Jyotikrishna Dass
- Assistant Professor, Electrical and Computer Engineering
- Member of the Graduate Faculty
- (520) 621-2434
- Electrical & Computer Engr, Rm. 456T
- Tucson, AZ 85721
- jdass@arizona.edu
Biography
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
- Ph.D. Computer Science and Engineering
- Texas A&M University, College Station, Texas, United States
- Efficient and Scalable Machine Learning for Distributed Edge Intelligence
- B. Tech Electronics and Communication Engineering
- Indian Institute of Technology, Guwahati, Assam, India
- Object Detection in Videos
Work Experience
- Rice University (2022 - 2024)
- Rice University (2021 - 2022)
- Texas A&M University, College Station, Texas (2018 - 2020)
- Amazon (2017)
- TCS Research (2013)
Awards
- Graduate Teaching Fellow
- Texas A&M University - College of Engineering, Spring 2020
- Best PhD Thesis Poster
- University of Alabama - Annual Computing@SEC Conference, Fall 2019
- Conference Travel Grants
- IEEE HiPC 2019, Hyderabad, India (TAMU: $500); ACM FPGA 2019, Seaside, CA (ACM: $950); IEEE ICDCS 2017, Atlanta, GA (NSF + TAMU: $1500); IEEE IPDPS 2016, Chicago, IL (NSF: $568); IEEE NCVPRIPG 2013, Jodhpur, India (TCS), Fall 2019
- Teaching Assistant Excellence Award
- Texas A&M University - Dept. of Computer Science and Engineering, Spring 2018
- IEEE IPDPS PhD Forum
- IEEE International Parallel and Distributed Processing Symposium, Summer 2016
Interests
Research
Distributed machine learning, edge AI, systems architecture for high-performance machine learning
Teaching
Machine learning and artificial intelligence, digital logic, computer organization and design, parallel computing, computer programming, engineering mathematics
Courses
2024-25 Courses
-
Digital Logic
ECE 274A (Spring 2025) -
Digital Logic
ECE 274A (Fall 2024)
Scholarly Contributions
Journals/Publications
- Zhang, S., Fu, Y., Wu, S., Dass, J., You, H., & Lin, Y. (2023). NetDistiller: Empowering Tiny Deep Learning via In Situ Distillation. IEEE Micro, 43(6), 84-92.
- Dass, J., Narawane, Y., Mahapatra, R. N., & Sarin, V. (2020). Distributed Training of Support Vector Machine on a Multiple-FPGA System. IEEE Transactions on Computers, 69(7), 1015-1026.
- Dass, J., Sarin, V., & Mahapatra, R. N. (2019). Fast and Communication-Efficient Algorithm for Distributed Support Vector Machine Training. IEEE Transactions on Parallel and Distributed Systems, 30(5), 1065-1076.
Proceedings Publications
- Dass, J., Wu, S., Shi, H., Li, C., Ye, Z., Wang, Z., & Lin, Y. (2023). ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention. In 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA).
- Dass, J., & Mahapatra, R. (2021, 18--24 Jul). Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers. In Proceedings of the 38th International Conference on Machine Learning, 139.
- Dass, J., Narawane, Y., Mahapatra, R., & Sarin, V. (2019). FPGA-based Distributed Edge Training of SVM. In Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays.
- Dang, D., Dass, J., & Mahapatra, R. (2017). ConvLight: A Convolutional Accelerator with Memristor Integrated Photonic Computing. In 2017 IEEE 24th International Conference on High Performance Computing (HiPC).
- Dass, J., Sakuru, V. P., Sarin, V., & Mahapatra, R. N. (2017). Distributed QR Decomposition Framework for Training Support Vector Machines. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).
- Lee, K., Bhattacharya, R., Dass, J., Prithvi, S., & Mahapatra, R. N. (2016). A Relaxed Synchronization Approach for Solving Parallel Quadratic Programming Problems with Guaranteed Convergence. In 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS).
- Dass, J., Sharma, M., Hassan, E., & Ghosh, H. (2013). A density based method for automatic hairstyle discovery and recognition. In 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG).
