Dhireesha Kudithipudi Headshot

Dhireesha Kudithipudi

Professor
Department of Computer Engineering
Kate Gleason College of Engineering
Director of Center for Human-Aware AI

585-475-5085
Office Location

Dhireesha Kudithipudi

Professor
Department of Computer Engineering
Kate Gleason College of Engineering
Director of Center for Human-Aware AI

Education

BS, Nagarjuna University (India); MS, Wright State University; Ph.D., University of Texas at San Antonio

Bio

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.

For more about Dr. Kudithipudi visit her personal website or research website.

Selected Publications

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,
DOI:https://doi.org/10.1109/ICRC.2017.8123647.

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.

585-475-5085

Currently Teaching

IMGS-891
0 Credits
Continuation of Thesis
CMPE-765
3 Credits
This course is primarily designed for graduate students and will expose them to theoretical and practical aspects of brain-inspired computing. It will offer students the opportunity to understand how the human brain computes to achieve intelligent behavior and how this understanding guides the development of new neural algorithms. We will identify the key developments and large issues at stake, and study brain inspired systems in the context of pragmatic applications. At the end of the course the students are expected to have expanded their knowledge of how the brain processes information, and how one can develop neuromorphic algorithms to tackle emergent spatio-temporal problems.
CMPE-795
0 Credits
The graduate seminar prepares graduate students to effectively conduct their thesis research and expose them to current research in various areas of computer engineering. Current literature topics are reviewed through interactive presentations and discussions.

Latest News

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    Artificial Intelligence - with a human touch

    There 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

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    Next-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

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    Researchers 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.