Jyotikrishna Dass
Jyotikrishna Dass is an assistant professor in the Department of Electrical and Computer Engineering at The University of Arizona. His research integrates machine learning, parallel computing, and hardware design to create efficient algorithms and systems for distributed edge intelligence. His work has been featured in the IEEE International Conference on Machine Learning (ICML), IEEE International Conference on Distributed Computing Systems (ICDCS), IEEE International Parallel and Distributed Processing Symposium (IPDPS), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE International Symposium on High-Performance Computer Architecture (HPCA), IEEE Micro and IEEE Transactions on Computers (TC). He has served as instructor-of-record for several courses during his graduate studies and is eager to contribute to the department's new computer science and engineering program.
Prior to joining U of A, Dr. Dass was a research scientist and executive director at Rice University, leading the Center for Transforming Data to Knowledge (D2K). From 2021-2022, he was a postdoctoral research associate at Rice, co-writing grant proposals for NSF Core Programs ($1.2 million), META Network for AI ($50K), and Rice University Creative Ventures Fund ($10K).
Dr. Dass earned his PhD in computer science and engineering from Texas A&M University in 2021. His research was recognized with the Best PhD Dissertation Poster Award at the Annual Computing Conference ’19 among fourteen SEC universities. He was also a College of Engineering Graduate Teaching Fellow in 2020 and received the CSE Teaching Assistant Excellence Award in 2018. He holds a B.Tech degree in electronics and communication engineering with a Minor in CSE from the Indian Institute of Technology (IIT) Guwahati.
Degrees
- PhD, Computer Science and Engineering, Texas A&M University, College Station, 2021
- B.Tech, Electronics and Communications Engineering; Minor in Computer Science and Engineering, Indian Institute of Technology, Guwahati, 2014
Teaching Interests
Machine learning and artificial intelligence, digital logic, computer organization and design, parallel computing, computer programming, engineering mathematics
Research Interests
Distributed machine learning, edge AI, systems architecture for high-performance machine learning
Textbooks/Most Significant Publications
Awards
- Texas A&M College of Engineering Graduate Teaching Fellow, 2020
- Best PhD Dissertation Poster Award, SEC Annual Computing Conference, 2019
- Texas A&M Department of CSE Teaching Assistant Excellence Award, 2018