• Technical Conference:  16 – 20 October 2022
  • Science + Industry Showcase:   18 – 19 October 2022
  • Joseph A. Floreano Rochester Riverside Convention Center Rochester, New York, USA

2021 Theme: Machine Learning

We welcome attendees to the Machine learning theme at the Frontiers in Optics (FiO) meeting. The Machine Learning theme provides an interdisciplinary platform to learn about and discuss a wide range of optics/photonics topics that machine learning has recently impacted on. The theme features 1 visionary speaker, 1 tutorial speaker, and 15 invited speakers spanning academia, industry and government institutions.

These exciting speakers will provide both historical retrospectives and state-of-the-art views on emerging machine learning techniques applied to optics/photonics applications. The topics include computational imaging, metaphotonics, optical computing, biophotonics and many more. Through these talks, attendees will learn about the newest machine learning technologies applied to optics and photonics and some of the emerging concepts in embedding optical physics into machine learning designs.

Theme Coordinators
Aydogan Ozcan, University of California, Los Angeles, USA
Lei Tian, Boston University, USA
Abbie Watnik, Naval Research Laboratory, USA


  • Eli Yablonovitch

    Visionary Speaker
    Eli Yablonovitch

    Professor, University of California Berkeley, USA

  • Jiangying Zhou

    Visionary Speaker
    Jiangying Zhou

    Program Manager, Defense Advanced Research Projects Agency, USA


Tuesday, 2 November  
09:15 – 10:00

Visionary Speaker: Eli Yablonovitch, University of California Berkeley, USA

Physics Does Digital Optimization—for Machine Learning, Control Theory, Backpropagation, etc.

15:30 - 17:00

Smart Cameras with Machine Learning 

David Brady, University of Arizona, USA
Achuta Kadambi, Akasha Imaging / University of California, Los Angeles, USA
Xin Yuan, Westlake University, China

Wednesday, 3 November  
08:00 – 09:00

Tutorial Speaker: George Barbastathis, Massachusetts Institute of Technology, USA

Incorporating Physics Priors into Machine Learning for Inverse Problems

10:15 – 11:00

Visionary Speaker: Jiangying Zhou, Defense Advanced Research Projects Agency, USA

Re-thinking Sensing in the Age of AI

15:30 – 17:00

Optical Computing in Machine Learning

Shanhui Fan, Stanford University, USA
Adrian Stern, Ben-Gurion University of the Negev, Israel
Marin Soljačić, Massachusetts Institute of Technology, USA

17:30 - 19:00

Machine Learning for Extreme Measurements

Peyman Milanfar, Google Research, USA
Katie Bouman, California Institute of Technology, USA
Alexandra Boltasseva, Purdue University, USA

Thursday, 4 November

10:30 - 12:00

Computational Imaging with Machine learning

Gordon Wetzstein, Stanford University, USA
Yair Rivenson, Pictor Labs, USA
Luat Vuong, University of California, Riverside, USA

15:00 - 17:00

Computational Microscopy with Machine learning

Sixian You, Massachusetts Institute of Technology, USA
Laura Waller, University of California, Berkeley, USA
Giovanni Volpe, University of Gothenburg, Sweden