TinyML Foundations: Machine Learning for Embedded Systems

Laurence Moroney is an award-winning artificial intelligence researcher and best-selling author dedicated to making AI and machine learning accessible to everyone. As an instructor at DeepLearning.AI, he has taught millions through MOOCs and YouTube, while also serving as a keynote speaker at various events. Moroney is a fellow of the AI Fund and advises several AI startups, leveraging his expertise to foster innovation in the field. Based in Seattle, Washington, he is also an active member of the Science Fiction Writers of America, having authored multiple sci-fi novels and comic books. When not immersed in technology, he enjoys indulging in coffee and exploring creative writing.

Dr. Vijay Janapa Reddi serves as the John L. Loeb Associate Professor of Engineering and Applied Sciences at Harvard University and Vice President of MLCommons, where he drives innovation in machine learning as both co-founder and research chair. His research integrates computer architecture and machine learning systems to advance intelligence and autonomy in mobile devices, edge computing platforms, and IoT devices. After completing his BS in Computer Engineering from Santa Clara University, MS from the University of Colorado Boulder, and PhD in Computer Science from Harvard University, he established himself at the University of Texas at Austin before joining Harvard in 2019. His significant contributions include co-leading the development of MLPerf benchmarks, creating the Tiny Machine Learning series on edX reaching thousands of global learners, and pioneering work in mobile and edge computing systems. His exceptional achievements have earned him numerous accolades, including the NAE Gilbreth Lecturer Honor, IEEE TCCA Young Computer Architect Award, Intel Early Career Award, multiple Google Faculty Research Awards, and induction into both the MICRO and HPCA Halls of Fame. Beyond academia, he serves on the boards of MLCommons and the tinyML Foundation, while actively working to democratize machine learning education through initiatives like the Austin Independent School District's hands-on computer science program and the development of open-source educational resources.