Dhireesha Kudithipudi, Ph.D. is a professor, graduate program chair, and director of the Nano Computing Lab in the Department of Computer Engineering at Rochester Institute of Technology.
Over the past decade, her research team is 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 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 and her team has exclusive access to cutting-edge AI processors. Dr. Kudithipudi is the recipient of the Airforce 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 PhD 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.
D. Kudithipudi, Q. Saleh, C. Merkel, J. Thesing, and B. Wysocki. (Feb. 2016). Design and analysis of a neuromemristive reservoir computing architecture for biosignal processing. Frontiers in neuroscience 9, 502, DOI:https://doi.org/10.3389/fnins.2015.00502.
C. Merkel, and D. Kudithipudi. (Sept. 2014). A stochastic learning algorithm for neuromemristive systems. In Proceedings of the 27th. IEEE International System-on-Chip Conference (SOCC '14), 359-364, DOI:https://doi.org/10.1109/SOCC.2014.6948954.