Hongming Zhang | Engineering | Best Researcher Award

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Assoc. Prof. Dr. Hongming Zhang | Engineering | Best Researcher Award

Academician | Beijing University of Posts and Telecommunications | China

Dr. Hongming Zhang is an accomplished Associate Professor at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, China. He earned his Ph.D. in Electrical and Electronic Engineering from the University of Southampton under the supervision of Prof. Lajos Hanzo and Prof. Lie-Liang Yang, following his M.Sc. from Southampton, B.Eng. with Honors from City, University of London, and B.Eng. in Information Engineering from Nanjing University of Aeronautics and Astronautics. Before joining BUPT, he conducted postdoctoral research at Columbia University, contributing to advancements in wireless communication technologies. His research focuses on wireless communications, heterogeneous networking, underwater acoustics, and AI-driven optimization, particularly in areas such as federated learning, intelligent reflecting surfaces, and 6G network design. As a prolific and highly cited researcher, Dr. Zhang has co-authored more than forty IEEE journal papers in collaboration with leading international scholars. His publication record includes 59 documents cited by 967 other documents, totaling 1,207 citations. He has served as an Associate Editor for Electronics Letters and a Review Editor for Frontiers in Communications and Networks. His excellence has been recognized through numerous honors, including the Boosting Project Award for Young Talents from the China Association for Science and Technology, multiple IEEE Best Paper Awards, and the Science and Technology Awards from the China Institute of Communications and the Radio Association of China. His work bridges theory and application, advancing intelligent, energy-efficient communication systems and inspiring innovation within the global telecommunications community.

Profile : Scopus | ORCID 

Featured Publications 

Zhang, H., Yang, L.-L., & Hanzo, L. (2016). Performance analysis of OFDM systems in dispersive indoor power line channels. IET Communications. [Cited by 35]

Zhang, H., Jiang, C., & Hanzo, L. (2019). Linear precoded index modulation. IEEE Transactions on Communications. [Cited by 120]

Zhang, H., & Hanzo, L. (2020). Federated learning assisted multi-UAV networks. IEEE Transactions on Vehicular Technology. [Cited by 90]

Jiang, H., Xiong, B., & Zhang, H. (2023). Hybrid far- and near-field modeling for RIS assisted V2V channels. IEEE Transactions on Wireless Communications. [Cited by 45]

Zhang, H., et al. (2024). Space-time shift keying aided OTFS modulation for orthogonal multiple access. IEEE Transactions on Communications. [Cited by 20]

Belkacem Bekhiti | Engineering | Best Researcher Award

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Prof. Belkacem Bekhiti | Engineering | Best Researcher Award

Prof. Belkacem Bekhiti | Institute of Aeronautics and Space Studies, University of Blida | Algeria

Dr. Bekhiti Belkacem is a distinguished academic and researcher in control theory, robotics, and aerospace engineering, currently serving as a Lecturer at the Institute of Aeronautics and Space Studies, Blida University 1, Algeria. His expertise spans guidance, navigation, and control systems, integrating theoretical modeling with real-world aerospace applications. He holds a Doctorate in Electrical Engineering with a specialization in Automatic Control from the University of Boumerdes, a Magister in Advanced Control of Complex Systems from the National Polytechnic School, Oran, a Master’s in Automatic Control from the University of Djelfa, and an Engineering degree in Electrical Engineering from Boumerdes. His career includes teaching positions at Blida and Djelfa Universities, collaboration with the Algerian Air Agency, and supervision of advanced student projects in UAVs, satellite control, and robotics. His research focuses on MIMO control, matrix polynomial theory, robotic modeling, nonlinear adaptive control, and intelligent aerospace system design, merging classical automation with artificial intelligence and fractional-order control. He has authored several books and numerous international publications, presented his work at major conferences, and earned recognition for his contributions to intelligent control and aerospace systems. His influence extends across the Algerian and international research communities, where he continues to inspire innovation and academic excellence in modern control and aeronautical engineering.

Profile : Google Scholar 

Featured Publications 

  • Bekhiti, B. (2015). On the theory of λ-matrices based MIMO control system design. Control and Cybernetics.

