Research Interests

AI for Chip Design, Machine Learning, Electronic Design Automation

Fusing Integrated Circuit Design and Artificial Intelligence offers a paradigm shift in how we think about Electronic Design Automation in the next decade. The modern integrated chip (IC) design process--spanning logic synthesis, placement, routing, and optimization, requires managing billions of interconnected variables while optimizing for multiple objectives such as power, performance, yield and area. Despite decades of development, state-of-the-art Electronic Design Automation (EDA) tools still lack the ability to learn from past iterations.

Machine learning (ML) offers a transformative paradigm by intertwining exploration, exploitation, and decision-making to solve complex problems. My research focuses on harnessing many latest learning mechanisms, to learn optimal decision sequences in complex design optimization problems.

Biographical Sketch

I received my Ph.D. in Computer Engineering from the University of California, San Diego. I was advised by Prof. Chung-Kuan Cheng, Prof. Tajana Rosing, and Prof. Bill Lin.

Prior to this, I obtained a Dual Degree (B.E. in Electrical and Electronics Engineering and M.Sc. in Physics) from BITS Pilani in 2011.

Currently, I am a Research Scientist at Intel AI Labs in Santa Clara, USA.