Nam Pyo Suh
Professor Nam Pyo Suh is the Cross Professor Emeritus at MIT and was the 13th and 14th President of KAIST. He serves on advisory boards of universities and industrial firms.
Nannaji Saka
Dr. Nannaji Saka is a former Research Affiliate and Principal Research Scientist in the Department of Mechanical Engineering and the Laboratory for Manufacturing and Productivity at MIT. Saka has co-authored over 100 technical papers in tribology, mechanical behavior of materials, and manufacturing processes. He holds seven U.S. patents on electrical contacts and chemical-mechanical polishing and has several patents pending.
Wojciech Matusik
Lead Instructor
Professor Wojciech Matusik is an Associate Professor of Electrical Engineering and Computer Science at the Computer Science and Artificial Intelligence Laboratory at MIT, where he leads the Computational Fabrication Group. His research interests are in direct digital manufacturing and computer graphics.
This year, MIT’s Enterprise Additive Manufacturing course coincides with RAPID + TCT, North America’s largest additive manufacturing and industrial 3D printing event, taking place April 13-17, 2026 in Boston, MA. The course will combine regular lecture and workshop programming with an integrated experience at RAPID, broadening exposure to key stakeholders in the AM industry and the latest technologies and applications. This 5-day program includes 3.5 full days at MIT, with two half-days split between MIT and the RAPID exposition floor.
John Hart
John Hart is the Class of 1922 Professor and Head of the Department of Mechanical Engineering at MIT. He is also a faculty Co-Director of the MIT Initiative for New Manufacturing, and Director of the MIT Center for Advanced Production Technologies. John’s research focuses on manufacturing processes, machine design, and integration of computing and automation in production systems. John is a co-founder/advisor of several startup companies including VulcanForms, Upgrade Manufacturing, Desktop Metal, and Fabri. He is also a Board Member of Carpenter Technology Corporation. LinkedIn: https://www.linkedin.com/in/ajhart/
Autonomous robots. Self-driving cars. Smart refrigerators. Now embedded in countless applications, deep learning provides unparalleled accuracy relative to previous AI approaches. Yet, cutting through computational complexity and developing custom hardware to support deep learning can prove challenging for many enterprises—and the cost of getting it wrong can be catastrophic. Do you have the advanced knowledge you need to keep pace in the deep learning revolution?
Over the past eight years, the amount of computing required to run these neural nets has increased over a hundred thousand times, which has become a significant challenge. Gain a deeper understanding of key design considerations for deep learning systems deployed in your hardware.