FREE WEBINAR: Inside MIT xPRO's Machine Learning Online Certificate Program
With Professor Youssef Marzouk, professor in the Department of Aeronautics and Astronautics at MIT, co-director of the CCSE, and director of MIT’s Aerospace Computational Design Laboratory
Live Webinar Date
January 14, 2026
TIME
2 PM ET
Duration
45 Minutes
Includes Q&A Session
RESERVE YOUR SEAT
All you need to know on MIT xPRO's popular online certificate program from MIT's Youssef Marzouk
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Machine learning is reshaping how engineers approach complex problems— but not every challenge requires it. That’s why MIT xPRO developed a two-course online program that takes a practical, hands-on approach to modern computational tools for engineering problem-solving.
In this free webinar, MIT Professor Youssef Marzouk offers an inside look at the Machine Learning, Modeling, and Simulation online certificate program.
In this interactive webinar, Professor Marzouk will:
- Share the timeline of computational science and engineering paradigms
- Explain what’s covered in the popular two-course program
- Walk through the program structure, who should enroll, and the faculty involved
- Highlight how the program is helping engineers bridge the gap between traditional methods and new AI-driven techniques
The session will end with a live Q&A. Register above and you'll receive an email from Zoom with your login instructions.
WHY ATTEND?
Hear directly from an MIT expert about the thinking behind the program and how it addresses real engineering challenges.
Understand what's covered across the two courses, from modeling and simulation to practical uses of machine learning.
Find out who the program is designed for and whether it's the right fit for your professional goals.
Get your questions answered in a live Q&A and engage directly with Professor Marzouk.
MEET YOUR SPEAKER
Youssef Marzouk is a professor in the Department of Aeronautics and Astronautics at MIT and co-director of the CCSE. He is also director of MIT’s Aerospace Computational Design Laboratory.
His research interests lie at the intersection of physical modeling with statistical inference and computation. In particular, he develops methodologies for uncertainty quantification, inverse problems, large-scale Bayesian computation, and optimal experimental design in complex physical systems. His methodological work is motivated by a wide variety of engineering, environmental, and geophysics applications.
ready to start learning now with mit xpro?
MIT xPRO’s Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI is a two-course online certificate designed to give engineers hands-on experience with the computational tools used to model, analyze, and solve complex engineering challenges — using machine learning when appropriate, and traditional methods when they are better suited.