School of Computer Science,
Carnegie Mellon University
6601 Gates Hillman Center
chiragn at cs dot cmu dot edu
I am a Doctoral Student in the School of Computer Science at Carnegie Mellon University. I am part of the Auton Lab and advised by Prof. Artur Dubrawski. I graduated with a Masters from the same department before starting the PhD.
I am interested in challenges of applying Data Science and Machine Learning in Healthcare, such as, Model Interpretability, Causal Inference and Uncertainity Estimation.
This summer I am working with Prof. Katherine Heller at Google Brain.
During my PhD, I have worked as a Summer Associate in Prof. Manuela Veloso's new AI Research Team within the Corporate and Investment Banking division of J.P. Morgan. I also spent a summer working with Kush R. Varshney and others at IBM Research's Thomas J. Watson Research Centre as a Science for Social Good Fellow.
I try to be a good citizen of the School of Computer Science by serving on the Dean's PhD Students Advisory Committee and co-chairing the SCS Dec/5.
Deep Parametric Time-to-Event Regression with Time-Varying Covariates
Chirag Nagpal, Vincent Jeanselme and Artur Dubrawski.
AAAI Spring Symposium on Survival Prediction 2021
Deep Cox Mixtures for Survival Regression.
Chirag Nagpal, Steve Yadlowsky, Negar Rostamzadeh and Katherine Heller.
Machine Learning for Health Conference 2021
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines.
Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, M. Shekhar, S. E. Berger, S. Das and Kush Varshney.
ACM Conference on Health, Inference and Learning 2020 (Spotlight Presentation)
[pdf] [code] [notebook]
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks.
Chirag Nagpal, Xinyu Li, and Artur Dubrawski.
IEEE Journal of Biomedical and Health Informatics
(and) NeurIPS Machine Learning for Health Workshop 2019 (Spotlight Presentation, top 3% of over 300 papers)
[pdf] [talk] [code]
Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise.
Chirag Nagpal, Robert E. Tillman, Prashant P. Reddy, and Manuela M. Veloso.
NeurIPS Robust AI in Financial Services Workshop 2019
Dynamically Personalized Detection of Hemorrhage.
Chirag Nagpal, Xinyu Li, Michael Pinsky, and Artur Dubrawski.
Machine Learning for Healthcare Conference 2019
Trust, Perceptions, and Effects of News Sources and Social Media:
Chirag Nagpal, A.S. Chonker, V. Nagpal, and Artur Dubrawski.
A Data Driven Study of the Recent Unrest in Kashmir.
Data for Policy 2019
An Entity Resolution Approach to Isolate Instances of Human Trafficking Online.
Chirag Nagpal, Kyle Miller, Benedikt Boecking, and Artur Dubrawski.
Bloomberg Data for Good Exchange 2017
Nonlinear Semi-Parametric Models for Survival Analysis.
Chirag Nagpal, R. Sangave, A. Chahar, P. Shah, Artur Dubrawski, and Bhiksha Raj.
|2020||NeurIPS Machine Learning for Health Workshop (ML4H)|
|2020||Neural Information Processing Systems (NeuRIPS)|
|2020||ACL Student Research Workshop (ACL-SRW)|
|2020 || Machine Learning for Healthcare Conference (MLHC)|
|2020|| ICLR ML in Real-Life Workshop (ML-IRL)
|2020|| ACM Conference on Health, Inference and Learning (CHIL)|
|2019|| NeurIPS Machine Learning for Health Workshop (ML4H)|
10-708 Probabilistic Graphical Models, Fall 2020
TAing for Prof. Pradeep Ravikumar
11-761/661 Language and Statistics, Fall 2019
TAing for Prof. Bhiksha Raj
I'm a dormant member of the W3VC, the Carnegie Tech Radio Club; In my past life, I used to make (hack?) stuff.
I enjoy Equitation, playing the Guitar, and Trivia and Quiz contests.
Here's a [list] of all the places I lived in before coming to Pittsburgh.