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. Data Science Pilot Project Funding Opportunity The DSI is pleased to announce a Pilot Project Funding Opportunity to support the development and application of innovative data science methodologies. Application Deadline: May 1, 2026. Learn More 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 Working Group MeetingsAll 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 Speaker TBD June 09, 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. Register 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. Register 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. Register 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. Register 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. Register 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. Register 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
