Dr. Yashar Kiarashi Profile

Dr. Yashar Kiarashi

Postdoctoral Research Fellow

SPIE Involvement:

Author

Area of Expertise:

Optical Computing , Machine Learning , Dimensionality Reduction , Deep Learning , Artificial Intelligence , Inverse Design

Publications (14)

Proceedings Article | 8 June 2024 Presentation

New machine learning paradigms for knowledge discovery and inverse design of photonic nanostructures

Ali Adibi, Mohammadreza Zandehshahvar, Mohammad Hadighehjavani, Yashar Kiarashi, Mahmoodreza Marzban, Mohammad Tavakol Harandi

Proceedings Volume PC13030, PC130300C (2024) https://doi.org/10.1117/12.3013800

KEYWORDS: Design and modelling, Knowledge discovery, Photonic nanostructures, Machine learning, Algorithm development, Physics, Nanophotonics, Light-matter interactions

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Proceedings Article | 7 April 2023 Presentation

Bayesian neural networks for severity assessment of COVID-19 pneumonia from chest x-ray using a multi-reader and binational dataset

Mohammadreza Zandehshahvar, Marly van Assen, Eun Young Kim, Yashar Kiarashi, Vikranth Keerthipati, Arthur Stillman, Peter Filev, Amir Davarpanah, Eugene Berkowitz, Stefan Tigges, Scott Lee, Brianna Vey, Carlo De Cecco, Ali Adibi

Proceedings Volume 12465, 124650G (2023) https://doi.org/10.1117/12.2653346

KEYWORDS: Chest imaging, Neural networks, Lung, Data modeling, Radiology, Monte Carlo methods, Machine learning

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Proceedings Article | 17 March 2023 Poster

Predictions of nonlinear generation from dielectric metasurfaces using a convolutional neural network

Muliang Zhu, Mohammadreza Zandehshahvar, Yashar Kiarashinejad, Sajjad Abdollahramezani, Ali Adibi

Proceedings Volume PC12431, PC1243119 (2023) https://doi.org/10.1117/12.2661614

KEYWORDS: Dielectrics, Convolutional neural networks, Silicon, Near infrared, Ultrafast phenomena, Third-harmonic generation, Physics, Optical simulations, Nanotechnology, Nanostructures

Proceedings Article | 17 March 2023 Presentation

Metric learning: a new approach for defining similarity measures for nanophotonics

Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Daqian Bao, Mohammad Hadigheh Javani, Reza Pourabolghasem, Ali Adibi

Proceedings Volume PC12431, PC124310P (2023) https://doi.org/10.1117/12.2661572

KEYWORDS: Nanophotonics

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Proceedings Article | 15 March 2023 Presentation + Paper

New paradigms in manifold learning for knowledge discovery and inverse design of photonic nanostructures

Mohammadreza Zandehshahvar, Mohammad Hadigheh Javani, Yashar Kiarashi, Muliang Zhu, Tyler Brown, Daqian Bao, Mahmoodreza Marzban, Reza Pourabolghasem, Ali Adibi

Proceedings Volume 12430, 1243009 (2023) https://doi.org/10.1117/12.2662754

KEYWORDS: Design and modelling, Machine learning, Photonic nanostructures, Nanophotonics, Knowledge discovery, Modeling, Silica, Nanostructures, Thin films, Computer aided design

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Proceedings Article | 5 March 2022 Presentation

Manifold learning: A promising tool for knowledge discovery and inverse design in nanophotonics

Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Tyler Brown, Mohammad Hadighe Javani, Ali Adibi

Proceedings Volume PC12010, PC120100U (2022) https://doi.org/10.1117/12.2617486

KEYWORDS: Nanophotonics, Nanostructures, Knowledge discovery, Artificial intelligence, Physics, Visualization, Structural design, Optimization (mathematics), Light-matter interactions, Genetic algorithms

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Proceedings Article | 5 March 2022 Presentation

Machine learning for efficient inverse design of nanophotonics structures

Mohammadreza Zandehshahvar, Mohammad Hadighe Javani, Michael Chen, Tyler Brown, Yashar Kiarashi, Ali Adibi

Proceedings Volume PC12010, PC120100W (2022) https://doi.org/10.1117/12.2617485

KEYWORDS: Nanophotonics, Structural design, Machine learning, Data modeling, Physics, Nanostructures, Knowledge discovery, Inverse problems

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Proceedings Article | 3 August 2021 Presentation

Latent learning for design and knowledge discovery in nanophotonics

Yashar Kiarashi, Mohammadreza Zandehshahvar, Muliang Zhu, Hossein Maleki, Sajjad Abdollahramezani, Tyler Brown, Reid Barton, Ali Adibi

Proceedings Volume 11795, 1179505 (2021) https://doi.org/10.1117/12.2595199

KEYWORDS: Nanophotonics, Knowledge discovery, Physics, Nanostructures, Light-matter interactions

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Proceedings Article | 5 March 2021 Presentation

Revealing the hidden capacity of artificial intelligence in nanoscience: physics-driven metric learning

Hossein Maleki, Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Ali Adibi

Proceedings Volume 11694, 116940O (2021) https://doi.org/10.1117/12.2590756

KEYWORDS: Signal attenuation, Machine learning, Evolutionary algorithms, Artificial intelligence, Nanophotonics, Photonic nanostructures, Performance modeling, Nanostructures, Knowledge discovery, Error analysis

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Proceedings Article | 5 March 2021 Presentation

Manifold learning for knowledge discovery and design in nanophotonics

Yashar Kiarashi, Mohammadreza Zandehshahvar, Ali Adibi

Proceedings Volume 11694, 116940M (2021) https://doi.org/10.1117/12.2590199

KEYWORDS: Nanostructures, Nanophotonics, Knowledge discovery, Wave propagation, Physics, Light-matter interactions

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Proceedings Article | 5 March 2021 Presentation

Breaking the geometric complexity of nanostructures using manifold learning

Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Hossein Maleki, Tyler Brown, Ali Adibi

Proceedings Volume 11694, 116940S (2021) https://doi.org/10.1117/12.2590200

KEYWORDS: Nanostructures, Structural design, Nanolithography, Artificial intelligence, Silica, Physics, Photonic nanostructures, Nanotechnology, Nanophotonics, Fabrication

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Proceedings Article | 10 March 2020 Presentation

Sample-efficient machine-learning method for designing photonic nanostructures (Conference Presentation)

Mohammadreza Zandehshahvar, Yashar Kiarashinejad, Sajjad Abdollahramezani, Omid Hemmatyar, Hossein Maleki, Ali Adibi

Proceedings Volume 11289, 112890O (2020) https://doi.org/10.1117/12.2554910

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Proceedings Article | 10 March 2020 Presentation

Deep-learning-based design of Fano resonant HfO2 metasurfaces for full color generation (Conference Presentation)

Omid Hemmatyar, Sajjad Abdollahramezani, Yashar Kiarashinejad, Mohammadreza Zandehshahvar, Ali Adibi

Proceedings Volume 11289, 112890F (2020) https://doi.org/10.1117/12.2554907

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Proceedings Article | 4 March 2019 Presentation

Mitigating inverse design complexity of nano-antennas using a novel dimensionality reduction approach (Conference Presentation)

Yashar Kiarashinejad, Sajjad Abdollahramezani, Tianren Fan, Ali Adibi

Proceedings Volume 10927, 109270C (2019) https://doi.org/10.1117/12.2517651

KEYWORDS: Nanoantennas, Wave propagation, Radio propagation, Electromagnetic radiation, Nanostructures, Printing, Absorption, Plasmonics, Titanium dioxide, Resolution enhancement technologies

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We leverage a dimensionality reduction approach to develop a novel inverse design platform applicable to a wide class of optical nanoantennas. The proposed dimensionality reduction technique uses a high level of correlation (in frequency and space domains) in the propagation of electromagnetic waves to considerably reduce the dimensionality of the response space of the problem. In addition, the correlation that often exists among the effects of design parameters on the response of the structure (i.e., selecting more design parameters than needed for uniquely identifying a structure for a given input-output relation) is used to reduce the dimensionality of the design space. In addition to the considerable mitigation of computation time and complexity, the two key features of dimensionality reduction, i.e., : 1) the ability to train a NN and later use it for a large class of problems, and 2) the possibility of analytically relating the reduced design space to the original design space to obtain valuable intuitive information about the roles of each design parameter in the overall performance of the nanostructure, highlight the superiority of the proposed approach. This is in contrast to existing analysis and optimization techniques, which require an intensive repetition of the simulations for each design problem without providing an intuitive understanding of the roles of design parameters. As a proof of concept, we apply this approach to a nanoscale structural color recently emerged as a promising candidate to organic colors in the printing technology. To circumvent the high absorption loss and efficiency of plasmonic color generators, we harness the fundamental dipolar Mie resonances of an array of asymmetric titanium dioxide elliptical nanopillars. We will further experimentally demonstrate such an optimized polarization-sensitive all-dielectric significantly enhance the resolution, saturation, and hue of color palettes. Such a novel inverse design approach highlights the performance of machine learning based approaches in developing highly-efficient metastructures.

Showing 5 of 14 publications

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