• Welcome

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



The Phenome Model - JBI Editor's Choice

December, 2015

Rimma Pivovarov, Adler J. Perotte, Edouard Grave, John Angiolillo, Chris H. Wiggins, Noémie Elhadad. Learning Probabilistic Phenotypes from Heterogeneous EHR Data. Journal of Biomedical Informatics 2015. bib pdf

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The Survival Filter - UAI

July, 2015

Rajesh Ranganath, Adler Perotte, Noemie Elhadad, and David M. Blei. The Survival Filter: Joint Survival Analysis with a Latent Time Series. UAI 2015. bib pdf


Predicting Chronic Kidney Disease Progression - JAMIA Journal Club

July, 2015

Adler Perotte, Rajesh Ranganath, Jamie S Hirsch, David Blei, Noémie Elhadad. Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis. J Am Med Inform Assoc. 2015 Jul;22(4):872-80. bib pdf



Observational Data and Machine Learning

As an Assistant Professor at Columbia's Medical Center, 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 2015: 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.

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 2016: 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


Guillaume David - Postdoctoral Research Scientist

Amelia Averitt - PhD Candidate

Aras Curukcu - High School Intern

Copyright © 2015 Adler Perotte. All rights reserved.