  • Bekhiti, B. (2017). Intelligent block spectral factors relocation in a quadrotor UAV. International Journal of Scientific Computing (IJSCC).

  • Bekhiti, B. (2018). On λ-matrices and their applications in MIMO control systems design. International Journal of Mathematical and Computational Intelligence (IJMIC).

  • Bekhiti, B. (2020). On the block decomposition and spectral factors of λ-matrices. Control and Cybernetics.

  • Bekhiti, B. (2020). Internal stability improvement of a natural gas centrifugal compressor. Journal of Natural Gas Science and Engineering.

Dan Uchimura | Engineering | Best Researcher Award

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Mr. Dan Uchimura | Engineering | Best Researcher Award

Mr. Dan Uchimura|Kajima Corporation | Japan

Dan Uchimura is an emerging professional in nuclear power plant structural design, currently serving as a designer in the Kajima Corporation Nuclear Power Department. With a Master’s Degree in Architecture from Waseda University, he has swiftly transitioned from academia to industry, applying his expertise in structural systems, safety analysis, and computational modeling. During his graduate studies in Tokyo, he focused on enhancing the resilience and sustainability of energy facilities, developing technical skills in MATLAB, Python, and Excel to simulate structural integrity under extreme conditions. Since joining Kajima, Dan has contributed to the planning and design of nuclear power facilities while spearheading research on integrating non-destructive inspection techniques—especially infrared thermography—into plant systems to detect structural anomalies without operational interruptions. Known for his analytical thinking, precision, and interdisciplinary approach, he collaborates with engineers, material scientists, and safety analysts to deliver reliable, innovative design solutions aligned with stringent safety regulations. His research interests center on advancing inspection technologies, modeling structural behavior under thermal and seismic loads, and exploring AI-driven predictive maintenance systems to enhance safety and efficiency in nuclear infrastructure. Though early in his career, Dan has already earned recognition for his innovative contributions, including commendations for his thesis on resilient energy infrastructure and praise from senior engineers for merging theoretical concepts with practical design solutions.

Profile : ORCID

Featured Publication 

Uchimura, D. (2024). Application of infrared thermography for non-destructive structural inspection in nuclear power facilities. Journal of Structural Engineering and Technology.

Uchimura, D. (2023). Resilient architectural design framework for nuclear power plants. International Journal of Sustainable Energy Infrastructure.

Uchimura, D. (2023). Computational modeling of seismic loads in nuclear plant structures. Journal of Advanced Structural Engineering.

 

Shangshang Wu | Engineering | Best Researcher Award

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Dr. Shangshang Wu | Engineering | Best Researcher Award

Tianjin university | China

Wu Shangshang is a mechanical engineer pursuing her Ph.D. at the School of Mechanical Engineering, Tianjin University in China, where she also completed her B.S. and M.S. in Mechanical Engineering. Her research focuses on underwater gliders, emphasizing hydrodynamic identification, motion behavior analysis, and front-end data processing for acoustic communication. Since her master’s studies, she has worked as a graduate researcher, contributing to both experimental sea trials and theoretical modeling, and has published journal articles and conference papers in marine robotics, acoustics, and signal processing. Wu’s doctoral work advances model-based and data-driven methods to improve hydrodynamic prediction and control under uncertain underwater conditions, supporting the development of reliable seabed vehicles and underwater communication systems. She collaborates closely with colleagues at Tianjin University, including researchers such as Guangwei Lv and Shaoqiong Yang, and her early contributions are gaining citations. Her interests also include neural network–based hybrid modeling, online estimation, and mitigating the effects of environmental factors like sea currents and noise on underwater navigation and sensor performance. While no specific awards are publicly documented, Wu shows strong potential in combining experimental insights with computational techniques to enhance the design, control, and stability of underwater gliders.

Profile : Scopus| ORCID  

Featured Publications

AuthorLastName, A. A., & AuthorLastName, B. B. Model and data-driven hydrodynamic identification and prediction for underwater gliders. Physics of Fluids.

AuthorLastName, A. A., & AuthorLastName, B. B. An enhanced variational mode decomposition method for processing hydrodynamic data of underwater gliders. Measurement.

