Vijay Janapa Reddi
Associate Professor at John A. Paulson School of Engineering and Applied Sciences (SEAS), Harvard University

Vijay Janapa Reddi is an Associate Professor at Harvard University, Inference Co-chair for MLPerf, and a founding member of MLCommons, a nonprofit ML (Machine Learning) organization aiming to accelerate ML innovation. He also serves on the MLCommons board of directors. Before joining Harvard, he was an Associate Professor at The University of Texas at Austin in the Department of Electrical and Computer Engineering. His research interests include computer architecture and runtime systems, specifically in the context of autonomous machines and mobile and edge computing systems. Dr. Janapa Reddi is a recipient of multiple honors and awards, including the National Academy of Engineering (NAE) Gilbreth Lecturer Honor (2016), IEEE TCCA Young Computer Architect Award (2016), Intel Early Career Award (2013), Google Faculty Research Awards (2012, 2013, 2015, 2017, 2020), Best Paper at the 2020 Design Automation Conference (DAC), Best Paper at the 2005 International Symposium on Microarchitecture (MICRO), Best Paper at the 2009 International Symposium on High Performance Computer Architecture (HPCA), IEEE’s Top Picks in Computer Architecture awards (2006, 2010, 2011, 2016, 2017) and he has been inducted into the MICRO and HPCA Hall of Fame (in 2018 and 2019, respectively). He received a Ph.D. in computer science from Harvard University, M.S. from the University of Colorado at Boulder and B.S from Santa Clara University.

Faculty Courses

Online

Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.

Price
Free*
Duration
5 weeks long
Registration Deadline
Available now
Online

Get the opportunity to see TinyML in practice. You will see examples of TinyML applications, and learn first-hand how to train these models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.

Price
Free*
Duration
6 weeks long
Registration Deadline
Available now
Online

Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.

Price
Free*
Duration
6 weeks long
Registration Deadline
Available now
Online

This course introduces learners to Machine Learning Operations (MLOps) through the lens of TinyML (Tiny Machine Learning). Learners explore best practices to deploy, monitor, and maintain (tiny) Machine Learning models in production at scale.

Price
Free*
Duration
7 weeks long
Registration Deadline
Available now