Fwd: Today, 7/9: LTI Summer Seminar Series Presents: Chirag Nagpal 9.30 AM

[Chirag Nagpal]

Hello

Perhaps this is a bit late and a lot of you would be already familiar with
this work, but I would be presenting my research *TODAY at 9.30 AM* on
Counterfactual Estimation and Subgroup Discovery for Causal Inference for
the benefit of the new incoming students at LTI.

I will also be talking a bit about Survival Analysis if time permits.

Do join if it is of interest....

The zoom link is https://cmu.zoom.us/j/96306773691

*Note: TODAY at 9.30 AM*

Thanks

Chirag

---------- Forwarded message ---------
From: Kate Schaich <kschaich at andrew.cmu.edu>
Date: Wed, Jul 8, 2020 at 9:03 AM
Subject: Tomorrow, 7/9: LTI Summer Seminar Series Presents: Chirag Nagpal
and Shikhar Vashishth
To: <incoming-students at cs.cmu.edu>, LTI Faculty Core <
lti-faculty-core at cs.cmu.edu>, <lti-students at cs.cmu.edu>, <
fall20-msaii at lists.andrew.cmu.edu>, <mcds-newstudents at cs.cmu.edu>, <
miis-2020 at mailman.srv.cs.cmu.edu>


Greetings all,

The LTI is proud to announce tomorrow's Summer Seminar presenters:

*Chirag Nagpal, "Interpretable Subgroup Discovery in Treatment Effect
Estimation with Application to Opioid Prescribing Guidelines"*

*Abstract: *The dearth of prescribing guidelines for physicians is one key
driver of the current opioid epidemic in the United States. In this work,
we analyze medical and pharmaceutical claims data to draw insights on
characteristics of patients who are more prone to adverse outcomes after an
initial synthetic opioid prescription. Toward this end, we propose a
generative model that allows discovery from observational data of subgroups
that demonstrate an enhanced or diminished causal effect due to treatment.
Our approach models these sub-populations as a mixture distribution, using
sparsity to enhance interpretability, while jointly learning nonlinear
predictors of the potential outcomes to better adjust for confounding. The
approach leads to human-interpretable insights on discovered subgroups,
improving the practical utility for decision support. Paper Link:
 https://arxiv.org/abs/1905.03297 <https://arxiv.org/abs/1905.03297>

*Bio: *Chirag is a 2nd Year PhD student (+MLT) student at LTI researching
Machine Learning in Healthcare. His interests include Graphical Models and
their applications in Survival Analysis, Causal Inference, and
Uncertainty Estimation. During his PhD, he has been a Science for Social
Good Fellow at IBM Research and a Summer Associate in JPMorgan AI Research.
This summer he is remotely interning at Google Brain and Google Health.
Personal Website: www.cs.cmu.edu/~chiragn

*Shikhar Vashishth, "Improving Medical Entity Linking with Semantic Type
Prediction"*

*Abstract: *Medical entity linking is the task of identifying and
standardizing medical concepts referred to in an unstructured text. Most of
the existing methods adopt a three-step approach of (1) detecting mentions,
(2) generating a list of candidate concepts, and finally (3) picking the
best concept among them. In this paper, we probe into alleviating the
problem of overgeneration of candidate concepts in the candidate generation
module, the most under-studied component of medical entity linking. For
this, we present MedType, a fully modular system that prunes out irrelevant
candidate concepts based on the predicted semantic type of an entity
mention. We incorporate MedType into five off-the-shelf toolkits for
medical entity linking and demonstrate that it consistently improves entity
linking performance across several benchmark datasets. To address the
dearth of annotated training data for medical entity linking, we present
WikiMed and PubMedDS, two large-scale medical entity linking datasets, and
demonstrate that pre-training MedType on these datasets further improves
entity linking performance. We make our source code and datasets publicly
available for medical entity linking research.

*Bio: *Shikhar Vashishth is a Postdoctoral Researcher at Language
Technologies Institute, Carnegie Mellon University. Currently, working in
the field of biomedical natural language processing under Prof. Carolyn
Rose. Previously, he completed his Ph.D. from the Indian Insitute of
Science under the guidance of Partha Pratim Talukdar, Chiranjib
Bhattacharyya, and Manaal Faruqui. His thesis topic was on Neural Graph
Embedding Methods for Natural Language Processing. Shikhar has been a
recipient of the prestigious Google Ph.D. Fellowship and has interned at
Google Research and Microsoft. He completed his graduation from BITS
Pilani, Pilani in 2016. Webpage: http://shikhar-vashishth.github.io/

Presentations will begin promptly at 9am Eastern Time.

Slides for previous presentations can be found here:
https://lti.cs.cmu.edu/intranet/lti-summer-seminar-2020-slide-decks

We hope to see you there!

Kate Schaich
Academic Program Manager, MLT
Language Technologies Institute
Carnegie Mellon University School of Computer Science
GHC 6719
She/Her
*T:  412-268-4788*


-- 

*Chirag Nagpal* PhD Student, Auton Lab
School of Computer Science
Carnegie Mellon University
cs.cmu.edu/~chiragn
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