About me:
I am a postdoctoral research scientist in the laboratory of Peter McMahon, in Cornell's Applied Physics department, as well as a research scientist in NTT's Physics and Informatics Laboratory, directed by Yoshihisa Yamamoto. Previously, I was a post-doc in Hideo Mabuchi's lab at Stanford, and before that a PhD student in Frank Wise's lab at Cornell.
A complete list of my academic publications can be viewed here, via Google Scholar.
I can be contacted through email: LGW32[at]cornell.edu
A copy of my CV may be downloaded here.
In July 2023, I will join Yale University as an assistant professor, starting a new experimental lab in applied physics.
Briefly, the lab's theme is:
Physical computation, control, and complexity; mostly with photons.
Scroll down to learn more, including about open positions for PhD students, post-docs, and visitors.
The lab's primary research goal is to explore, and to construct a positive feedback loop between two topics:
(A) the physics and applications of complex physical systems (most often high-dimensional nonlinear and quantum optical waves) and,
(B) concepts and algorithms that symbiotically combine physics and computer science (mostly through the lens of artificial intelligence).
Outcomes I hope to eventually see from this expedition include:
Photonic brains: light-based systems for AI and computational sensing that vastly outperform digital electronics.
Photonic robotics ("phobots"): light-based systems for controlling, measuring, and conducting data-driven explorations of micro- and mesoscale physical processes.
Physical programming: methods and platforms to "code" the functionality and form of complex physical systems.
Research directions in the lab include:
1. Physical and quantum neural networks: Using physical systems with many controllable parameters for analog computing, and for computational imaging and sensing.
2. Automated experimental science and engineering: Computer-driven discovery, design, and control of complex physical systems, usually with physics-informed machine learning.
3. Multimode quantum and nonlinear photonics: The physics and applications of nonlinear and quantum optical wave propagation and oscillators, usually involving many degrees of freedom.
Research on these topics includes experimental, theoretical, and computational components, with an emphasis on experiments and prototypes based on, or enabled by, photonics. Where feasible, I'm interested in developing and applying broad, systems-level concepts and algorithms. Accordingly, I also welcome diverse ideas, applications, and people not just from photonics, but also from adjacent fields, such as robotics, applied mathematics, fluids, biology, and materials science.
If you want to learn a bit more about these topics, you could start with these recent publications:
L.G. Wright*, T. Onodera* et al., Deep physical neural networks trained with backpropagation. Nature 601, 549-555 (2022).
L.G. Wright et al., Physics of highly multimode nonlinear optical systems. Nat. Phys. 18, 1018–1030 (2022).
L.G. Wright et al., Nonlinear multimode photonics: nonlinear optics with many degrees of freedom. Optica 9, 824-841 (2022).
T. Wang*, M.M. Sohoni*, L.G. Wright et al., Image sensing with multilayer, nonlinear optical neural networks. arXiv:2207.14293 (2022).
Open positions:
If this sounds exciting to you, and you want to do some new science, expanding the frontiers of science and technology, I am planning to hire both PhD students and post-docs to help start this new lab at Yale.
PhD students: The application deadline for the PhD program is December 15, 2022.
PhDs or near-PhDs interested in a post-doc: Please contact me by email and we can chat more, including about specific projects. When you get in touch, please include a CV with publication list. Experience with ultrafast lasers, quantum optics and/or neural networks are helpful, but not essential.
Any other collaborators - please reach out!