April 30, 2019 Miguel Atencia

AINanophotonics

 

Artificial Intellegence in nanophotonics (IWANN workshop)
           
  12-Jun   13-Jun
  Session AI@NF 1 Chair: Nikolay Zheludev   Session AI@NF 4 Chair: Cesare Soci
09:00-9:45 Prof David A. B. Miller, Stanford University (Keynote) (#72) Self-configuring optical mesh networks 09:00-9:45 Prof Bhavin Shastri, Queen’s University (Keynote) (#182) Advances in Neuromorphic Photonics
9:45-10:00 Discussion 9:45-10:00 Discussion
10:00 Prof Haim Suchowski, Tel Aviv University (#71) Deep Learning for Design and Retrieval of Nano-photonic Structures 10:00 Prof Vladimir Shalaev, Purdue University (#52) Artificial-Intelligence-Assisted Approach for Speeding Up Quantum Optical Measurements
10:30 Dr Daniel Brunner, CNRS, FEMTO ST Institute (#30) Greedy reinforcement learning in large photonic neural networks: empirical findings of convexity and scaling 10:30 Prof Mark I. Stockman, Georgia State University (#88) Quantum Solids in Ultrafast Strong Laser Fields: Topological Nanophotonic Phenomena
11:00 Coffee   11:00 Coffee
  Session AI@NF 2 Chair: Allan Miller   Session AI@NF 5 Chair: Nader Engheta
11:30 Prof Andrea Fratalocchi, KAUST (#49) Machine learning metamaterials with 98% experimental efficiency and 50 nm thickness for broadband vectorial light control 11:30 Dr Ben Mills, Univ. of Southampton (#167) Smart Predictive Laser Materials Processing via Deep Learning
12:00 Prof Junsuk Rho, POSTECH (#28) Deep learning assisted design of plasmonic nanostructures and metamaterials 12:00 Prof Brian E. Hayden, Univ. of Southampton (#114) Building a Materials and Device Data-Bases for the Development of Photonic Nano-Structures through Machine Learning
12:30 Prof Alexandra Boltasseva, Purdue University (#50) Machine-Learning-Assisted Topology Optimization for Refractory Photonics 12:30 Prof Cesare Soci
 Cognitive Photonic Networks
13:00 Plenary (IWANN)   13:00 Prof Marin Soljacic, MIT (IWANN Plenary) (#25) Neural Networks and Photonics (plenary)
13:30     13:30
14:00 Lunch   14:00 Lunch  
14:30     14:30    
15:00     15:00    
15:30     15:30    
  Session AI@NF 3 Chair: Paul Prucnal   Session AI@NF 6 Chair: Marin Soljacic
16:00-16:45 Prof Nader Engheta, University of Pennsylvania (Keynote) (#105) Metamaterial Computing Machines 16:00 Dr Laura Pilozzi, Institute for Complex Systems (ISC), CNR (#102) Topological photonic phases through artificial neural networks
16:45 -17:00 Discussion 16:30 Dr Marina Radulaski, Stanford University/ UC Davis (#64) Nanophotonic Architecture for Machine Learning Hardware
17:00 Prof Uriel Levy, The Hebrew University, Israel
 TBA 17:00 Javier García de Abajo, ICFO (#120) Analog computing with optical excitations in the near field
17:30 Mr Logan Wang Su, Stanford University (#51) From inverse design to implementation of practical photonics 17:30 Dr Predrag Milojkovic, US Office of Naval Research Global (#158) Introduction to Office of Naval Research Global – Naval Science and Technology International Office
18:00 Prof Claudio Conti, Sapienza University of Rome Deep reservoir computing in tumor cells and Ising machines by spatial light modulators 18:00 Dr. Satoshi Kako, NTT Basic Research Laboratories Coherent Ising Machine – Optical Neural Network operating at the Quantum Limit
18:30 Coffee 18:30 Coffee
19:00 Poster   19:00 Poster  
19:30     19:30    
20:00     20:00    

 

 

  Accepted presentations
25 Marin Soljacic. Neural Networks and Photonics
28 Junsuk Rho. Designing nanophotonic structures using conditional- deep convolutional generative adversarial network
30 Daniel Brunner. Greedy reinforcement learning in large photonic neural networks: empirical findings of convexity and scaling
49 Fedor Getman, Maksim Makarenko, Andrea Fratalocchi and Arturo Burguete Lopez. Machine learning metamaterials with 98% experimental efficiency and 50 nm thickness for broadband vectorial light control
50 Zhaxylyk Kudyshev, Alexander Kildishev, Vlad Shalaev and Alexandra Boltasseva. Machine-Learning-Assisted Topology Optimization for Refractory Photonics
51 Logan Su and Jelena Vuckovic. From inverse design to implementation of practical photonics
52 Zhaxylyk Kudyshev, Simeon Bogdanov, Theodor Isacsson, Alexander Kildishev, Alexandra Boltasseva and Vladimir Shalaev. Artificial-Intelligence-Assisted Approach for Speeding Up Quantum Optical Measurements
61 Hamza Kurt. Design of different nanophotonic structures beyond intuition
64 Marina Radulaski, Ranojoy Bose, Tho Tran, Thomas Van Vaerenbergh and Raymond Beausoleil. Nanophotonic Architecture for Machine Learning Hardware
71 Itzik Malkiel, Michael Mrejen, Achiya Nagler, Uri Arieli, Lior Wolf and Haim Suchowski. Deep Learning for Design and Retrieval of Nano-photonic Structures
72 David Miller. Self-configuring optical mesh networks
88 Mark Stockman. Quantum Solids in Ultrafast Strong Laser Fields: Topological Nanophotonic Phenomena
102 Laura Pilozzi, Francis A. Farrelly, Giulia Marcucci and Claudio Conti. Topological photonic phases through artificial neural networks.
105 Nader Engheta. Metamaterial Computing Machines
114 Brian Hayden. Building a Materials and Device Data-Bases for the Development of Photonic Nano-Structures through Machine Learning.
120 Javier García de Abajo. Analog computing with optical excitations in the near field
158 Predrag Milojkovic. Introduction to Office of Naval Research Global – Naval Science and Technology International Office
167 Ben Mills, Daniel Heath, James Grant-Jacob, Yunhui Xie, Benita Mackay, Michael McDonnell, Matthew Praeger and Robert W. Eason. Smart Predictive Laser Materials Processing via Deep Learning
181 Bhavin Shastri, Alexander Tait, Mitchell Nahmias, Thomas Ferreira de Lima, Hsuan-Tung Peng and Paul Prucnal. Application for Neuromorphic Silicon Photonics
182 Paul R. Prucnal, Alexander Tait, Mitchell Nahmias, Thomas Ferreira de Lima, Hsuan-Tung Peng and Bhavin Shastri. Advances in Neuromorphic Photonics