Jayasimha Atulasimha is an Engineering Foundation Professor of Mechanical and Nuclear Engineering with a courtesy/affiliate appointment in Electrical and Computer Engineering and Physics at the Virginia Commonwealth University. He received his B. Tech degree from the Indian Institute of Technology, Madras in 2001 and PhD from the University of Maryland, College Park in 2006. Spintronics, neuromorphic computing, hardware AI and quantum computing are his current research interests. Atulasimha has authored or coauthored over 90 journal publications, has coedited a book on nanomagnetic and spintronic devices for energy efficient computing, published 5 book chapters and has 4 patents. Some of this work has been highlighted in Nature and IOP Nanotechnology, and appeared in Chemical and Engineering. He has won several awards including most recently, VCU's National and International Recognition Award (NIRA) in 2023, Active Multifunctional Materials Outstanding Contribution Award presented by the Active and Multifunctional Materials Technical Committee of ASME in 2021 and the Engineer of the Year Award, Richmond Joint Engineering Council in 2021.

Atulasimha is a fellow of the ASME, an IEEE Senior Member and past chair for the Technical Committee on Spintronics, IEEE Nanotechnology Council. He also serves as the Associate Director for the Institute for Sustainable Energy and Environment (ISEE) at VCU. He is a Co-Chair of the Material and Devices working group for Semiconductor Industry Energy Efficiency Scaling (EES2), which is a US Department of Energy (DOE) roadmap effort to make computing devices energy efficient. In 2018 and 2021 he co-organized the 6th and 7th US Government Working Group meeting on Magnetic tunnel junctions (MTJs) respectively.

Early in his academic career, he was one of the pioneers in the development of a new field “straintronics” that employs electric field induced strain to manipulate magnetic information in an energy efficient manner. Furthermore, his group's work on electric field control of magnetic skyrmions was published in Nature Electronics. He is working on demonstrating a prototype of an energy efficient non-volatile magnetic memory based on this work. Atulasimha's current research focus includes hardware AI and quantum computing. In hardware AI, the specific focus is on neuromorphic (brain-like) computing hardware that could enable embedded devices to learn from data in real-time while consuming very little power and hardware resources. Towards the goals of Convergence Lab Initiative (CLI) his group's research efforts will focus on edge AI for anomaly detection, information processing and developing prototypes of edge AI hardware on CMOS and CMOS+Spintronics platforms. Specifically, his group will explore various hardware AI paradigms and algorithms that enable state of the art testing accuracies while being extremely energy and hardware resource efficient both of which are severely constrained in edge devices.