[AI Seminar Series] REMINDER: Seminar by Prof. Salil Koner, tomorrow Friday Dec.5th, 12-1pm, MRB Seminar Room

[Vassilis Tsotras]

Reminder for the AI seminar tomorrow; please register below if you plan to
attend.
Best,
V. Tsotras

---------------------------------
On Sun, Nov 30, 2025 at 11:00 PM Vassilis Tsotras <vassilis.tsotras at ucr.edu>
wrote:

> The next AI Seminar will be on Friday December 5th, 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-1976430395517
>
> The talk will be given by *Prof. Salil Koner*, Department of Statistics,
> UCR
>
> TITLE: Reliable Uncertainty Quantification for Continuous-time Signals
> Across Diverse Sampling Regimes
>
>
> ABSTRACT:
> Continuous-domain signals, curves, and trajectories sampled over time or
> space—arise routinely in modern sensing, health, and human–computer
> interaction applications. Such a type of data is also called Functional
> data in the statistical literature. Despite their prevalence, providing
> reliable uncertainty quantification for predictions such as
> continuous-domain data remains challenging, especially when observations
> are irregular or sparsely sampled. We introduce a general conformal
> prediction framework that produces finite-sample valid prediction sets for
> functional data under any sampling regime, including dense and sparse
> designs.
>
> Our approach uses a Karhunen–Loève (KL) representation to model
> non-stationary domain variability and introduces an efficient inversion
> procedure that converts conformal prediction sets into interpretable
> prediction bands with lower and upper envelopes. We establish theoretical
> guarantees for validity and coverage, and show that the prediction band
> achieves provably correct coverage under mild conditions for densely
> observed signals, and approximate coverage for sparsely observed data.
> Extensive simulations demonstrate strong empirical coverage and competitive
> predictive accuracy across both sparse and dense settings. Applications to
> three widely used datasets—fractional anisotropy profiles from multiple
> sclerosis patients and longitudinal measurements from Alzheimer’s disease
> cohorts—highlight the method’s robustness and ease of interpretation.
>
> Bio:
> Salil Koner is an Assistant Professor of Statistics at the University of
> California, Riverside. His research develops principled statistical and
> machine learning methods for complex structured data, with a focus on
> continuous-domain signals, high-dimensional longitudinal data, and
> uncertainty quantification with application to biomedical data. Dr. Koner
> graduated with a Ph.D. in Statistics from North Carolina State University.
> Before joining UCR, he was a Postdoctoral Associate in the Department of
> Biostatistics at Duke University. His statistical research has been
> supported by fellowships and has been published at major venues in
> statistics, biomedicine, and data science.
>
> ------------------------------------
> 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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