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

[Vassilis Tsotras]

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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