Yu Luo | Antenna Design | Research Excellence Award

Published on by Electrical Engineering

Prof. Dr. Yu Luo | Antenna Design | Research Excellence Award

Professor | Tianjin University | China

Yu Luo is a Full Professor at the School of Microelectronics, Tianjin University, and a leading expert in antenna theory and technology within the field of electrical and electronic engineering. He earned his Bachelor of Engineering and Doctorate degrees in electronic engineering, developing strong specialization in antennas, arrays, and high-frequency systems. His professional career encompasses academic appointments and research fellowships at internationally recognized universities, where he has contributed to advanced millimeter-wave and terahertz system development, led nationally funded research projects, and served as principal investigator for competitive programs and industry collaborations. His scholarly contributions have achieved wide international recognition and strong citation impact. Professor Luo serves as an Associate Editor for an international physics journal, chairs professional IEEE chapters, and leads technical program committees for major global conferences, and his academic profile reflects 2,226 citations, 201 published documents, and an h-index of 26.

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Featured Publications

A Flat-Top Beam Nonuniform Waveguide Slot Antenna Based on Beamforming
IEEE Antennas and Wireless Propagation Letters · Citations: 1
A Terahertz Wide-Angle Beam-Steering 3D-Printed Dual-Polarized GRIN Lens With Planar Focal Surface
IEEE Transactions on Terahertz Science and Technology · Citations: 3
A Stacked-Plate-Ball Antenna Design for Optimized Gain and Extended Bandwidth
IEEE Antennas and Wireless Propagation Letters · Citations: 1
Flat-Top Beamforming Synthesis Method Achieved With High-Order Mode Dipoles
IEEE Antennas and Wireless Propagation Letters · Citations: 2

Zhengying Cai | Electric Vehicles | Best Researcher Award

Published on by Electrical Engineering

Prof. Zhengying Cai | Electric Vehicles | Best Researcher Award

Prof | China Three Gorges University  | China

Zhengying Cai is a Professor in the College of Computer and Information Technology at China Three Gorges University, specializing in artificial intelligence and quantum computing, with a strong academic and research profile that underpins his nomination for this award. He earned his bachelor’s, master’s, and doctoral degrees from Huazhong University of Science and Technology with advanced specialization in computer and information technologies, establishing a solid foundation for interdisciplinary research. His professional experience encompasses academic leadership, supervision of graduate researchers, and active involvement in high-impact research projects, alongside contributions to curriculum development and institutional research advancement. His research focuses on artificial intelligence methodologies, quantum computing frameworks, and their applications, resulting in a substantial body of scholarly output, including 38 research documents that have received 224 citations and reflect an h-index of 9, demonstrating both productivity and sustained academic impact.

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Featured Publications

An Artificial Plant Community Algorithm for Collision-Free Multi-Robot Aggregation
Z. Cai, Q. Yu, Z. Lu, Z. Liu, G. Gong – Applied Sciences (Switzerland)

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications
S. Eggen, T.J. Espe, K. Grude, M. Risstad, R. Sandberg – Journal of Economic Surveys

Forecasting Implied Volatilities of Currency Options with Machine Learning Techniques and Econometric Models
A. Olsen, G. Djupskås, P.E. de Lange, M. Risstad – International Journal of Data Science and Analytics

Mr. Nicholas Otumi | Biomedical Instrumentation | Research Excellence Award

Published on by Electrical Engineering

Mr. Nicholas Otumi | Biomedical Instrumentation | Research Excellence Award

Research Assistant | University of Rochester | United States

Nicholas Otumi is a biomedical imaging researcher and diagnostic imaging specialist affiliated with the University of Rochester, with expertise spanning diagnostic imaging, biomedical engineering, and artificial intelligence–driven medical image analysis. He holds a Master of Science in Diagnostic Imaging with specialization in bioengineering and biomedical engineering, and a Bachelor of Science in Diagnostic Imaging Technology, supported by advanced training in deep learning, signal processing, inverse problems, and clinical imaging. His professional experience includes research assistant roles across academic and clinical settings, where he has contributed to imaging model development, clinical data management, AI-based brain and chest image segmentation, and interdisciplinary healthcare research, alongside leadership and community engagement roles within university environments. His research focuses on medical image quality, user-centered imaging system evaluation, deep learning for disease classification, and the application of advanced MRI and CT techniques in both high- and low-resource settings, with multiple peer-reviewed journal publications and ongoing studies under review. He has received competitive research funding and fellowship recognition, institutional awards for leadership and versatility, and has participated in international scientific schools and specialized workshops in radiochemistry, medical physics, AI diagnostics, and imaging quality assurance. His academic service includes research collaboration, technical support, and scholarly dissemination, reflecting a strong commitment to advancing diagnostic imaging research and education.

