Vijay Janapa Reddi

My research integrates computer architecture and machine learning systems to advance intelligence and autonomy in mobile devices, edge computing platforms, and the Internet of Things (IoT), enabling the development of smarter and more efficient autonomous systems.

Vijay Janapa Reddi is the John L. Loeb Associate Professor of Engineering and Applied Sciences at Harvard University and Vice President and co-founder of MLCommons, a nonprofit organization committed to accelerating machine learning (ML) innovation for all. As the head of MLCommons Research, he drives the organization’s strategic direction and serves on its Board of Directors. Dr. Janapa Reddi co-led the development of the MLPerf benchmarks, which set the standard for evaluating ML performance across IoT, mobile, edge, and datacenter applications. He also serves on the board of the tinyML Foundation, where he helps build industry-academia partnerships and shape the future of edge AI technologies.

Before joining Harvard, he was an Associate Professor in the Department of Electrical and Computer Engineering at the University of Texas at Austin. His research bridges computer architecture and applied machine learning methods to develop innovative solutions at the intersection of mobile computing, edge computing, and the Internet of Things.

Dr. Janapa Reddi’s contributions to the field have been recognized with numerous accolades, including the prestigious Gilbreth Lecturer Honor from the National Academy of Engineering (NAE) in 2016, the IEEE TCCA Young Computer Architect Award (2016), and the Intel Early Career Award (2013). He has received multiple Google Faculty Research Awards (2012, 2013, 2015, 2017, 2020) and Best Paper Awards at top conferences, such as the 2020 Design Automation Conference (DAC), the 2005 International Symposium on Microarchitecture (MICRO), and the 2009 International Symposium on High-Performance Computer Architecture (HPCA). His work has been frequently recognized as IEEE Top Picks in Computer Architecture (2006, 2010, 2011, 2016, 2017, 2022, 2023), and he is an inductee of the MICRO and HPCA Hall of Fame.

Dr. Janapa Reddi is deeply committed and passionate about expanding access to applied machine learning and advocating for STEM diversity. He actively promotes ML education through his work, including authoring and editing the widely adopted open-source textbook, "Machine Learning Systems" (MLsysbook.AI), which has become a key resource for teaching machine learning systems engineering worldwide. He frequently emphasizes the often-overlooked but crucial role of ML engineers in a world centered on training AI models, noting that while ML developers are like astronauts exploring new frontiers, ML engineers are the rocket scientists and mission control specialists who make the journey possible and keep it on track.

To make machine learning accessible at a low cost, he developed the Tiny Machine Learning (TinyML) series on edX, focusing on integrating ML into resource-constrained, embedded systems. This popular MOOC has reached over 100,000 students globally, providing an affordable pathway for learners to explore the intersection of ML and embedded computing. He also spearheaded the Austin Hands-on Computer Science (HaCS) program, which brought computer science education to K-12 students in the Austin Independent School District. His passion lies in helping individuals and organizations succeed because he believes that while talent is present nearly everywhere, opportunities to succeed aren't distributed equally.

Dr. Janapa Reddi holds a Ph.D. in Computer Science from Harvard University, an M.Sc. in Electrical and Computer Engineering from the University of Colorado Boulder, and a B.Sc. in Computer Engineering from Santa Clara University.