Fwd: RI Ph.D. Thesis Proposal: Mononito Goswami

[Artur Dubrawski]

Team, please mark your calendars for Mononito's upcoming thesis proposal
talk.

Cheers
Artur

---------- Forwarded message ---------
From: Suzanne Muth <lyonsmuth at cmu.edu>
Date: Mon, Apr 22, 2024, 9:47 AM
Subject: RI Ph.D. Thesis Proposal: Mononito Goswami
To: RI People <ri-people at andrew.cmu.edu>


Date: 01 May 2024
Time: 2:00 PM (ET)
Location: NSH 1305

Zoom Link:
https://cmu.zoom.us/j/97119194168?pwd=Uzd4cXFzTHRuZWU1RVJwb1JOMHU0UT09

Type: Ph.D. Thesis Proposal
Who: Mononito Goswami
Title: Towards Pragmatic Time Series Intelligence


Abstract:

The widespread adoption of time series machine learning (ML) models faces
multiple challenges involving data, modeling and evaluation.

*Data.* Modern ML models depend on copious amounts of cohesive and reliably
annotated data for training and evaluation. However, labeled data is not
always available and reliable, and can also be dispersed across different
locations. We propose systematic solutions to making time series data
ML-ready.

*Modeling.* Most current time series ML models are built, trained and
evaluated on individual datasets from a specific application domain. Thus,
to build an effective model for a particular application scenario,
substantial effort, time, and domain expertise are required to develop a
successful task-specific design. We propose to partially address this
limitation by developing large pre-trained foundation models for time
series, to ease development of useful models across diverse application
domains with limited resources, data and labels.

*Evaluation.* Currently, time series models are commonly evaluated using
relatively small, specific and highly tailored benchmarks, which may
obfuscate assessment of their performance. We highlight the gaps in
evaluation techniques and propose addressing the most important of them
through comprehensive, multi-metric assessment.

In summary, this thesis aims to democratize time series artificial
intelligence by simplifying and accelerating development of models, while
improving their performance in real-world application scenarios facing
resource constraints and imperfect data.

Thesis Committee Members:
Artur Dubrawski, Chair
Jean Oh
Barnabas Poczós
Frederic Sala, University of Wisconsin-Madison
Laurent Callot, Amazon


A draft of the thesis proposal document is available at:

https://drive.google.com/drive/folders/1yzdjxg0Koj92msch3kRxJDemi29E9Snj?usp=sharing
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