Our research group sits at the interface of substance use disorder treatment, artificial intelligence, statistics and neuroscience. The overall goal is to leverage new technology to match patients with substance use disorders to optimal treatment. We use both bottom-up (phase I/II clinical trials, single site human subjects research) and top-down (phase III and beyond clinical trial data analytics, electronic medical data and claims databases, mobile devices) methods. Current research projects are in the following areas. Faculty collaborators are at Columbia unless otherwise noted.

1. Addiction Data Science

Individual Level Predictive Modeling of Opioid Use Disorder Treatment Outcome (NIH HEAL Initiative)

This project will develop models to predict individual patient’s risk for relapse (or dropout from treatment) when treated with medications for OUD including methadone, buprenorphine, or extended-release depot naltrexone.

We are currently harmonizing clinical trial datasets from 3 multi-site, NIDA sponsored clinical trials in opioid use disorder and developing a portfolio of projects relating to analytics, harmonization, Common Data Models and related areas.

For more information, see CTN Dissemination Library.

Collaborating Faculty
Daniel Feaster, Ph.D. - UMiami
Ying Liu, Ph.D.
Raymond Balise, Ph.D. - UMiami

2. Individual Level Biomarkers for Substance Use and Co-Morbid Disorders

Electroretinogram as a Novel Dopamine Biomarker

We are developing new biomarkers that can predict treatment responses in tobacco use disorder. In particular, we are focusing on the neural circuit in the retina as a window to measure various neurotransmitters. There is an ongoing pilot study for using electroretinogram, which records electrical signals from the retina in response to light flashes, as a biomarker for dopamine release.

Collaborating Faculty
Diana Martinez, M.D.
Stephen H. Tsang, M.D., Ph.D.
Ragy Girgis, M.D.

Novel Neuroscience-Based Biomarker Development in Addiction

We are part of the Columbia Addiction Biomarkers Workgroup, and are currently developing several new biomarkers including functional near infared spectroscopy (fNIRS), multi-modal eye tracking, quantitative pain measures, and other technologies. The Data Science Research Group primarily assist in developing the statistical and quantitative methods applied to novel biomarker development projects.

Collaborating Faculty
Caroline Arout, Ph.D.
John Mann, M.D.
Elizabeth Sublette, M.D.

3. Methodological Development in Predictive Modeling and Causal Inference in Mental Health

Predictive Modeling of Suicide Behavior in Pharmacotherapy

We are developing new techniques and leveraging data science to make individual level predictions of suicide behavior during the course of treatment of bipolar disorder, major depressive disorder, and co-morbid depression and substance use disorders.

Collaborating Faculty
Hanga Galfalvy, Ph.D.
Maria A. Oquendo, M.D. - UPenn

Causal Inference of Assault and Violence In Psychiatric Emergency Rooms and Beyond

We are developing new methodological approaches to apply analytic tools to electronic medical records at NewYork-Presbyterian Hospital to assess whether assualt and violence in the psychiatric emergency room can be predicted and modeled. This is in collaboration with the NYP Value Institute

Collaborating Faculty
Ryan Lawrence, M.D.
Matthew Oberhardt

4. Interventions and Treatment Research in Addiction Science

Technology Enhanced Smoking Cessation Treatment

We are developing new combined mobile technology driven psychotherapy and psychopharmacological strategies for e-cigarettes. This line of research is primarily in collaboration with the the Columbia STARS clinic, which enrolls participants for novel therapeutics devlopment for many Substance Use Disorders.

Collaborating Faculty
Christina Brezing, M.D.
Frances R Levin, M.D.
John Mariani, M.D.

Participating In Our Research

You may find and contact us to enroll into one of our current studies through
Electroretinogram: a New Human Biomarker for Smoking Cessation Treatment

Results and Code

You may find the most current results and working code at our Github repository.


We are looking for individuals with a strong background in data science and technology devleopment at various training levels both on a part time and full time basis, who are interested in applying their skills to substance abuse treatment and research. Please send an E-mail with your CV and references to


Data Science Quality Improvement

We collaborate with organizations to provide data-driven services (predictive model construction, evaluation, dissemination) for quality improvement and care delivery. In particular we have expertise in behavioral health data analytics as applied to treatment prediction, delivery, claims analysis, quality of care measures, cost benefit analysis, technology development and related areas. We have ongoing collaborations with NewYork-Presbyterian Hospital (Evaluation Core) and New York State Psychiatric Institute to improve mental health delivery. Please contact us directly if you are interested in an evaluation.

Emerging Addiction Science Workshop

This informal workshop series aims to provide a thoughtful and free-flowing forum for fellows and faculty both within the division and in the department to discuss contemporary issues in research in addiction science. The topics covered have been very broad, from molecular biology of addiction to optimal service delivery in treatment. We also routinely invite and welcome outside speakers, both in the tristate area and nationwide to present the most cutting edge research topics of their interest. The aim for the workshop is to build collaborations, strengthen the addiction research community, and refine novel ideas.

2017-2018 speaker schedule

Other Training Services

I developed a short course for training psychiatry residents on the basics of Data Science. The course material is released in public domain in our Github repository, and can be adapted for any number of executive training program to provide basic proficiency of analytics for mental health professionals.

Becoming a Data Savvy Psychiatrist



Phone: 646-776-6144
Fax: 646-774-6111
Office Address:
Room 3624
Unit 66, New York State Psychiatric Institute
1051 Riverside Dr.
New York, NY 10033



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