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.

Become a Member of the DSI

Become part of Einstein's multidisciplinary data science community. Whether you are a data science expert or apply data science methods in your research, DSI membership connects you with collaborators, resources, and opportunities to advance data-driven discovery.

Why Join DSI?

Connect with investigators and data scientists across disciplines
Compete for DSI-sponsored pilot funding opportunities
Receive priority access to seminars, workshops, training, and data science resources
Increase your visibility through DSI programs and communications

Membership Options

Full Member — For faculty whose primary expertise is in data science (including biostatistics, bioinformatics, health research informatics, or AI/ML) and who actively contribute to research and education in these areas. Full Members help shape DSI priorities, mentor trainees, are featured on the DSI website, and are expected to actively participate in DSI activities and acknowledge DSI affiliation in relevant publications.

Affiliate Member — For faculty and staff who use or are interested in applying data science methods in their research or professional work. Affiliate Members are encouraged to participate in DSI seminars, educational programs, and collaborative activities with a flexible level of engagement.

Ready to Join?
Help strengthen Einstein's data science community by becoming a DSI member.

Events

Biostatistics Methodology Journal Club

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

Upcoming

Jee-Young Moon, Ph.D.
Assistant Professor, Department of Epidemiology & Population Health
October 12, 2026

Nan Xue, Ph.D.
Professor, Department of Epidemiology & Population Health
November 9, 2026

Ryung Kim, Ph.D.
Professor, Department of Epidemiology & Population Health Profssor
January 11, 2027

Angel Garcia de la Garza, Ph.D.
Assistant Professor, Department of Epidemiology & Population Health
February 8, 2027

Wenzhu Mowrey, Ph.D.
Associate Professor, Department of Epidemiology & Population Health
March 8, 2027

Yungtai Lo Ph.D.
Professor, Department of Epidemiology & Population Health
April 12, 2027

Juan Lin, Ph.D.
Assistant Professor, Department of Epidemiology & Population Health
May 10, 2027

Previous

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

Kith Pradhan, Ph.D.
Assistant Professor, Department of Epidemiology & Population Health
April 13, 2026

Tao Wang, M.D., Ph.D.
Professor, Department of Epidemiology & Population Health (Biostatistics)
Director, Genetic and Genomics Data Analysis Unit
March 09, 2026

Sam Xie, Ph.D.
Research Assistant Professor, Department of Epidemiology & Population Health
February 09, 2026

Data Science Workshop Series

Upcoming

Introduction to Cloud Computing and Linux Environment for Life Scientists: Session 1 - Linux Basics
Instructor: Harrison Ho, Ph.D.
New to Linux or working on the Einstein high-performance computing cluster?
Learn how to access and navigate the Einstein cluster, manage files and directories, use essential Linux commands and pipelines, edit text with vi, and get started with Bash scripting for task automation.
September 29, 2026 | 12:00 PM - 2:00 PM

Introduction to Cloud Computing and Linux Environment for Life Scientists: Session 2 - Cloud Computing and Job Management
Instructor: Harrison Ho, Ph.D.
Ready to take the next step with high-performance computing at Einstein?
Build on the Linux fundamentals from Session 1 while learning how to manage software environments with Conda, deploy applications on the Einstein cluster, and use SLURM to efficiently schedule and manage computational jobs.
October 6, 2026 | 12:00 PM - 2:00 PM

Previous

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

Data Visualization in R
Instructor: Kith Pradhan, Ph.D.
Not sure how to turn your data into clear, compelling graphics?
Build scatterplots, boxplots, and bar charts step-by-step in ggplot2 while mastering layering, readable syntax, and simple customizations to create polished, effective visualizations.
April 27, 2026 | 12:00 PM - 2:00 PM

Data Wrangling
Instructor: Angel Garcia de la Garza, Ph.D.
Overwhelmed by messy datasets?
Learn how to transform, reshape, and summarize complex data step-by - step using tidyverse in R, applying tidy data principles and reproducible workflows to prepare datasets for analysis and visualization.
April 20, 2026 | 1:00 PM - 3:00 PM

Machine Learning Seminar Series Dept. Of Systems and Computational Biology

Previous

Tai-Sing Lee, Ph.D.
Professor, Computer Science Department
Carnegie Mellon University
March 02, 2026 | Price Center Rm. 35*

Lyle Muller, Ph.D.
Assistant Professor, Department of Mathematics
Western Institute for Neuroscience
February 02, 2026 | Virtual Session

* More information about ML Seminar Series available here.

Bioinformatics Collaborative Group Meetings

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

Upcoming

Libusha Kelly, Ph.D.
Associate Professor, Departments of Systems, Computational Biology, Microbiology and Immunology
Tuesday, October 13, 2026

Maxwell Horton, Ph.D Student
Sidoli Lab
Tuesday, November 10, 2026

David Entenberg, Ph.D.
Associate Professor, Department of Pathology
Vladislav Verkhusha, Ph.D.
Professor, Department of Genetics
Tuesday, December 8, 2026

Lindsay LaFave Ph.D.
Assistant Professor, Department of Cell Biology
Tuesday, February 9, 2027

Matthew Gamble, Ph.D.
Professor, Departments of Molecular Pharmacology and Cell Biology
Tuesday, March 9, 2027

Previous

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

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

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

Zhengdong Zhang, Ph.D.
Professor, Genetics
April 17, 2026

William Chang, Ph.D.
Staff Scientist, Department of Ophthalmology & Visual Sciences
April 07, 2026

Tao Wang, M.D., Ph.D.
Co-Lead for Bioinformatics, DSI
March 10, 2026

Artificial Intelligence Workshop Series

Previous

AI as a Research Partner: A Graduate Student's Perspective on Daily Use
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.
July 16, 2026 | 11 AM–12 PM

Register

Trust, Verify, and Iterate: Human Judgment in AI-Assisted Research
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.
June 25, 2026 | 11 AM–12 PM

AI Use for Scholarly Communication
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.
June 15, 2026 | 12–1 PM

AI with Care: Integrating Ethics and Innovation
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.
June 04, 2026 | 12–1 PM

Coding Smarter: Building Reproducible Biomedical Analysis Pipelines with Codex and Python
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.
May 12, 2026 | 11 AM–12 PM

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