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Priyadarshini Panda
"My Ph.D. under Prof. Kaushik Roy at Purdue Engineering was instrumental in shaping my success. The holistic perspective offered by Purdue allowed me to transcend my focus on circuits and algorithms, exposing me to the broader realms of nanoelectronics, semiconductor technology, and device physics. Opportunities to present my research at prestigious platforms like the Grace Hopper Conference laid the foundation for my academic career. Purdue's workshops and academic events guided me through the academic job search and faculty interview process. I am profoundly grateful for the nurturing environment at Purdue Engineering, which empowered me to pursue my interests and aspirations with freedom and confidence."
Priyadarshini Panda | Electrical Engineering
Assistant Professor of Electrical Engineering, Yale University
As an assistant professor of electrical engineering at Yale University, Priyadarshini Panda addresses the challenge of
energy efficiency in artificial intelligence, but also opens new avenues for understanding and replicating the
intricacies of natural intelligence in artificial systems. Her most notable contribution lies in the realm of spiking
neural networks (SNNs), for which she has developed scalable and efficient algorithms and complementary hardware to pave
the way for low-power machine intelligence. One of her pivotal roles was contributing to the development of Intel's
Loihi chip, which has become a cornerstone in the field of neuromorphic computing. Beyond SNNs, her group developed an
innovative approach known as the conditional early exit model, which was adopted by Intel's Nervana to enhance the
efficiency of deep learning accelerators.
Panda considers her students as the true markers of her success. Two undergraduate students in her group have won
high-level awards and published co-authored papers, and her PhD students have received best paper awards and earned
prestigious fellowships.
Her contributions to the field of machine intelligence have made a significant impact, as evidenced by her 80+
publications, citation count of 7200, and h-index of 41. Her Nature Perspective article on spike-based machine
intelligence has gained widespread recognition, as has her YouTube Spiking Neural Network tutorial. She has delivered
invited talks at forums such as the DARPA Electronics Resurgence Initiative Summit, and has received accolades including
the NSF CAREER Award, the DARPA Young Faculty Award, as well as industry recognition with the Amazon Research Award and
the Google Research Scholar Award.