Master the Future of Public Health:
2026 Machine Learning Workshop
A 3-Day Intensive for Health Professionals and Researchers
DATES: July 8, 9, & 10, 2026
TIME: 9:00 am – 5:00 pm
LOCATION: Levy Library, Annenberg Building, 1468 Madison Ave
Room 11-46 or
Live via Zoom
REGISTRATION DEADLINE: July 1, 2026
REGISTER NOW
Offered by the Department of Public Health in collaboration with the
Department of Environmental Medicine
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.
Learn by Doing. Leave with Results.
Led by Dr. Vishal Midya, PhD, this is a hands-on technical workshop. We bypass the "black box" of AI, teaching you the statistical foundations and the practical coding skills needed to conduct independent research.
- Technical Requirements: While we focus on making ML accessible, this is a "keyboard-on" workshop. Participants should have:
Curriculum Highlights
- Regression & Classification: The foundations of predictive modeling.
- Tree-Based Methods: Master Random Forests and decision trees.
- Supervised Learning: Discover hidden clusters in population health data.
- Feature Engineering: Learn to prepare "messy" real-world data for high-performance models.
Learning Objectives
By the end of this workshop, participants should be able to:
- Build basic machine-learning models to solve public health problems.
- Have an advanced understanding of machine-learning methods, specifically when to use one and when not to use one.
- Understand the statistical underpinning of the models and how they can be modeled to address a public health problem.
- Have the knowledge and skill sets to develop and conduct their machine learning research projects independently.
Registration deadline: July 1
* Funding Support Opportunity: Potential CTSA funding support is available for eligible faculty and post-docs. This requires a separate application and approval by the Dean and Chair for the Department of Public Health. See registration form
Non-Mount Sinai Employee/Staff: $1,200/pp Click here to register and submit payment.
Mount Sinai Current Student special discounted rate: $250/pp. Click here to register and submit payment. *Potential CTSA funding for students may be available. Apply by June 26 (details on registration form)
