[AI Seminar Series] REMINDER: Seminar by Prof. Vishnu Boddeti (MSU), tomorrow Friday Dec.16th, 12:30-1:30pm, WCH 205-206

[Vassilis Tsotras]

Reminder for the AI seminar tomorrow. Please note the change in room and
time.
Register below if you plan to attend.

best,
V. Tsotras
-------------------------------

On Sun, Jan 11, 2026 at 6:01 PM Vassilis Tsotras <vassilis.tsotras at ucr.edu>
wrote:

> Dear colleagues, best wishes for a Happy New Year!
>
> The next AI Seminar will be on Friday January 16th, 12:30-1:30pm at the
> Winston Chung Hall (WCH) 205-206.
> PLEASE NOTE the update in start time and place (just for this seminar).
>
> *** 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-1980342380369
>
> The talk will be given by *Prof. Vishnu Boddeti*, Department of Computer
> Science and Engineering, Michigan State University
>
> TITLE: The Art of Unseeing Ghosts in our Data
>
> ABSTRACT:
> Modern deep learning is exceptionally good at seeing patterns, but it
> often sees too much. As models scale, they increasingly begin to see
> “ghosts", which are nuisance factors that haunt the data and masquerade as
> true signals. These ghosts appear as stereotypes in social data, as
> overwhelming thermal emission in physical sensor data, or as rigid concept
> associations in generative models. All of these artifacts obscure the true
> causal structure of the world.
> In this talk, I argue that scaling alone cannot exorcise these ghosts.
> Instead, we must learn the art of unseeing them. I will present a unified
> framework for Invariant Representation Learning that formalizes this
> unseeing as "structural surgery" on the underlying causal graph. This
> approach mathematically severs the dependence on nuisance factors while
> preserving the true signal.
>
> I will demonstrate how this single theoretical approach addresses three
> seemingly distinct challenges:
> 1. Exorcising Social Ghosts: How to surgically erase sensitive attributes
> from latent representations to ensure fairness and controllability, without
> destroying model utility.
> 2. Exorcising Physical Ghosts: How thermodynamic laws can be used as
> causal priors to disentangle direct heat emission from surface texture in
> thermal imaging, enabling AI to see clearly through darkness.
> 3. Exorcising Conceptual Ghosts: How decomposing score functions in
> diffusion models allows us to "unsee" spurious co-occurrences, unlocking
> compositional generalization for novel creation.
>
> By moving from passive correlation mining to active structural
> enforcement, this work lays the foundation for AI systems that are not
> fooled by the ghosts in the data, but are robust, fair, creative, and
> grounded in the causal structure of the real world.
>
> Bio:
> Vishnu Naresh Boddeti is an Associate Professor in the Department of
> Computer Science and Engineering at Michigan State University. His research
> focuses on building AI systems with provable guarantees, moving beyond
> "black box" scaling to models that are fair, private, and physically
> grounded.
> His work spans three interconnected pillars: (1) Responsible AI, where he
> develops invariant representation learning methods to audit and mitigate
> bias in foundation models; (2) Physics-Informed AI, which integrates
> physical laws with AI models; and (3) Secure AI, designing AI systems that
> operate on homomorphically encrypted user data for real-world deployment.
> His research has been featured on the cover of Nature and recognized with
> multiple awards, including the 2024 IEEE-CCF Best Paper Award and the 2023
> IEEE-TBIOM Best Student Paper Award. He currently serves as a Senior Area
> Editor for IEEE TIFS.
>
> ------------------------------------
> Sponsored by the RAISE at UCR Institute, the AI Seminar Series presents
> speakers working on cutting edge Foundational AI or applying 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/undergraduate students. Please forward this email to other
> colleagues or 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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