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).
