Back to researchers
Lead Scientist
Dr. Sadia Noureen
Lecturer, Department of Electrical Engineering, ITU Lahore
Research Interests
Artificial Intelligence
Quantum Machine Learning
Generative & Agentic AI
Meta-optics
Computational Lithography
Computer Vision
Biography
Dr. Sadia Noureen is a Lecturer in the Department of Electrical Engineering at ITU Lahore and a Visiting Researcher at Nanjing University of Science and Technology (NJUST), China. She teaches undergraduate and graduate courses in AI, Machine Learning, Deep Learning, Computer Vision, Generative AI and Quantum AI.
She earned her PhD in Electrical Engineering from ITU (2018–2024) with a dissertation on deep-learning approaches to meta-device design, her MS from UCET, The Islamia University of Bahawalpur as a Gold Medalist (2014–2016), and her BS as a Silver Medalist (2009–2013). Her research spans artificial intelligence, computational electromagnetics, optics and photonics, meta-optics and intelligent healthcare systems.
She earned her PhD in Electrical Engineering from ITU (2018–2024) with a dissertation on deep-learning approaches to meta-device design, her MS from UCET, The Islamia University of Bahawalpur as a Gold Medalist (2014–2016), and her BS as a Silver Medalist (2009–2013). Her research spans artificial intelligence, computational electromagnetics, optics and photonics, meta-optics and intelligent healthcare systems.
Recent Publications +
- Design and Simulation of Compact AR/VR Optics: Integrating Meta Lens Array with RGB Filters — Journal of Materials Research, Volume 40, pages 2499–2510 (2025)
- SwinTransILT: A Physics Aware Swin Vision Transformer for Inverse Lithographic Mask Optimization — Journal of Micro/Nanopatterning, Materials, and Metrology (2025)
- Deep-learning Empowered Unique and Rapid Optimization of Meta-absorbers for Solar Thermophotovoltaics. — Optical Materials Express, Vol. 14, Issue 4, pp. 1025-1038 (2024)
- Trans-Reflective Tunable Color Filter Using Electro-Optic Material — Optical Materials Express, Vol. 14, Issue 2, pp. 522-537 (2024)
- Machine-learning-driven Accelerated Design-method for Metadevices — Materials Today Communications, Volume 37, 106951 (2023)
- Multi-Material Described Metasurface Solar Absorber Design with Absorption Prediction Using Machine Learning Models — Materials Today Communication, Volume 36, 106377 (2023)
- Physics-driven Tandem Inverse Design Neural Network for Efficient Optimization of UV-VIS Meta-devices — Applied Surface Science Advances, Volume 18, 100503 (2023)
- A Unique Physics-inspired Deep-learning-based Platform for Rapid Optical-response Prediction and Parametric-optimization for All-dielectric Metasurfaces — Nanoscale, 14, 16436-16449 (2022)
- Deep Learning based Hybrid Sequence Modeling for Optical Response Retrieval in Metasurfaces for STPV Applications — Optical Materials Express, Volume 11, Issue 9, pp. 3178-3193 (2021)