[AI Seminar Series] Seminar by Prof. Yuzhou Chen, Friday May 23, 12-1pm, MRB Seminar Room

[Vassilis Tsotras]

Colleagues,
the next AI Seminar will be next Friday May 23, 12:00-1:00pm at the MRB 
Seminar Room (1st floor).

**** Pizza and refreshments will be provided ****

To keep track of the number of attendees, please *register* at:
https://www.eventbrite.com/e/ai-seminar-series-tickets-1369759099339

The talk will be given by Prof. Yuzhou Chen, Department of Statistics, 
UCR

TITLE:
"Topology-Guided Deep Learning for Spatio-Temporal Data and Beyond"


ABSTRACT:
In many real-world applications such as intelligent transportation, 
biosurveillance, climate science, and bioinformatics, statistical models 
and machine learning algorithms are applied to large-scale 
spatio-temporal data. Such data are typically high-dimensional and 
demonstrate complex nonlinear spatial and temporal dependencies. In 
recent years, graph machine learning (GML) has emerged as a powerful 
machinery to harness the rich information encoded in various 
spatio-temporal datasets. However, the existing models still tend to be 
insufficient to handle complex structural phenomena exhibited by 
spatio-temporal processes and do not explicitly account for 
time-conditioned properties of the encoded knowledge. In this talk, I 
will demonstrate how our innovative approaches harnessing the 
interdisciplinary strengths of topological data analysis, statistics, 
and machine learning allow us to tackle these limitations across a 
spectrum of spatio-temporal applications. In particular, I will focus on 
topology-guided deep learning models for spatio-temporal processes that 
incorporate time-aware shape descriptors and discuss how pushing forward 
the performance boundary of GML models can assist in decision-making 
under uncertainties. I will showcase the applications of our models to 
challenging problems in spatio-temporal forecasting and dynamic link 
prediction tasks.


Bio:

Dr. Yuzhou Chen is a tenure-track Assistant Professor in the Department 
of Statistics at University of California, Riverside. He is also an 
adjunct professor in the Department of Computer and Information Sciences 
at Temple University and a Visiting Research Collaborator in Department 
of Electrical and Computer Engineering at Princeton University. Before 
that, Dr. Chen worked as a postdoctoral scholar in the Department of 
Electrical and Computer Engineering at Princeton University. Dr. Chen 
received his Ph.D. in Statistics from Southern Methodist University. His 
research focuses on geometric deep learning, topological data analysis, 
knowledge discovery in graphs and spatio-temporal data, with 
applications to power systems, biosurveillance and environmental data 
analytics. His research has appeared in the top machine learning and 
data mining top conferences, including ICML, ICLR, NeurIPS, KDD, AAAI, 
ICDM, ECML-PKDD, etc. He was the recipient of 2024 American Statistical 
Association on Joint Statistical Computing and Statistical Graphics 
Section Best Student Paper Award, 2021/2022 American Statistical 
Association Section on Statistics in Defense and National Security Best 
Student Paper Award, and 2021 Chateaubriand Fellowship from the Embassy 
of France in the United States.


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Sponsored by the RAISE at UCR Institute, the AI Seminar Series presents 
speakers working on cutting edge Foundational AI or apply AI in their 
research. The goal of these seminars is to inform the UCR community 
about current trends in AI research and promote collaborations between 
faculty in this emerging field.
These seminars are open to interested faculty and graduate students. 
Please forward this email to other colleagues or graduate students in 
your lab that may be interested. After the seminar a discussion will 
follow for questions, open problems, ideas for possible collaborations 
etc.

Sincerely,
Vassilis Tsotras
Professor, CSE Department
co-Director, RAISE at UCR Institute

Amit Roy-Chowdhury
Professor, ECE Department
co-Director, RAISE at UCR Institute
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