AuthorLastName, A. A., & AuthorLastName, B. B. Multi-body modelling and analysis of the motion platform for underwater acoustic dynamic communication. Applied Mathematical Modelling.

Jingyi Gao | Engineering | Best Researcher Award

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Ms. Jingyi Gao | University of Virginia | United States

Ms. Jingyi Gao | University of Virginia | United States

Jingyi Gao is a Ph.D. candidate in Systems and Information Engineering at the University of Virginia with a 3.75 GPA, focusing on time series prediction, Bayesian probabilistic modeling, and federated learning. She holds an M.S. in Applied Mathematics and Statistics from the Johns Hopkins University (GPA 3.9) and dual bachelor’s degrees in Mathematics–Computer Science and Economics from the University of California, San Diego. Jingyi has extensive teaching experience, serving as a teaching assistant at UVA where she has instructed over 1,000 students across multiple courses in statistical modeling, data mining, AI, and big data systems, and previously supported courses at Johns Hopkins and UC San Diego. She has mentored underrepresented students through the Data Justice Academy and completed research internships at the University of Pittsburgh and Tencent, developing machine learning models for stress detection, healthcare data analysis, and cloud resource forecasting. Jingyi has authored several publications, including work accepted by Pattern Recognition and under review at AAAI and IISE Transactions. Her recent projects involve designing deep latent variable models for ergonomic risk assessment, developing real-time adaptive prediction frameworks for occupational health monitoring, creating federated learning approaches for multi-output Gaussian processes, and modeling behavioral regularity and predictability from multidimensional sensing signals. Combining expertise in machine learning, statistical modeling, and data-driven decision systems, Jingyi aims to advance human-centered intelligent systems through interpretable and privacy-preserving predictive modeling.

Profile: Scopus | Google Scholar

Featured Publications 

Gao, J., Rahman, A., Lim, S., & Chung, S. TimeSets: A real-time adaptive prediction framework for multivariate time series (Manuscript under review at the Association for the Advancement of Artificial Intelligence).

Gao, J., Lim, S., & Chung, S. Gait-based hand load estimation via deep latent variable models with auxiliary information (Manuscript under review at IISE Transactions).

Gao, J., & Chung, S. Federated automatic latent variable selection in multi-output Gaussian processes (Accepted for publication in Pattern Recognition)*.

Gao, J., Yan, R., & Doryab, A. Modeling regularity and predictability in human behavior from multidimensional sensing signals and personal characteristics. Proceedings of the International Conference on Machine Learning and Applications (ICMLA). Institute of Electrical and Electronics Engineers.

Chen, T., Chen, Y., Gao, J., Gao, P., Moon, J. H., Ren, J., … & Woolf, T. B. Machine learning to summarize and provide context for sleep and eating schedules. bioRxiv.

Mohsen Hatami | Electrical and Computer | Best Researcher Award

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Mohsen Hatami | Electrical and Computer | Best Researcher Award

PhD candidate at Binghamton University, United states

Mohsen Hatami is a highly motivated and accomplished Ph.D. candidate in Electrical and Computer Engineering at Binghamton University, SUNY. With a strong foundation in IoT systems, smart technologies, and AI/ML, his work focuses on advancing sustainable computing and cybersecurity within emerging technologies such as smart grids and metaverse applications. Throughout his academic and professional journey, Mohsen has led innovative projects, particularly in IoT solar cell systems, smart grid management, and cyber-physical defense systems, contributing significantly to the field through his published works.

Profile:

Google scholar

Education:

Mohsen Hatami’s educational background reflects a robust commitment to the advancement of electrical and computer engineering. He is currently pursuing a Ph.D. in Electrical and Computer Engineering at Binghamton University, where he has achieved a remarkable GPA of 3.94/4.0. His research explores the intersection of IoT, AI, machine learning, and smart grid technologies, with an expected completion date in May 2026. Mohsen holds a Master’s degree in Electrical and Electronic Engineering from Kashan University, Iran, where he was recognized as a top student and researcher. His academic journey began with a Bachelor of Science in Applied Science Electronics from Bahar Higher Education Institute of Mashhad and an Associate degree from Shahrekord All Boys Vocational College, both in Iran.

