Our paper on the forward mode differentiation of Maxwell’s equations was published in ACS Photonics! This mode is the counterpart to the adjoint / backward mode typically used in inverse design.
Our work towards realizing nonlinear activation functions for optical neural networks was published in IEEE Journal of Selected Topics in Quantum Electronics
I am giving a talk at CLEO on broadband optical switches using dynamic modulation in the Time Varying Metasurfaces session at 9:15 am
I am a Postdoctoral Researcher at Stanford University in Professor Shanhui Fan’s group.
I am an engineer with a research background in optics and electromagnetics, but I am also excited by physics at the intersection between optics, electronics, mechanics, and acoustics. In particular, I am interested in applications for information processing and analog computing. I also enjoy developing high-performance software in Python, Julia, and Matlab for performing numerical simulation and optimization.
More recently, I have been interested in machine learning and, specifically, how concepts in optical signal processing and microwave photonics can be used to enhance the performance of neuromorphic hardware. Along a related direction, I am also intrigued by automatic differentiation and differentiable programming, which are fundamental in training neural networks. I would like to apply these techniques to inverse design and optimization of physics-constrained problems.