I'm Adler Perotte, an assistant professor in the Department of Biomedical Informatics at Columbia University.



The Counterfactual χ-GAN

January, 2020

Averitt A, Vanitchanant, N, Ranganath R, Perotte A. "The Counterfactual χ-GAN." arXiv preprint arXiv:2001.03115. 2020 Jan 9. bib pdf


Characterizing non-heroin opioid overdoses using electronic health records

December, 2019

Averitt AJ, Slovis BH, Tariq AA, Vawdrey DK, Perotte AJ. Characterizing non-heroin opioid overdoses using electronic health records. JAMIA Open. 2019 Nov 26. bib pdf


Deep Vision: Learning to Identify Renal Disease With Neural Networks

July, 2019

Pavinkurve NP, Natarajan K, Perotte AJ. Deep Vision: Learning to Identify Renal Disease With Neural Networks. Kidney International Reports. 2019 Jul;4(7):914. bib pdf

Density Image

Multiple Causal Inference with Latent Confounding

March, 2019

Ranganath R, Perotte A. "Multiple causal inference with latent confounding." arXiv preprint 2018. pdf


Phenotype inference with Semi-Supervised Mixed Membership Models

December, 2018

Rodriguez V, Perotte A. "Phenotype inference with Semi-Supervised Mixed Membership Models." NeurIPS Machine Learning for Health Workshop 2018 Dec 7. pdf


Deep Survival Analysis: Missingness and Nonparametrics

August, 2018

X Miscouridou, A Perotte, N Elhadad, R Ranganath. "Deep survival analysis: Missingness and Nonparametrics." In Machine Learning for Healthcare Conference, 244-256. 2018. bib pdf



Observational Data and Machine Learning

I work on prediction and analysis of electronic health record data using existing and novel probabilistic methods.

I am interested in developing methods that combine disparate sources of data, such as clinical notes, laboratory values, medications, procedures, and billing codes to predict things such as chronic kidney disease progression or analyze data to identify side-effects that were previously unknown.

Wearable Medical Diagnostics and Sensors

As an extention of my work in analyzing electronic health record data, I am also interested in developing new sources of data to improve our ability to predict and analyze.

I am interested in developing methods that leverage data from mass spectrometry and light spectroscopy to better characterize an individual's current state of health and predict their future state of health.



Fall 2018-present: Computational Methods (BINF G4002)

This course is targeted to biomedical scientists developing a broad understanding of computational methods applicable in biomedicine. This is a fast-paced, technical course covering a broad range of topics including: Density estimation, regression, classification, deep learning, probabilistic graphical models, clustering, dimensionality reduction, time series models, statistical NLP, networks, hypothesis testing, causal inference, imputation, and association rule mining. Students are expected to read technical texts carefully, participate actively in lecture discussion, and develop hands-on skills in labs involving real-world biomedical and health datasets.

Spring 2016: Readings in Biomedical Informatics - Probabilistic Graphical Models for Biomedical Informatics (BINF 8001)

This course is a reading course targeted towards biomedical scientists interested in developing in-depth knowledge of Bayesian statistics and the graphical modeling framework. This is a fast-paced course covering the fundamentals of probabilistic graphical modeling theory, exponential families, model design, latent variable models, the expectation maximization algorithm, and various methods for inference including Markov chain Monte Carlo methods and variational methods. This course will involve several programming assignments and a final project.

Fall 2015-Fall 2016: Acculturation to Programming and Statistics (BINF 4000)

This course is targeted for biomedical scientists looking for working knowledge of programming and statistics. This is a fast-paced, hands-on course covering the following topics: programming basics in Python, probabilities, elements of linear algebra, elements of calculus, and elements of data analytics. Students are expected to learn lecture material outside of the classroom and focus on labs during class. All labs revolve around real-world biomedical and health datasets.

Fall 2016-Fall 2019: Biomedical Informatics Seminar Series (BINF 4099)

Weekly seminar series with invited speakers, student research talks, and journal clubs. View the seminar schedule here.

Curriculum Vitae

For a PDF version, please click here.

For links to my publications, please see my Google Scholar page.

Adler Perotte

Department of Biomedical Informatics
Columbia University Medical Center
622 West 168th Street. PH20
New York, New York 10032


Lab Members


Amelia Averitt - PhD Candidate

Shreyas Bhave - PhD Student

Victor Rodriguez - PhD Student

Katherine Schlosser - Masters

Aurnov Chattopadhyay - Undergraduate

Francesco Grechi - Undergraduate

Jason Ping - High School


Pierre Elias - Clinical Fellow


Peter Bullen - PhD Candidate, Applied Math and Applied Physics

Joongheum (PJ) Park - Clinical Informatics Fellow

Guillaume David - Postdoctoral Research Scientist

Anando Sen - Postdoctoral Research Scientist

Natnicha (Numfah) Vanitchanant - Masters

Liana Tascau - Masters

Aras Curukcu - High School Intern

Copyright © 2020 Adler Perotte. All rights reserved.