Fwd: [Location Change to NSH 4305] RI Ph.D. Thesis Defense: Mononito Goswami

[Artur Dubrawski]

Mono's defense will happen closer to the Auton Wing.

See you all there tomorrow!
(4:30pm, NSH 4305)

Cheers
Artur

---------- Forwarded message ---------
From: Suzanne Muth <lyonsmuth at cmu.edu>
Date: Wed, Apr 16, 2025 at 9:45 AM
Subject: [Location Change to NSH 4305] RI Ph.D. Thesis Defense: Mononito
Goswami
To: RI People <ri-people at andrew.cmu.edu>


This talk will take place in *NSH 4305* on April 17th beginning at 4:30pm.


> *RI Events Calendar Posting
> <https://www.ri.cmu.edu/event/towards-pragmatic-time-series-intelligence-2/>*
>
> *Date:* Thursday, 17 April 2025
> *Time:* 4:30 p.m. (ET)
> *Location:* NSH 4305
> *Zoom: Link
> <https://cmu.zoom.us/j/91581272900?pwd=DCOr0EFMMTuAncqaeJvNJgw9W8Xsqb.1&jst=2>*
>
> *Type:* Ph.D. Thesis Defense
> *Who:* Mononito Goswami
> *Title:* Towards Pragmatic Time Series Intelligence
>
> *Abstract:*
> This thesis aims to democratize time series intelligence by making
> advanced modeling capabilities accessible to users without specialized
> machine learning knowledge. We pursue this goal through three complementary
> contributions that build foundation models, improve our understanding of
> them, and address challenges emerging in their practical use.
>
> We start by introducing MOMENT, the first family of open source time
> series foundation models capable of performing well on a variety of tasks
> on data from diverse domains with minimal supervision. We extend these
> models to handle long multivariate contexts and integrate multimodal data,
> enabling their application to complex real-world scenarios where
> traditional approaches fall short.
>
> Next, we examine what these foundation models learn by investigating their
> compositional reasoning abilities, representation structures, and encoded
> concepts. We identify practical insights that improve both our
> understanding of the models and their performance.
>
> Then, we tackle deployment challenges by developing methods to learn from
> distributed unlabeled data, assess label quality, and select robust models
> when labeled data is scarce. Lastly, we explore how Large Language Model
> agents can automate the time series intelligence engineering process, using
> open-source tools and tools developed in this thesis.
>
> We demonstrate the utility of our methodology in clinical settings, where
> time series data is plentiful and where modeling of it can be impactful. We
> conclude that specialized foundation models, combined with practical tools
> supporting their real-world deployment, can substantially advance time
> series intelligence and yield practical solutions of societal importance.
>
> *Thesis Committee Members:*
> Artur Dubrawski (Chair)
> Jean Oh
> Barnabás Póczos
> Frederic Sala (University of Wisconsin-Madison)
> Laurent Callot (Amazon)
>
> Draft of the Thesis Document
> <https://drive.google.com/drive/folders/1yzdjxg0Koj92msch3kRxJDemi29E9Snj?usp=sharing>
>
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