I am giving a workshop with Momchil Minkov on inverse design and automatic differentiation for photonic devices at Stanford University
Updated software page to describe my recent open source projects and research in machine learning and optical simulation / optimization
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 JSTQE
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.