BS, Nagarjuna University (India); MS, Wright State University; Ph.D., University of Texas at San Antonio
Dhireesha Kudithipudi, Ph.D. is a professor, graduate program chair, and director of the Neuromorphic AI (Nu.AI) Lab in the Department of Computer Engineering at Rochester Institute of Technology.
Over the past decade, her research team has been paving a path to creating artificial intelligence platforms inspired by the brain. Her research lab has developed neuromemristive AI platforms with continual learning capabilities. Dr. Kudithipudi’s team has robust crossdisciplinary knowledge across the neuromorphic computing stack. Her current research interests are in neuromorphic computing, brain inspired neural algorithms, novel computing substrates (memristors and 3D-ICs), energy efficient machine intelligence, and AI-on-Chip. She has secured multi-million dollar research grants in neuromorphic computing and her team has exclusive access to cutting-edge AI processors.
Dr. Kudithipudi is the recipient of the Digital Rochester Technology Women of the Year, AFOSR faculty fellowship, Telluride cognitive computing fellowship, ASEE faculty fellowship, D&C Women to Watch, and UTSA outstanding graduate research award. She has authored or co-authored 100 manuscripts, 2 patents, and leads technical workshops in neuromorphic computing/AI. She consults and collaborates with startup firms in AI. Dr. Kudithipudi also serves as an associate editor for IEEE Transactions on Neural Networks and Learning systems.
Dr. Kudithipudi holds a Ph.D. in Electrical and Computer Engineering from the University of Texas- San Antonio and MS in Computer Engineering from Wright State University.
N. Soures, A. M. Zyarah, K. Carlson, J. B. Aimone, and D. Kudithipudi. (Sept. 2017). How Neural Plasticity Boosts Performance of Spiking Neural Networks. Annual Conference on Cognitive Computational Neuroscience (CNN '17).
J. Mnatzaganian, E. Fokoué, and D. Kudithipudi. (Jan. 2017). A mathematical formalization of hierarchical temporal memory’s spatial pooler. Frontiers in Robotics and AI 3, 81, DOI:https://doi.org/10.3389/frobt.2016.00081.
A. M. Zyarah, N. Soures, L. Hays, R. B. Jacobs-Gedrim, S. Agarwal, M. Marinella and D. Kudithipudi. (May 2017). Ziksa: On-chip learning accelerator with memristor crossbars for multilevel neural networks. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '17), 1-4, DOI:https://doi.org/10.1109/ISCAS.2017.8050531.
D. Graham, S. H. F. Langroudi, C. Kanan and D. Kudithipudi. (Nov. 2017). Convolutional Drift Networks for Video Classification. In Proceedings of the IEEE International Conference on Rebooting Computing (ICRC '17), 1-8,
C. Merkel, R. Hasan, N. Soures, D. Kudithipudi, T. Taha, S. Agarwal, and M. Marinella. (Oct. 2016). Neuromemristive Systems: Boosting Efficiency through Brain-Inspired Computing. IEEE Trans. Computers 49, 10, 56-64, DOI:https://doi.org/10.1109/MC.2016.312.
November 20, 2018
Artificial Intelligence - with a human touchThere is a growing group of RIT researchers working in a field broadly known as artificial intelligence, or AI. They are building increasingly complex algorithms—the rules that govern operating systems—so that machines can perform tasks that normally require human intelligence.
November 20, 2018
Giving computers a better brainNext-generation computing systems modeled after the human brain’s information processing capability and energy efficiency are becoming a reality through work by Dhireesha Kudithipudi.
June 18, 2018
Mapping artificial intelligence at RITResearchers in RIT’s Center for Human-Aware Intelligence believe their work could lead to breakthroughs in everything from health care to energy management to cybersecurity.