Assist. Prof. Dr. Yan Zeng | AI in Engineering | Best Researcher Award

Published on by Superior Awards

Assist. Prof. Dr. Yan Zeng | AI in Engineering | Best Researcher Award

Assist. Prof. Dr. Yan Zeng | Hangzhou Dianzi University | China

Assist. Prof. Dr. Yan Zeng, an accomplished associate professor at the School of Computer Science, Hangzhou Dianzi University, has made significant contributions in the fields of distributed and parallel computing, distributed machine learning, and big data analytics. After earning her PhD from the Institute of Software, Chinese Academy of Sciences in 2016, her research has focused on advancing large-scale computation and data-intensive systems.  The Key Research and Development Program of Zhejiang Province, the Yangtze River Delta Project, and the Natural Science Foundation of Zhejiang Province. Her academic influence is reflected in 173 citations by 161 documents, 42 published papers, and an h-index of 9, demonstrating strong research impact and visibility. With 10 peer-reviewed publications in SCI and Scopus-indexed journals, Yan Zeng’s scholarly output showcases innovation in computational frameworks and distributed systems. Furthermore, she has been actively involved in practical technological advancements, holding 34 patents that bridge theoretical insights with industrial applications. Through her extensive research, publication record, and innovation-driven approach, Yan Zeng continues to play a pivotal role in shaping advancements in computer science and data engineering.

Profile: Scopus

Featured Publications

Zeng, Y., et al. (2025). FedAMM: Federated learning for brain tumor segmentation with arbitrary missing modalities [Conference paper]. Proceedings of the International Conference on Artificial Intelligence and Machine Learning.

Zeng, Y., et al. (2025). TransAware: An automatic parallel method for deep learning model training with global model structure awareness [Conference paper]. Proceedings of the International Conference on Advanced Computing and Applications.

Zeng, Y., et al. (2025). A correlation analysis-based federated learning framework for defending against collusion-free-riding attacks. Cybersecurity, 2025(1), 1–12.

Zeng, Y., et al. (2025). FedAEF: Optimizing federated learning with mining and enhancing local data features. Cluster Computing, 2025(1), 1–15.

Assoc. Prof. Dr. Alireza Zirak | Robotics & Automation | Best Researcher Award

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Assoc. Prof. Dr. Alireza Zirak | Robotics & Automation | Best Researcher Award

Assoc. Prof. Dr. Alireza Zirak | Nuclear Science and Technology Research Institute | Iran

Prof. Alireza Zirak is an Associate Professor at the Laser and Optics Research School, specializing in electronics, telecommunications, and advanced photonics. His research focuses on tomography and radiotherapy systems, quantum technology, digital signal and image processing, laser applications, IoT, embedded systems, and stochastic modeling. He has received multiple national honors, including top rankings in Iran’s University Entrance Examination and Mathematics Olympiad, the National Science Festival design prize, and recognition at the National Engineer’s Day festival. Prof. Zirak has also contributed as a scientific committee member for major conferences and received acknowledgments from institutions such as the Vice Presidency for Science and Technology. With expertise in optimization, microcontroller programming, and photonics system design, he has played a significant role in advancing interdisciplinary applications of electronics and optics. His academic influence is reflected in 174 citations from 139 documents, 20 publications, and an h-index of 8, demonstrating both his productivity and impact in modern engineering and photonics research.

Profile: Scopus | Google Scholar | Orcid

Featured Publications

  • Nankali, S., Worm, E. S., Weber, B., Høyer, M., Zirak, A., Poulsen, P. R. (2018). Geometric and dosimetric comparison of four intrafraction motion adaptation strategies for stereotactic liver radiotherapy. Physics in Medicine & Biology, 63(14), 145010.

  • Khezerloo, D., Nedaie, H. A., Zirak, A., Farhood, B., Banaee, N., Alidokht, E. (2018). Dosimetric properties of new formulation of PRESAGE® with tin organometal catalyst: Development of sensitivity and stability to megavoltage energy. Journal of Cancer Research and Therapeutics, 14(2), 308–315.

  • Rajabi, H., & Zirak, A. (2016). Speckle noise reduction and motion artifact correction based on modified statistical parameters estimation in OCT images. Biomedical Physics & Engineering Express, 2(3), 035012.

  • Zirak, A. R., & Khademi, M. (2007). An efficient method for model refinement in diffuse optical tomography. Optics Communications, 279(2), 273–284.

  • Zirak, A. R., & Roshani, S. (2016). A reduced switch voltage stress class E power amplifier using harmonic control networks. International Journal of Advanced Computer Science and Applications, 7, 38–42.