Machine Learning
The theme program provides an interdisciplinary platform to learn about and discuss a wide range of optics and photonics topics that machine learning impacts. The theme invited speakers spanning academia, industry and government institutions.
This year’s theme focuses on two subtopics. One, how is machine learning being used to improve fabrication and manufacturing of optics or by optics, such as in laser manufacturing? The other subtopic examines solvers based on machine learning approaches. Through these talks, attendees will learn about the newest machine learning technologies applied to optics and photonics and compare them with conventional approaches.
Theme Coordinators
- Groot Gregory, Synopsys Inc., USA
- Chenkai Mao, Stanford University, USA
- Ben Mills, University of Southampton, UK
Visionary Speaker
Tyler Hughes, Flexcompute, USA
Talk Title: Building the Future of Photonic Design and Simulation with Machine Learning
Invited Speakers
David Brady, Univ. of Arizona, USA
Inteferometric Focal Planes: A Case Study in LLM Research
Wanli Chi, Meta, USA
High Efficiency LCOS Laser Projector with Holographic Beam Expander
Jonathan Fan, Stanford Univ., USA
Automated Design for Large and Manufacturable Metalenses
Priyanka Ghosh, MTC, UK
AIgorithm Driven Approaches to Nanophotonic Systems Design
Ulrich Hofmann, OQumented, Germany
Power Efficiency of progressive Lissajous Laser Scanning Displays for AR
Ighodalo Idehenre, Core4ce, USA
MAISY: an AI Guided Deep Learning Software Package for the Design and Fabrication of Next-Generation Optics
Masa Kamata, Sony Semiconductor Solutions, Japan
Diode-pumped Solid-state Surface-emitting Lasers
Yannik Mahlau, Leibniz University Hannover, Germany
Automatic Differentiation and Reinforcement Learning for Inverse Design in Nanophotonics
Dylan McGuire, Ansys, Inc., Canada
Multi-Scale Photonic Inverse Design, Optimization and Sensitivity Analysis for Advanced Optical IO
Juuso Olkkonen, Dispelix, Finland
Designing Diffractive Waveguides for Laser Projectors
Raphael Pestourie, Georgia Institute of Technology, USA
Machine Learning-Enhanced Optimization for Metamaterial Design
Phillip Skahan, Kyocera, USA
Advanced InGaN Laser Manufacturing and Applications in Display
Peter Smith, University of Southampton, UK
Nonlinear Wave-mixing of Infra-red Lasers as a Route to Transparent Near-to-eye Displays
Karolina Traczyk, AlphaLum, Switzerland
Innovative 24-Channel RGB Illuminator Engine with compact Multi-Ridge Lasers and cutting-edge Driver IC providing 4.8Gpulses/s for Laser Beam Scanning Displays
Jiahui Wang, Google X, USA
End-to-End Inverse Design for Repeatable, High-Performance Silicon Photonics
Xuan Wang, Meta, USA
Peripheral Light Field Display for Wide FOV
Shinton Wu, Univ. of Florida, CREOL, USA
Power Consumption of Light Engines for AR Glasses
Jonas Zuener, Vitrealab, Austria
Laser Light Source for AR LCoS Light Engines