Andrew Maloney
Andrew Maloney

Andrew J. Maloney is a Process Development Scientist in the Bioprocess Sciences and Technology Group at Amgen.  He obtained a Bachelor of Science degree in chemical engineering from West Virginia University in 2016, a Master of Chemical Engineering Practice degree in chemical engineering from the Massachusetts Institute of Technology in 2018, and a Ph.D. degree from MIT, in 2021.  At MIT, he worked under the supervision of Prof. Richard D. Braatz on the development of process models and control strategies for the manufacturing of pharmaceuticals. After MIT, Andy joined Amgen as a process development scientist. His group works on late-stage process development for biological products. Outside of work Andy enjoys baking, cooking, reading, and board games.   
 

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Amanda Lewis
Amanda Lewis

Amanda M. Lewis is the Senior Director of Quality Operations for Moderna’s Individualized Neoantigen Therapy program.  Amanda joined Moderna in Feb 2023 to support this patient specific medicine focused on oncology indications, and has been accountable for building out a team to support Phase 3 studies and commercial readiness.

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Transform how you design, build, and train using the power of virtual and augmented reality. Developing Immersive Technologies for Manufacturing and Design Engineering is a hands-on, project-based course that teaches you how to solve complex industry problems. Whether you are a student or an industry professional, this program gives you the practical skills you need to innovate. 

What would Artificial General Intelligence look like if its first breakthrough were not in language, but in the invention of matter? Join the frontier of superintelligence applied to agentic materials discovery. In this condensed four-day course, you will move beyond static design to master autonomous AI workflows. Through hands-on clinics, you will build multi-agent systems that do not merely predict material properties, but reason, plan, and invent next-generation smart materials - integrating large-scale computational modeling with generative AI to solve complex engineering challenges across scales, from atoms to systems, from concept to physical realization.
This course introduces the modeling and mathematical foundations of modern AI. Starting from essential refreshers in calculus and linear algebra, we build toward the core structures underlying today’s supervised, unsupervised, and generative models. Case studies develop skill in translating real-world problems into the abstract language of modern AI pipelines.
Robert Jackson
Robert Jackson

Since 2004, Rob Jackson has served a Professor in the Department of Mechanical Engineering at Auburn University. He earned his B.S., M.S., and Ph.D. degrees in Mechanical Engineering from the Georgia Institute of Technology. His research focuses on tribology, contact mechanics, electrified contacts, and lubricated bearings—critical areas for advancing mechanical systems and energy efficiency, particularly in electric vehicles (EVs). Professor Jackson directs Auburn’s Tribology Program and created one of the first undergraduate minors in tribology in 2012.

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Peter Lee
Peter Lee

Peter Lee, Ph.D. is an Institute Engineer and the Chief Tribologist at Southwest Research Institute (SwRI), an Adjunct Professor at Texas A&M, and summer lecturer at the University of California Merced. Peter Lee received a B.Eng in Automotive Engineering and a Ph.D. in Engine Tribology from the University of Leeds, UK. Prior to attending University, Peter worked as a qualified motor vehicle technician repairing both light and heavy-duty vehicles as well as various motorsport activities on the side.

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Ready to revolutionize transportation systems and discover how disruptive innovations are reshaping the mobility sector? In this immersive five-day course, you will learn to analyze and optimize transportation systems using the latest research from MIT and beyond, delving into demand and network modelling, artificial intelligence, simulation, optimization and control. These methods are explored alongside selected future solutions, with a focus on user-centric new smart mobility services , automated and AI-driven vehicles and alternative energy vectors for decarbonizing transportation. Through real-world case studies, professionals from transport service providers, urban and mobility planning, automotive, and transportation sectors can gain actionable insights to address current and future transportation challenges.
Anticipate where your industry is headed—and secure a competitive advantage—by mastering the latest discrete choice models and techniques. In this five-day course, you’ll work with leading MIT experts to discover how to apply discrete choice techniques; analyze challenges related to data collection, model formulation, estimation, testing, and forecasting; and assess online applications that drive optimization and personalization of results.
Michael Birnbaum
Dr. Michael Birnbaum

Participating Instructor

Michael obtained an A.B. in Chemical and Physical Biology at Harvard University in 2008. He then moved to Stanford University, where he completed his Ph.D. in Immunology in 2014. At Stanford, he worked in Professor K. Christopher Garcia’s laboratory, studying the molecular mechanisms of T cell receptor recognition, cross-reactivity, and activation. He then conducted postdoctoral research in Professor Carla Shatz’s laboratory, studying novel roles for immune receptors expressed by neurons in neural development and neurodegenerative disease. Michael joined the Department of Biological Engineering in 2016 as an Assistant Professor.

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