Fwd: Reminder - Thesis Defense - April 11, 2022 - Maria Jahja - Sensor Fusion Frameworks for Nowcasting

[Artur Dubrawski]

This can be of interest to many of us.

Artur

---------- Forwarded message ---------
From: Diane Stidle <stidle at andrew.cmu.edu>
Date: Mon, Apr 11, 2022 at 9:22 AM
Subject: Reminder - Thesis Defense - April 11, 2022 - Maria Jahja - Sensor
Fusion Frameworks for Nowcasting
To: ml-seminar at cs.cmu.edu <ML-SEMINAR at cs.cmu.edu>, <jsharpna at ucdavis.edu>


*Thesis Defense*

Date: April 11, 2022
Time: 2:30pm (EDT) (Remote)
PhD Candidate: Maria Jahja
                            (Statistics & Machine Learning PhD)

*Title: Sensor Fusion Frameworks for Nowcasting*
*Abstract:*
A fundamental task in many online time series settings is to estimate the
finalized value of a signal that will only be fully observed at a later
time. The goal in nowcasting is to produce such estimates using
contemporaneous information; this differs from the task of forecasting,
which learns from past data to predict future values. In this thesis, we
study sensor fusion (SF), a sequential nowcasting framework derived from a
process-agnostic Kalman filter (KF), and detail two (mathematically
equivalent) reformulations: first to the standard KF itself via an
augmented measurement space, and then to an equality-constrained regression
problem. We leverage these equivalences to port several established ideas
(e.g., regularization schemes) in regression to dynamical systems.

In settings where only convolved outcomes of the signal can be observed,
several new challenges arise: (i) deconvolution to infer the latent state,
(ii) subsequent uncertainty propagation through SF, and (iii) reconvolution
frameworks to evaluate performance. Towards solving these challenges, we
introduce new methodology to perform and evaluate real-time nowcasting by
deconvolution with specialized regularization techniques, which can prepend
the SF framework. We motivate our work throughout by applications to track
disease activity of influenza and COVID-19 in the United States.

*Thesis Committee:*
Ryan Tibshirani, Chair
Roni Rosenfeld
Valérie Ventura
Larry Wasserman
James Sharpnack (UC Davis)

Zoom Meeting Link:
https://cmu.zoom.us/j/98710500061?pwd=SFJLUGFyMjg5ek9naU44K2tDeW90Zz09
Meeting ID: 987 1050 0061
Passcode: 434421

Link to Draft Document:
https://drive.google.com/file/d/13LD1fIF9dGNDH3mQgX1Au5Vxq4yXLARe/view?usp=sharing

-- 
Diane Stidle
Graduate Programs Manager
Machine Learning Department
Carnegie Mellon Universitystidle at andrew.cmu.edu
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