Shima Kamyab | Reverse Osmosis | Best Researcher Award

Published on by Natural Scientist Awards

Dr. Shima Kamyab | Reverse Osmosis | Best Researcher Award

Postdoctoral Researcher | University of Victoria | Canada

Dr. Shima Kamyab is an accomplished researcher whose work focuses on advancing generative artificial intelligence, deep learning, and computational modeling for complex data-driven applications. Her research encompasses diverse yet interrelated domains such as large language models (LLMs), image processing, 3D point cloud analysis, inverse problems, and additive manufacturing. She has demonstrated exceptional capability in integrating transformer-based AI with time series forecasting and optimization, contributing significantly to the development of intelligent prediction and reconstruction frameworks. Dr. Kamyab’s innovative research on generative AI for shipping freight rate forecasting and 3D morphable models has led to the preparation of high-impact journal publications and recognition through prestigious awards, including Best Ph.D. Student Award and the 2nd Best Paper Award at the HI-AM Conference in Canada. Her scientific contributions are well-documented in leading international journals, covering topics such as multimodal optimization, hyperspectral imaging, and deep learning methods for inverse problems. Through her experience in academia and collaborative research, she has displayed strong analytical, programming, and problem-solving skills, supported by proficiency in Python, MATLAB, and deep learning frameworks like PyTorch and TensorFlow. Beyond technical expertise, she has played an active role in knowledge dissemination as a lecturer, workshop instructor, and mentor, fostering the growth of emerging researchers. Dr. Kamyab’s research excellence is further underscored by her ability to bridge theoretical AI advancements with industrial and applied solutions, making her a highly impactful and forward-looking scientist well-suited for recognition through the Best Researcher Award.

Profile: ORCID | Google Scholar

Featured Publications

  • Yazdani, S., Nezamabadi-Pour, H., & Kamyab, S. (2014). A gravitational search algorithm for multimodal optimization. Swarm and Evolutionary Computation, 14, 1–14.

  • Moosavi-Nasab, M., Khoshnoudi-Nia, S., Azimifar, Z., & Kamyab, S. (2021). Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis. Scientific Reports, 11(1), 5094.

  • Kamyab, S., & Eftekhari, M. (2016). Feature selection using multimodal optimization techniques. Neurocomputing, 171, 586–597.

  • Haghbayan, P., Nezamabadi-Pour, H., & Kamyab, S. (2017). A niche GSA method with nearest neighbor scheme for multimodal optimization. Swarm and Evolutionary Computation, 35, 78–92.

  • Kamyab, S., Azimifar, Z., Sabzi, R., & Fieguth, P. (2022). Deep learning methods for inverse problems. PeerJ Computer Science, 8, e951.