Join us for a virtual open house to learn more about the MIT Professional Education AI and ML: Leading Business Growth program.
The 21-week live online program is designed to equip professionals with the ability to identify, evaluate, implement, and scale AI opportunities that create measurable business value. With a structured learning experience, this program brings together learning from experts and learning bydoing, critical to developing the intuition and strategic judgment needed to guide AI and ML initiatives at scale.
The session will be led by Devavrat Shah, Professor of Electrical Engineering and ComputerScience, MIT, and Jehangir Amjad, VP of Engineering, AI, Ikigai Labs. The program faculty will share an in-depth overview of the no-code approach of the program used to build the foundational knowledge and technical instincts needed to lead enterprise AI.
Agenda for the session
- Learning journey and curriculum
- Networking and career application
- Measurable outcomes
- Live Q&A session
About Speakers
Devavrat Shah
Professor of Electrical Engineering and Computer Science,MIT
Devavrat Shah is a Professor of Electrical Engineering and Computer Science at MIT, and a member of the Laboratory for Information and Decision Systems and the Operations Research Center. His research explores the theory of large, complex networks, spanning network algorithms, stochastic networks, and large-scale statistical inference. Shah earned the first ACM SIGMETRICS Rising Star Award in 2008 for his contributions to network scheduling algorithms, and later received the 2010 Erlang Prize from INFORMS for outstanding work in applied probability.
Jehangir Amjad
VP of Engineering, AI, Ikigai Labs
Lecturer both at Stanford (2023-now) and CSAIL at MIT (2018-2022), teaching courses in Machine Learning (CS 229, 6.036/6.390) and Artificial Intelligence (CS 221). He was awarded the Jerome H. Saltzer Award for Excellence in Teaching at MIT (2022). Jehangir joined Ikigai from Google where he was a Software Engineer (2018-2024) helping build and deploy distributed pipelines for statistical inference to help make Google's global networking infrastructure increasingly more robust and reliable.
