Dhireesha Kudithipudi

Areas of Teaching Interest

Brain-Inspired AI, Energy Efficient Machine Intelligence, Computer Architecture.

Areas of Research Interest

Neuromorphic AI systems and architectures, AI Algorithms (eg: brain-inspired), Emerging Computing Substrates, Energy efficient machine learning, Lifelong learning, Spiking and rate based models.

Educational Background

Ph.D., UTSA, 2006

Selected Publications

• T. Pandit and D. Kudithipudi, “Relational neurogenesis for lifelong learn- ing agents,” in Proceedings of the 8th ACM Annual Workshop on Neuro- inspired Computational Elements (NICE), 2020, pp. 1–9
• N. Soures and D. Kudithipudi, “Spiking reservoir networks: Brain-inspired recurrent algorithms that use random, fixed synaptic strengths,” IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 78–87, 2019
• A. Yanguas-Gil, A. Mane, J. W. Elam, F. Wang, W. Severa, A. R. Daram, and D. Kudithipudi, “The insect brain as a model system for low power electronics and edge processing applications,” in Proceeding of the IEEE Space Computing Conference (SCC), 2019, pp. 60–66
• N. Soures and D. Kudithipudi, “Deep liquid state machines with neural plasticity for video activity recognition,” Frontiers in neuroscience, vol. 13, pp. 1–12, 2019
• Z. Carmichael, H. F. Langroudi, C. Khazanov, J. Lillie, J. L. Gustafson, and D. Kudithipudi, “Deep positron: A deep neural network using the posit number system,” in Proceedings of the IEEE Design, Automation & Test in Europe Conference & Exhibition (DATE), 2019, pp. 1421–1426
• A. M. Zyarah and D. Kudithipudi, “Neuromorphic architecture for the hierarchical temporal memory,” IEEE Transactions on  Emerging  Topics in Computational Intelligence (TETCI), vol. 3, no. 1, pp. 4–14, 2019
• M. Soltiz, D. Kudithipudi, C. Merkel, G. S. Rose, and R. E. Pino, “Memristor- based neural logic blocks for nonlinearly separable functions,” IEEE Trans- actions on computers, vol. 62, no. 8, pp. 1597–1606, 2013