As of October 2019, I recently completed my PhD in Applied Physics at Stanford University, where I was advised by Professor Shanhui Fan. I work on mathematical and numerical techniques for inverse design and sensitivity analysis of photonic structures based on the 'adjoint variable method' (AVM). I have used this technique to perform large-scale optimization of various photonic devices and have also shown that it can be adapted to train machine learning hardware implemented in photonic circuits. Additionally, I am part of the Accelerator on a Chip International Program (ACHIP), which has the goal of building miniature particle accelerators on a chip using advanced laser and nanofabrication technology (video explanation). I have been heavily involved in the scaling of these accelerators using integrated photonic circuits and control methods built directly onto the chip.
I grew up in San Diego and went to the University of Michigan for my undergraduate degree, where I graduated with a BS in physics. At Michigan, I researched fabrication techniques for economical thin-film solar cells with Prof. Stephen Forrest. Between my undergrad and PhD, I first worked on trapped ion platforms for quantum computation at the National University of Singpore's Centre for Quantum Technologies. After that, I worked as a junior software engineer at GudTech Inc.
When I’m not doing science, you can find me surfing, rock climbing, or experimenting with various fermentation projects.
Machine Learning / Statistics:
Computer Science :
Numerical Methods / Mathematics:
Nonlinear Dynamics / Complexity: