Journals Papers

  1. Zandehshahvar, M., van Assen, M., Maleki, H., Kiarashi, Y., De Cecco, C., and Adibi, A., 2021. Toward understanding COVID-19 pneumonia: A deep-learning-based approach for severity analysis and monitoring the disease, Scientific Reports, 11(1), 1-10. [link]

  2. Zandehshahvar, M., Kiarashi, Y., Chen, M., Barton, R., & Adibi, A., 2021. Inverse design of photonic nanostructures using dimensionality reduction: reducing the computational complexity. Optics Letters, 46(11), 2634-2637. [link]

  3. Zandehshahvar, M., Kiarashi, Y., Zhu, M., Maleki, H., Brown, T., and Adibi, A., 2021. Manifold learning for Knowledge discovery and intelligent inverse design of photonic nanostructures: Breaking the geometric complexity. [link]

  4. Kiarashinejad, Y., Zandehshahvar, M., Abdollahramezani, S., Hemmatyar, O., Pourabolghasem, R. and Adibi, A., 2020. Knowledge discovery in nanophotonics using geometric deep learning. Advanced Intelligent Systems, 2(2), p.1900132. [link]

  5. Abdollahramezani, S., Hemmatyar, O., Taghinejad, H., Krasnok, A., Kiarashinejad, Y., Zandehshahvar, M., Alù, A. and Adibi, A., 2020. Tunable nanophotonics enabled by chalcogenide phase-change materials. Nanophotonics, 1(ahead-of-print). [link]

  6. Abdollahramezani, S., Hemmatyar, O., Taghinejad, M., Taghinejad, H., Kiarashinejad, Y., Zandehshahvar, M., Fan, T., Deshmukh, S., Eftekhar, A.A., Cai, W. and Pop, E., 2020. Dynamic hybrid metasurfaces. arXiv preprint arXiv:2008.03905. [link]

  7. Kiarashinejad, Y., Abdollahramezani, S., Zandehshahvar, M., Hemmatyar, O. and Adibi, A., 2019. Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices. Advanced Theory and Simulations, 2(9), p.1900088. [link]

  8. Hemmatyar, O., Abdollahramezani, S., Kiarashinejad, Y., Zandehshahvar, M. and Adibi, A., 2019. Full color generation with fano-type resonant HfO 2 nanopillars designed by a deep-learning approach. Nanoscale, 11(44), pp.21266-21274. [link]

Selected Conference Papers and Presentations

  1. Zandehshahvar, M., Kiarashinejad, Y., Zhu, M., Maleki, H., Hemmatyar, O., Abdollahramezani, S., Pourabolghasem, R., and Adibi, A., 2020, November. Accelerating Inverse Design of Nanostructures Using Manifold Learning. Ml4Eng at NeurIPS. [link]

  2. Zandehshahvar, M., Kiarashinejad, Y., Hemmatyar, O., Abdollahramezani, S., Pourabolghasem, R. and Adibi, A., 2020, May. Cracking the Design Complexity of Nanostructures Using Geometric Deep Learning. In CLEO: Science and Innovations (pp. SF1R-4). Optical Society of America.

  3. Kiarashinejad, Y., Zandehshahvar, M., Abdollahramezani, S., Hemmatyar, O., Pourabolghasem, R. and Adibi, A., 2020, May. Geometric Deep Learning Unlocks the Underlying Physics of Nanostructures. In CLEO: Science and Innovations (pp. JTh2A-15). Optical Society of America.

  4. Zandehshahvar, M., Kiarashinejad, Y., Abdollahramezani, S., Hemmatyar, O., Maleki, H. and Adibi, A., 2020, March. Sample-efficient machine-learning method for designing photonic nanostructures (Conference Presentation). In Photonic and Phononic Properties of Engineered Nanostructures X (Vol. 11289, p. 112890O). International Society for Optics and Photonics.

  5. Zandehshahvar, M., Hemmatyar, O., Kiarashinejad, Y., Abdollahramezani, S. and Adibi, A., 2019, September. Dimensionality reduction based method for design and optimization of optical nanostructures using neural network. In Frontiers in Optics (pp. FM5C-2). Optical Society of America.

  6. Kiarashinejad, Y., Abdollahramezani, S., Zandehshahvar, M., Hemmatyar, O. and Adibi, A., 2019, September. Nanophotonics design platform based on double-step dimensionality reduction. In Frontiers in Optics (pp. JTu3A-4). Optical Society of America.

  7. Kiarashinejad, Y., Masnadi-Shirazi, M., Yazdi, M. and Zandehshahvar, M., 2015, November. Quality enhancement of low-resolution face images. In 2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) (pp. 228-231). IEEE.