[Sds-seminars] S&DS In-Person Seminar, Tailin Wu, 2/22, 4pm-5pm, "Learning structured representations for accelerating scientific discovery and simulation"

[elizavette.torres at yale.edu]

 <https://statistics.yale.edu/>     <https://statistics.yale.edu/>
Department of Statistics and Data Science  

In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with
the option of virtual participation (
<https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f
8b73c34-a27b-42a7-a073-af2d00f90ffa>
https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8
b73c34-a27b-42a7-a073-af2d00f90ffa)

 <https://0.0.0.10/> 3:30pm -   Pre-talk meet and greet teatime - Dana
House, 24 Hillhouse Avenue 

 


TAILIN WU, Stanford University




Date; Wednesday, February 22, 2023

Time: 4:00PM to 5:00PM

Mason Lab 211
<http://maps.google.com/?q=9+Hillhouse+Ave%2C+New+Haven%2C+CT%2C+06511%2C+us
> see map 

9 Hillhouse Ave

New Haven, CT 06511

 <https://tailin.org/> Website

 

 

Title: Learning structured representations for accelerating scientific
discovery and simulation

 

Information and Abstract: 

Across most disciplines of science, e.g., physics, chemistry, biomedicine,
materials, mechanical engineering, and energy, a most critical challenge is
that their simulations and discoveries are typically slow due to the
large-scale, complex and multi-scale nature of the system. In this talk, I
will introduce my research that tackles this challenge by developing machine
learning models with structured and efficient representations for
accelerating scientific discovery and simulation. To accelerate scientific
discovery, I developed neuro-symbolic methods which can distill the data
into human-interpretable symbolic knowledge (governing equations and
relational structures) and generalize to more complex data in inference. To
accelerate large-scale scientific simulations, I developed structured
representations to accelerate critical scientific simulations for fluid
dynamics, plasma science, and generic partial differential equations (PDEs).
For example, I developed a hybrid particle-fluid representation for
simulating a large-scale laser-plasma interaction in a national lab facility
that has important applications in physics, materials, and biomedical
science. Our model is able to simulate millions of particles per time step,
orders of magnitude faster than the classical solver, and significantly
reduce long-term prediction error compared to strong deep learning
baselines.

BIO: Tailin Wu is a postdoctoral scholar in the Computer Science Department
at Stanford University, working with Prof. Jure Leskovec. He received his
Ph.D. from MIT Physics, where his thesis focused on AI for Physics and
Physics for AI. His research interests include developing machine learning
methods for large-scale scientific simulations, neuro-symbolic methods for
scientific discovery, and representation learning, using tools of graph
neural networks, information theory, and physics. His work has been
published in top machine learning conferences and leading physics journals,
and featured in MIT Technology Review. He also serves as a reviewer for
high-impact journals such as PNAS, Nature Communications, Nature Machine
Intelligence, and Science Advances. 

In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with
the option of virtual participation (
<https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f
8b73c34-a27b-42a7-a073-af2d00f90ffa>
https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8
b73c34-a27b-42a7-a073-af2d00f90ffa)

 <https://0.0.0.10/> 3:30pm -   Pre-talk meet and greet teatime - Dana
House, 24 Hillhouse Avenue 

For more details and upcoming events visit our website at
<http://statistics.yale.edu/> http://statistics.yale.edu/

 

Department of Statistics and Data Science

Yale University
24 Hillhouse Avenue
New Haven, CT 06511

t 203.432.0666
f 203.432.0633

 

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