Technical Conference: 26 - 30 October 2025
Exhibition: 28 - 29 October 2025
Colorado Convention Center | Denver, Colorado, USA

Technical Conference: 26 - 30 October 2025
Exhibition: 28 - 29 October 2025
Colorado Convention Center | Denver, Colorado, USA

Theme: Machine Learning

Attendees

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
Tyler Hughes
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

Yijun (Joy) Ding, Synopsys, USA
Automated Design for Large and Manufacturable Metalenses

Priyanka Ghosh, MTC, UK
AI for High-Power Laser Based Manufacturing, Present and Future

Ighodalo Idehenre, Core4ce, USA
MAISY: an AI Guided Deep Learning Software Package for the Design and Fabrication of Next-Generation Optics

Yannick Mahlau, Leibniz University Hannover, Germany
Title to be Announced

Dylan McGuire, Ansys, Inc., Canada
Multi-Scale Photonic Inverse Design, Optimization and Sensitivity Analysis for Advanced Optical IO

Raphael Pestourie, Georgia Institute of Technology, USA
Machine Learning-Enhanced Optimization for Metamaterial Design

Martin Schubert, invrs.io, USA
Tools for Inverse Design Algorithm Research

Jiahui Wang, Google X, USA
End-to-End Inverse Design for Repeatable, High-Performance Silicon Photonics