization methods to best
> the effort for learning under such a principled framework; 3) compositional
> strategies for building production-grade ML programs from standard parts. I
> will present our recent work toward developing a standard model for
> Learning that unifies different special-purpose machine learning paradigms
> and algorithms, then a Bayesian blackbox optimization approach to Meta
> Learning in the space of hyperparameters, model architectures, and system
> configurations, and finally principles and designs of standardized software
> Legos that facilitate cost-effective building, training, and tuning of
> practical ML pipelines and systems.
>
> *Speaker Bio*: Eric P. Xing is the President of the Mohamed bin Zayed
> University of Artificial Intelligence, a Professor of Computer Science at
> Carnegie Mellon University, and the Founder and Chairman of Petuum Inc., a
> 2018 World Economic Forum Technology Pioneer company that builds
> standardized artificial intelligence development platform and operating
> system for broad and general industrial AI applications. He completed his
> PhD in Computer Science at UC Berkeley. His main research interests are the
> development of machine learning and statistical methodology; and
> composable, automatic, and scalable computational systems, for solving
> problems involving automated learning, reasoning, and decision-making in
> artificial, biological, and social systems. Prof. Xing currently serves or
> has served the following roles: associate editor of the Journal of the
> American Statistical Association (JASA), Annals of Applied Statistics
> (AOAS), and IEEE Journal of Pattern Analysis and Machine Intelligence
> (PAMI); action editor of the Machine Learning Journal (MLJ) and Journal of
> Machine Learning Research (JMLR); he is a board member of the International
> Machine Learning Society.
>
> *In Person: *GHC 8102
> *Zoom Link*:
> https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09
>
> Thanks,
> Asher Trockman
>
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