Our Mission

The Data Science Institute at Montefiore Einstein aims to transform the increasingly massive amount of data generated from -omics, imaging, electronic health records, mobile devices, and other modern technologies into knowledge, insights, and innovations that drive scientific discovery and improve human health. We serve as an interdisciplinary hub that brings together experts in biostatistics, bioinformatics, epidemiology, health research informatics, and artificial intelligence (AI)/machine learning (ML) to tackle the most pressing and complex analytic challenges in biomedical research today.

As a vibrant hub for interdisciplinary research and education, the institute:

fosters collaborations across departments and disciplines
provides data science training programs and mentorship
develops and applies innovative methods and tools in data science
expands and coordinates data science resources and support across the institution

Our methodological research spans a wide range of areas, including:

integration and analysis of high-dimensional molecular (“-omics”) data
electronic health records data
wearable and mobile health technologies
imaging studies
pragmatic clinical trials
AI/ML approaches for risk prediction, personalized medicine, and drug discovery

Through developing and applying innovative data science methods, we are committed to accelerating research, increasing scientific and clinical impact, and educating the next generation of physicians and scientists.

Upcoming Events

Biostatistics Methodology Journal Club

2nd Monday of the month
12:00PM - 1:00PM | Belfer 1006E

Charlie Hall, Ph.D.
Professor, Department of Epidemiology & Population Health (Biostatistics) and Neurology
May 11, 2026

Data Science Workshop Series

Practical Machine Learning
Instructor: Melissa Fazzari, Ph.D.
Curious how modern machine learning classifiers actually compare?
Use R to preprocess real data, fit Random Forests, penalized regression, gradient boosting, and SuperLearner models, evaluate them with cross-validation, and explore variable importance for interpretability.
May 04, 2026 | 12:00 PM - 2:00 PM

Register

Machine Learning Seminar Series Dept. Of Systems and Computational Biology

* More information about ML Seminar Series available here.

OMICs Club X DSI Bioinformatics Symposium

April 14, 2026 - 9AM-5PM
Location: LeFrak Auditorium/Atrium
Event Details

Bioinformatics Collaborative Group Meetings

All are hybrid at 2PM in Price-451 or via Zoom

Roger Chang, Ph.D.
Assistant Professor, Systems & Computational Biology and Biochemistry
May 12, 2026 | Zoom Link

Srilakshmi Raj, Ph. D.
Assistant Professor, Genetics
June 09, 2026 | Zoom Link

Divij Verma, Ph.D.
Assistant Professor, Department of Pathology and Oncology
June 14, 2026 | Zoom Link

Artificial Intelligence Workshop Series

Prompt Engineering for Healthcare in 2026: From Scaffolding Models to Structuring Thinking
Tuesday, April 14, 2026 | 12–1 PM
Speaker: Carlo Lutz, MD
An introduction to how large language models work and how clinicians can use prompt engineering to improve AI-generated clinical reasoning, moving from scaffolding prompts to structured outputs aligned with clinical workflows.

Coding Smarter: Building Reproducible Biomedical Analysis Pipelines with Codex and Python
Tuesday, May 12, 2026 | 11 AM–12 PM
Speaker: Matthew Gamble, PhD
An introduction to Codex as an AI coding agent that accelerates Python-based biomedical data analysis, with live demonstrations of how AI-assisted coding can streamline pipelines and visualizations while keeping workflows reproducible and scientifically rigorous.

AI with Care: Integrating Ethics and Innovation
Thursday, June 4, 2026 | 12–1 PM
Speaker: Elizabeth Chuang, MD
An overview of key ethical challenges in healthcare AI development and deployment, with practical frameworks for evaluating AI solutions and best practices for responsible innovation across the AI lifecycle.

AI Use for Scholarly Communication
Monday, June 15, 2026 | 12–1 PM
Speaker: Aurelia Minuti, MLS
A practical guide to using AI tools for scholarly writing and literature reviews, covering the benefits and limitations of these tools, proper citation of AI-generated content, and current journal and publishing guidelines.

Trust, Verify, and Iterate: Human Judgment in AI-Assisted Research
Thursday, June 25, 2026 | 11 AM–12 PM
Speaker: David Shechter, PhD
A hands-on exploration of how scientists can productively use generative AI in research while maintaining critical oversight, with strategies for identifying common failure modes like hallucination and overconfidence.

AI as a Research Partner: A Graduate Student's Perspective on Daily Use
Thursday, July 16, 2026 | 11 AM–12 PM
Speaker: Maxwell Horton
A live demonstration of how generative AI can support the full graduate research workflow — from experimental design to result interpretation — while keeping the focus on scientific thinking and innovation.

Contact Us

Feedback, questions, and suggestions about this site can be emailed to:

datascience@einsteinmed.edu

Albert Einstein College of Medicine
Jack and Pearl Resnick Campus
1300 Morris Park Avenue
Bronx, NY 10461