Experience:

Mohsen’s professional experience spans multiple roles where he applied his technical expertise in both hardware and software engineering. At Genoptic (Canada) and Tavanmand (Iran), he led the design and implementation of IoT systems for solar cell monitoring, enhancing energy efficiency through real-time data collection. He also worked on industrial IoT solutions, including an IoT-based failure management system for industrial use, leveraging 4G/5G networks for robust connectivity. Further, Mohsen contributed to the development of smart farm IoT systems at Paya Chip Co., Iran, optimizing water usage and soil monitoring for enhanced agricultural productivity. In addition, he designed fiber optic networks and power systems for the smart grid at Diaco Co. and Pars Kavian Niroo, respectively, demonstrating his versatility across various technical domains.

Research Interests:

Mohsen’s research interests cover a broad spectrum of cutting-edge fields within electrical engineering, including AI and machine learning, embedded systems, network security, blockchain technology, and the metaverse. His work primarily focuses on the integration of IoT with emerging technologies such as 5G/6G communication, edge computing, and digital twins. He is particularly interested in exploring the role of AI in enhancing the security of cyber-physical systems, especially in smart grid environments, and the potential applications of the metaverse in smart grid management.

Awards:

Throughout his academic career, Mohsen Hatami has earned several honors recognizing his research contributions and academic excellence. As a top student and researcher at Kashan University, he was awarded for his outstanding performance in his Master’s program. Additionally, Mohsen has been acknowledged for his leadership in research projects and his dedication to advancing knowledge in fields such as IoT systems and smart technologies.

Publications:

Mohsen Hatami’s research has been widely recognized in top-tier journals and conferences. Some of his key publications include:

  1. Hatami, M., Nasab, M. A., Chen, Y., Mohammadi, J., Ardiles-Cruz, E., & Blasch, E. (2024). ELOCESS: An ESS Management Framework for Improved Smart Grid Stability and Flexibility. IEEE Transactions on Consumer Electronics.

  2. Hatami, M., Qu, Q., Chen, Y., Kholidy, H., Blasch, E., & Ardiles-Cruz, E. (2024). A Survey of the Real-Time Metaverse: Challenges and Opportunities. Future Internet, 16(10), 379.

  3. Hatami, M., Nasab, M. A., Zand, M., Padmanaban, S. (2024). Demand Side Management Programs in Smart Grid Through Cloud Computing. Renewable Energy Focus, 51, 100639.

  4. Hatami, M., Khan, M., Zhao, W., Chen, Y. (2024). A Novel Trusted Hardware-Based Scalable Security Framework for IoT Edge Devices. Discover Internet of Things, 4(1), 4.

  5. Hatami, M., Qu, Q., Chen, Y., Mohammadi, J., Blasch, E., Ardiles-Cruz, E. (2024). ANCHOR-Grid: Authenticating Smart Grid Digital Twins Using Real World Anchors.

  6. Hatami, M., Qu, Q., Xu, R., Nagothu, D., Chen, Y., Li, X., Blasch, E., Ardiles-Cruz, E. (2024). The Microverse: A Task-Oriented Edge-Scale Metaverse. Future Internet, 16(2), 60.

  7. Hatami, M., Nikoufard, M. (2018). Analysis of Ultra-Compact TE to TM Polarization Rotator in InGaAsP and SOI Technologies. Optik-International Journal for Light and Electron Optics, 153, 9-15.

Conclusion:

Mohsen Hatami is a promising researcher and engineer in the field of Electrical and Computer Engineering, with a focus on IoT systems, AI/ML, and cybersecurity. His academic achievements and professional experience reflect a strong commitment to advancing technology in the fields of smart grids, metaverse applications, and embedded systems. With numerous published works in leading journals and his continuous contributions to innovative projects, Mohsen stands out as a dedicated researcher and an emerging expert in his field. His ongoing work in the smart grid and cybersecurity domains holds significant potential for addressing future challenges in these rapidly evolving areas.