Profiles:  Google Scholar | ORCID

Featured Publications

AD Piersson, G Nunoo, E Tettey, N Otumi, User-centered assessment of MRI equipment flexibility, workspace adequacy, user interface usability, and technical proficiency. Appl. Clin. Inform., 2025, 16(5), 1595–1605.

AD Piersson, G Nunoo, K Dzefi-Tettey, N Otumi, Utility of advanced brain MRI techniques for clinical and research purposes in a low-resource setting: A multicentre survey. Next Res., 2025, 100638.

A Piersson, E Frost, K Dzefi-Tettey, N Otumi, H Mumuni, S Kafwimbi, et al., Public attitudes and preferences towards artificial intelligence in diagnostic imaging for clinical diagnosis in a low-resource setting (n = 1,032). Eur. Soc. Radiol. (ECR), 2025.

E Fiagbedzi, J Arkorful, IF Brobbey, E Appiah, S Nyarko, N Otumi, et al., A case of ruptured infrapatellar bursa sac with Baker’s cyst. Radiol. Case Rep., 2025, 20(1), 700–704.

E Fiagbedzi, J Arkorful, E Appiah, N Otumi, I Ofori, PN Gorleku, A rare case of intussusception in a 6-month-old baby. Radiol. Case Rep., 2025, 19(10), 4451–4456.

Zhongjun Ma | Nanoelectronics | Research Excellence Award

Published on by Electrical Engineering

Dr. Zhongjun Ma | Nanoelectronics | Research Excellence Award

Engineer | Henan Normal University | China

Zhongjun Ma is a faculty researcher at the School of Environment, Henan Normal University, Xinxiang, China, specializing in microwave absorption and related electromagnetic functional materials. He earned his doctoral degree with a specialization in microwave absorption science, building a strong foundation in material design, absorption mechanisms, and device-oriented applications. Professionally, he has contributed through academic teaching, research project participation, and collaborative research activities, demonstrating leadership in experimental design and scholarly dissemination. His research focuses on microwave absorbing materials, absorption mechanisms, and the development of high-performance microwave absorbing devices, with contributions that advance both theoretical understanding and practical implementation. He has authored 22 scholarly documents that collectively reflect sustained research productivity and impact, achieving 521 citations and an h-index of 12, underscoring the relevance and influence of his work within the field.

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Featured Publications

Toroidal Curved Overlapping Patch Resonant Microwave Sensor for Bolt Loosening Detection
Han, X.-Y.; Zhang, D.; Liu, J.; Liu, X.; Sun, C.; Fu, C.-H.; Ma, Z.-J. – SSRN
ZIF-67-Derived Hierarchical Porous Hollow Shell Engineering for Efficient Microwave Absorption in Ku Band
Han, X.; Sun, C.; Zhang, S.; Liu, K.; Zhai, P.; Liu, X.; Zhang, D.; Liu, J.; Ma, Z. – SSRN
Conversion of Lignin into Porous Carbons for High-Performance Supercapacitors via Spray Drying and KOH Activation
Feng, S.; Ouyang, Q.; Huang, J.; Zhang, X.; Ma, Z.; Liang, K.; Huang, Q. – Journal of Renewable Materials

Rémi Cogranne | Signal & Image Processing | Research Excellence Award

Published on by Electrical Engineering

Dr. Rémi Cogranne | Signal & Image Processing | Research Excellence Award

Troyes University of Technology | France

Rémi Cogranne is an Associate Professor at Troyes University of Technology (UTT), France, and a leading researcher in signal processing, applied mathematics, computer science, and information forensics. His research focuses on hypothesis testing theory, statistical modeling of digital images, image forensics, steganography and steganalysis, and computer network traffic modeling for attack detection, resulting in a substantial body of high-impact journal articles, conference papers, patents, and book chapters. His scholarly influence is demonstrated by 4,624 citations, an h-index of 32, and an i10-index of 68. He has made significant contributions to the research community through editorial service as Senior Associate Editor and Associate Editor for leading IEEE and international journals, membership in prestigious IEEE technical committees, and leadership roles in major international conferences. His work has been widely recognized through best paper awards, editorial honors, and sustained contributions to advancing theory and practice in signal processing and information forensics.

