Priya Panda is an assistant professor in the electrical engineering department at Yale University, USA. She received her B.E. and Master’s degree from BITS, Pilani, India in 2013 and her PhD from Purdue University, USA in 2019. During her PhD, she interned in Intel Labs where she developed large scale spiking neural network algorithms for benchmarking the Loihi chip. She is the recipient of the 2019 Amazon Research Award, 2022 Google Research Scholar Award, 2022 DARPA Riser Award. She has published more than 60 publications in well recognized venues including, Nature, Nature Communications, IEEE among others. Her research interests include- neuromorphic computing, energy efficient deep learning, adversarial robustness and hardware centric design of robust neural systems.
What do you do with data science?
My research interests lie in Neuromorphic Computing: Energy-efficient design methodologies for Deep Learning, Novel learning algorithms for Spiking Neural Networks, Explainability and Robustness for Spiking Neural Networks, Developing emerging non-volatile memory-/ CMOS-based neural architectures for new computing scenarios (such as Adversarial Robustness, Lifelong/Continual Learning, Federated Learning, Stochastic Optimization etc.).
My interest in data science is to see if we can use grounded theories to develop energy efficient algorithm-hardware co-design solutions.