| Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review O Khatib, S Ren, J Malof, WJ Padilla Advanced Functional Materials, 2101748, 2021 | 102 | 2021 |
| Neural-adjoint method for the inverse design of all-dielectric metasurfaces Y Deng, S Ren, K Fan, JM Malof, WJ Padilla Optics Express 29 (5), 7526-7534, 2021 | 65 | 2021 |
| Benchmarking deep inverse models over time, and the neural-adjoint method S Ren, W Padilla, J Malof (NeurIPS 2020) Advances in Neural Information Processing Systems 33 33 (2020 …, 2020 | 47 | 2020 |
| Inverse deep learning methods and benchmarks for artificial electromagnetic material design S Ren, A Mahendra, O Khatib, Y Deng, WJ Padilla, JM Malof Nanoscale 14, 3958-3969, 2022 | 31 | 2022 |
| Learning the physics of all‐dielectric metamaterials with deep Lorentz neural networks O Khatib, S Ren, J Malof, WJ Padilla Advanced Optical Materials 10 (13), 2200097, 2022 | 27 | 2022 |
| Direct Evidence of Void-Induced Structural Relaxations in Colloidal Glass Formers CHL Cho-Tung Yip, Masaharu Isobe, Chor-Hoi Chan, Simiao Ren, Kin-Ping Wong ... Phys. Rev. Lett. 125 (258001), 2020 | 25 | 2020 |
| Segment anything, from space? S Ren, F Luzi, S Lahrichi, K Kassaw, LM Collins, K Bradbury, JM Malof Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 21 | 2024 |
| Automated extraction of energy systems information from remotely sensed data: A review and analysis S Ren, W Hu, K Bradbury, D Harrison-Atlas, LM Valeri, B Murray, ... Applied Energy 326, 119876, 2022 | 20 | 2022 |
| Utilizing geospatial data for assessing energy security: Mapping small solar home systems using unmanned aerial vehicles and deep learning S Ren, J Malof, TR Fetter, R Beach, J Rineer, K Bradbury ISPRS Int. J. Geo-Inf 11 (4), 222, 2022 | 11 | 2022 |
| Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials Y Deng*, J Dong*, S Ren*, O Khatib, M Soltani, V Tarokh, W Padilla, ... (NeurIPS 2021) Advances in Neural Information Processing Systems 34 …, 2021 | 10 | 2021 |
| Deep inverse photonic design: a tutorial Y Deng, S Ren, J Malof, WJ Padilla Photonics and Nanostructures-Fundamentals and Applications 52, 101070, 2022 | 8 | 2022 |
| Self-supervised encoders are better transfer learners in remote sensing applications ZD Calhoun, S Lahrichi, S Ren, JM Malof, K Bradbury Remote Sensing 14 (21), 5500, 2022 | 5 | 2022 |
| Informed deep learning in metamaterials O Khatib, S Ren, J Malof, WJ Padilla 2023 International Applied Computational Electromagnetics Society Symposium …, 2023 | 4 | 2023 |
| Heterogeneity and Memory Effect in the Sluggish Dynamics of Vacancy Defects in Colloidal Disordered Crystals and Their Implications to High‐Entropy Alloys CH Chan, Q Huo, A Kumar, Y Shi, H Hong, Y Du, S Ren, KP Wong, ... Advanced Science 9 (36), 2205522, 2022 | 4 | 2022 |
| Machine learning for mie-tronics W Li, HB Sedeh, WJ Padilla, S Ren, J Malof, NM Litchinitser arXiv preprint arXiv:2305.18589, 2023 | 1 | 2023 |
| Deep Active Learning for Scientific Computing in the Wild S Ren, Y Deng, WJ Padilla, L Collins, J Malof arXiv preprint arXiv:2302.00098, 2023 | 1 | 2023 |
| Closing the Domain Gap -- Blended Synthetic Imagery for Climate Object Detection C Kornfein, F Willard, C Tang, Y Long, S Jain, J Malof, S Ren, K Bradbury NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning, 2022 | 1 | 2022 |
| Machine Learning for Exotic Metasurfaces Y Deng, S Ren, K Fan, J Malof, WJ Padilla 2020 45th International Conference on Infrared, Millimeter, and Terahertz …, 2020 | 1 | 2020 |
| Transfer learning for metamaterial design and simulation R Peng, S Ren, J Malof, WJ Padilla Nanophotonics, 2024 | | 2024 |
| Machine learning assisted design of effective potentials, surface ligand patterns, and annealing protocols for colloidal self-assembly G Arya, Y Zhou, PA Lin, S Callioglu, S Bore, S Ren, Y Yang, A Tao, ... Bulletin of the American Physical Society, 2024 | | 2024 |
Jordan M MalofAssistant Professor, University of MontanaVerified email at duke.edu
