Fwd: ML/Duolingo Seminar - David Sontag - June 8, 2021 @ 10:30am

[Artur Dubrawski]

May be quite relevant to many of us.

Artur


---------- Forwarded message ---------
From: Sharon Cavlovich <sharonw at cs.cmu.edu>
Date: Thu, Jun 3, 2021 at 12:33 PM
Subject: ML/Duolingo Seminar - David Sontag - June 8, 2021 @ 10:30am
To: <ml-seminar at cs.cmu.edu>, Will Monroe <monroe at duolingo.com>, Zachary
Lipton <zlipton at cmu.edu>, Andrej Risteski <aristesk at andrew.cmu.edu>, David
Sontag <dsontag at csail.mit.edu>


Please join us for a ML/Duolingo Seminar!

June 8, 2021
10:30am EDT
Zoom Link
<https://cmu.zoom.us/j/97102017174?pwd=RWZ1aE9Qd3pKcXRFWSttVkp2bzBhdz09>

Meeting ID: 971 0201 7174
Passcode: 814851

[image: david_headshot.jpeg]
Speaker:  David Sontag <https://people.csail.mit.edu/dsontag/>, MIT

Title:  Learning Deep Markov Models for Precision Medicine

Abstract:
I present a new approach to learning from temporal data, coupling deep
learning with probabilistic inference. Applied to learning disease
progression models from clinical data, our algorithms learn rich
representations that are capable of answering counterfactual questions
such as which treatment is most appropriate to which patient, providing a
new theoretical framework for precision medicine.

Making valid causal inferences from observational data requires a number of
assumptions to be satisfied. I show how machine learning can be used to
test and explain one of these (overlap) and how machine learning can help
circumvent another (hidden confounding). Along the way, I'll make
connections to recent work on domain adaptation and dataset shift.

Finally, I discuss my vision for the future, where these methods are
scalably used to guide millions of patients' health care. Doing so will
require policy and legislative changes to improve health data collection
and curation, new algorithms for extracting treatment and outcomes from
clinical text, and advances in human-computer interaction to safely and
effectively explain algorithm predictions to patients and providers.

Bio:
David Sontag is an Associate Professor in the Department of Electrical
Engineering and Computer Science (EECS) at MIT, and member of the Institute
for Medical Engineering and Science (IMES) and the Computer Science and
Artificial Intelligence Laboratory (CSAIL). Prior to joining MIT, Dr. Sontag
was an Assistant Professor in Computer Science and Data Science at New York
University from 2011 to 2016, and a postdoctoral researcher at Microsoft
Research New England. Dr. Sontag received the Sprowls award for outstanding
doctoral thesis in Computer Science at MIT in 2010, best paper awards at
the conferences Empirical Methods in Natural Language Processing (EMNLP),
Uncertainty in Artificial Intelligence (UAI), and Neural Information
Processing Systems (NeurIPS), faculty awards from Google, Facebook, and
Adobe,
and a National Science Foundation Early Career Award. Dr. Sontag received a
B.A. from the University of California, Berkeley.

-- 

-- 
Sharon Cavlovich
Senior Department Administrative Assistant | Machine Learning Department

Carnegie Mellon University
5000 Forbes Avenue | Gates Hillman Complex 8215
Pittsburgh, PA 15213
412.268.5196 (office) | 412.268.3431 (fax)
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