[Sds-seminars] This Monday: Adji Bousso Dieng on "Deep Probabilistic Graphical Modeling"

[Dan Spielman]

S&DS|CS JOINT SEMINAR, ADJI BOUSSO DIENGColumbia University
Deep Probabilistic Graphical Modeling
Monday, March 02, 20204:00PM to 5:00PM
YINS see map
<http://maps.google.com/?q=17+Hillhouse+Avenue%2C+Rm.+328%2C+New+Haven%2C+CT%2C+06511%2C+us>

17 Hillhouse Avenue, Rm. 328
New Haven, CT 06511
Website <https://adjidieng.github.io/>
Information and Abstract:

Deep learning (DL) is a powerful approach to modeling complex and large
scale data. However, DL models lack interpretable quantities and calibrated
uncertainty. In contrast, probabilistic graphical modeling (PGM) provides a
framework for formulating an interpretable generative process of data and a
way to express uncertainty about what we do not know. How can we develop
machine learning methods that bring together the expressivity of DL with
the interpretability and calibration of PGM to build flexible models
endowed with an interpretable latent structure that can be fit efficiently?
I call this line of research deep probabilistic graphical modeling (DPGM).
In this talk, I will discuss my work on developing DPGM for text data. In
particular, I will show how DPGM enables flexible and interpretable topic
modeling at large scale, unlocking several known challenges. Furthermore, I
will describe how we can account for both local and long-range context,
under the DPGM framework, to build a flexible sequential document model
that leads to state-of-the-art performance on a downstream document
classification task.

*Bio:* Adji Bousso Dieng is a PhD Candidate at Columbia University where
she is jointly advised by David Blei and John Paisley. Her research is in
Artificial Intelligence and Statistics, bridging probabilistic graphical
models and deep learning.
Dieng is supported by a Dean Fellowship from Columbia University. She won a
Microsoft Azure Research Award and a Google PhD Fellowship in Machine
Learning. She was recognized as a rising star in machine learning by the
University of Maryland.
Prior to Columbia, Dieng worked as a Junior Professional Associate at the
World Bank. She did her undergraduate studies in France where she attended
Lycee Henri IV and Telecom ParisTech–France’s Grandes Ecoles system. She
spent the third year of Telecom ParisTech’s curriculum at Cornell
University where she earned a Master in Statistics.
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