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

[Vassilis Tsotras]

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.

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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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