Bio-plausible artificial intelligence
Today, artificial intelligence is broadly pursued by Deep learning and Neuromorphic Computing researchers in the design space of energy-accuracy tradeoff with the motif of creating a machine exhibiting brain-like cognitive ability with brain-like efficiency. However, there are several questions regarding what Priya Panda terms as appropriateness of intelligent systems: robustness, explainability, security in adversarial scenarios, adaptivity or lifelong learning in a real-time complex environment and compatibility with hardware stack. With the advent of the Internet of Things and the necessity to embed intelligence in all technology that surrounds us, Panda's research aims to explore energy-accuracy-appropriateness tradeoff cohesively with algorithm hardware co-design to create truly functional intelligent systems. The Intelligent Computing Lab is also interested in exploring bio-plausible algorithms-and-hardware guided by natural intelligence (how the brain learns, the internal fabric of the brain, etc.) to define the next generation of robust and efficient AI systems for beyond-vision static recognition tasks with the ability to perceive, reason, and decide autonomously in real-time. In summary, their research focuses on building efficient, robust, and reliable machine intelligence grounded with bio-plausibility.
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Biography
Priya Panda is an assistant professor in the electrical engineering department at Yale University. She received her BE and Master's degree from BITS, Pilani, India in 2013 and her PhD from Purdue University, USA in 2019. During her PhD, she interned at 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, 2023 NSF CAREER Award, 2023 DARPA Young Faculty Award, and 2024 Purdue Engineering 38 under 38 Award.