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

[Vassilis Tsotras]

Reminder for the AI Seminar, tomorrow Friday, at noon in the MRB seminar 
room. Please use the link below if you plan to attend.

Sincerely,
V. Tsotras

--------------------------------------
On 2025-05-17 13:15, Vassilis Tsotras wrote:
> 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.
> 
> 
> ------------------------------------
> 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
> --