Google, San Francisco
chiragn at cs dot cmu dot edu
Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes
Chirag Nagpal, Vedant Sanil and Artur Dubrawski.
Participatory Systems for Personalized Prediction
Hailey Joren, Chirag Nagpal, Katherine Heller and Berk Ustun.
NeurIPS - Neural Information Processing Systems Conference 2023
Counterfactual Phenotyping with Censored Time-to-Events
Chirag Nagpal, Mononito Goswami, Keith Dufendach and Artur Dubrawski.
KDD - ACM Conference on Knowledge Discovery and Data Mining 2022
Deep Cox Mixtures for Survival Regression.
Chirag Nagpal, Steve Yadlowsky, Negar Rostamzadeh and Katherine Heller.
MLHC - Machine Learning for Healthcare 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.
CHIL - 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.
JBHI - 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]
Dynamically Personalized Detection of Hemorrhage.
Chirag Nagpal, Xinyu Li, Michael Pinsky, and Artur Dubrawski.
MLHC - Machine Learning for Healthcare Conference 2019
auton-survival: an open-source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data
Chirag Nagpal, Willa Potosnak and Artur Dubrawski.
MLHC - Machine Learning for Healthcare 2022
[tech-report] [official-blog] [code]
Deep Parametric Time-to-Event Regression with Time-Varying Covariates
Chirag Nagpal, Vincent Jeanselme and Artur Dubrawski.
AAAI Spring Symposium on Survival Prediction 2021
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
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
Interpretable Treatment Prioritization Rule Identifies Diabetic Patients Who Can Benefit from Prompt Coronary Revascularization.
ACC '23 [pdf]
Novel Machine Learning Technique Defines Patients Who Benefit from Off-Pump CABG
STS Coronary '22 [pdf]
Phenogrouping of hemorrhagic trauma patients using latent variable machine learning.
ISICEM '22 [pdf]
Accuracy of identifying venous thromboembolism by administrative coding compared to manual review.
|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.