Machine Learning in Public Health: A 3-day Intensive Workshop

July 8, 9 & 10, 2026 | TIME: 9:00 am – 5:00 pm 
Location: The Levy Library, Annenberg Building, Room 11-46 
Icahn School of Medicine at Mount Sinai, New York

Register Here

Data is Everywhere. Insight is Rare.

From predicting disease outbreaks to identifying patterns in environmental exposures, Machine Learning (ML) is no longer a "future technology"—it is an essential tool for the modern health researcher. This workshop bridges the gap between raw data and actionable public health solutions.

Why this workshop?

• The Power of Prediction: Move beyond traditional statistics to build models that forecast outcomes.
• Competitive Advantage: Gain the technical literacy required for high-impact publishing and modern research grants.
• Transdisciplinary Application: Learn use cases ranging from disease diagnosis to climate-related health risks.

This workshop is taught by three Mount Sinai researchers and clinician-scientists actively publishing in machine learning, causal inference, and natural language processing. You're learning from practitioners, not lecturers.

Technical Requirements: You should have some proficiency in R and R Studio.

While we focus on making ML accessible, this is a "keyboard-on" workshop. Participants should have:

• Software Proficiency: A working knowledge of R and R Studio.

• Foundational Knowledge: A basic understanding of epidemiology and biostatistics.
• Equipment: A laptop with R andLed by Mount Sinai Faculty Expert

THREE-DAY CURRICULUM

Over three days, you'll progress from foundational statistical ML through specialized applications in public health research.

Day 1: Foundations & Core Methods

Build your technical foundation in regression, classification, and regularization. Learn practical approaches to variable selection, model evaluation, and feature engineering. Work with real-world health datasets in R from the first session.

Topics: Introduction to ML in Public Health · Regression & Classification · Variable Selection & Regularization · Support Vector Machines · Resampling & Feature Engineering · Ensemble Methods

Day 2: Advanced Methods & Domain Applications

Deepen your methodological toolkit with tree-based models, unsupervised learning, and specialized applications. Learn how machine learning intersects with causal inference — a critical skill for health research where understanding why matters as much as prediction.

Topics: Tree-Based Methods & Feature Importance · Unsupervised Learning · Machine Learning in Causal Inference (Theory & Practice) · Introduction to Mixture Models

Day 3: Emerging Methods & Capstone

Explore cutting-edge applications of machine learning in public health, including natural language processing and large language models. Conclude with hands-on risk modeling and a group discussion on future directions in ML for health.

Topics: Natural Language Processing in Public Health · Large Language Models in Health Research · Neural Networks Preliminaries · Risk Scoring & Thresholding · Capstone Discussion: Challenges & Future Directions