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Featured Publications

Content-adaptive steganography by minimizing statistical detectability
V. Sedighi, R. Cogranne, J. Fridrich – IEEE Transactions on Information Forensics and Security · Citations: 663

Selection-Channel-Aware Rich Model for Steganalysis of Digital Images
T. Denemark, V. Sedighi, V. Holub, R. Cogranne, J. Fridrich – IEEE WIFS · Citations: 469

Moving steganography and steganalysis from the laboratory into the real world
A. D. Ker et al., including R. Cogranne – ACM Workshop on Information Hiding and Multimedia Security · Citations: 344

Rich Model for Steganalysis of Color Images
M. Goljan, J. Fridrich, R. Cogranne – IEEE WIFS · Citations: 203

The ALASKA Steganalysis Challenge: A First Step Towards Steganalysis “into the wild”
R. Cogranne, É. Giboulot, P. Bas – ACM Workshop on Information Hiding and Multimedia Security · Citations: 186

Sina Saadati | Signal & Image Processing | Research Excellence Award

Published on by Electrical Engineering

Mr. Sina Saadati | Signal & Image Processing | Research Excellence Award

Computer Scientist | Amirkabir University of Technology | Iran

Sina Saadati is an emerging researcher and academic affiliated with a higher education and research institution, with expertise spanning interdisciplinary scientific and engineering research. He holds advanced academic degrees with specialization aligned to his research domain, supported by rigorous scholarly training that underpins his analytical and methodological contributions. His professional experience includes active involvement in research projects, collaborative investigations, and academic responsibilities that demonstrate leadership, independence, and commitment to knowledge advancement. His research focuses on targeted thematic areas within his field, with peer-reviewed scholarly publications contributing to the academic literature and supporting evidence-based innovation. His work has achieved measurable academic impact, reflected in 21 citations, an h-index of 3, and an i10-index of 0, indicating growing recognition within the research community. In addition to his research output, he has engaged with the scholarly ecosystem through professional memberships, academic service, and adherence to recognized research standards, positioning him as a dedicated and promising contributor suitable for award recognition.

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Featured Publications

Revolutionizing Endometriosis Treatment: Automated Surgical Operation through Artificial Intelligence and Robotic Vision

S. Saadati, M. Amirmazlaghani – Journal of Robotic Surgery, Vol. 18(1), p. 383, 2024

A Natural Way of Solving a Convex Hull Problem

S. Saadati, M. Razzazi – Proceedings of the National Academy of Sciences, India Section A, 2025

Cloud and IoT Based Smart Agent-Driven Simulation of Human Gait for Detecting Muscle Disorders

S. Saadati, A. Sepahvand, M. Razzazi – Heliyon, Vol. 11(2), 2025

Nahid: AI-Based Algorithm for Operating Fully-Automatic Surgery

S. Saadati – arXiv Preprint, arXiv:2401.08584

Aseel Basheer | Machine Learning | Excellence in Research Award

Published on by Electrical Engineering

Dr. Aseel Basheer | Machine Learning | Excellence in Research Award

Postdoc | University of Oklahoma | United States

Aseel Basheer is a Graduate Research Assistant and Ph.D. candidate in Computer Science at the University of Oklahoma, with expertise in machine learning, data science, and large-scale data analytics. The candidate holds a master’s degree in Computer Science with a specialization in data analytics and is pursuing advanced doctoral research focused on predictive modeling, visual analytics, and AI-driven decision support. Professionally, Aseel has contributed to interdisciplinary research projects in public health intelligence and pandemic surveillance, developing AI/ML models, data-driven forecasting systems, and visualization platforms, while also demonstrating academic leadership through teaching, mentoring, and curriculum support in higher education. The candidate’s professional profile is further strengthened by recognized certifications in data analytics, machine learning, healthcare data science, and research rigor, alongside active engagement in scholarly communities. The scholarly impact is reflected through 22 citations, an h-index of 2, and an i10-index of 1.

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Jingjing Liang | Renewable Energy Systems | Research Excellence Award

Published on by Electrical Engineering

Dr. Jingjing Liang | Renewable Energy Systems | Research Excellence Award

Engineer | Tsinghua University | China

Jingjing Liang is an Engineer associated with the Department of Power and Energy Engineering at Tsinghua University, with recognized expertise in electrochemical energy systems and low-carbon energy technologies. She earned her undergraduate and doctoral degrees at Tsinghua University, specializing in solid oxide electrolysis and energy storage, and enhanced her training through international doctoral research at a leading European technical institution. Her professional experience spans academic research and engineering practice, including participation in nationally funded energy research projects, collaboration with international research groups, and an engineering role within a major energy engineering corporation, where she contributes to the application of advanced energy technologies. Her research focuses on solid oxide electrolysis cells, co-electrolysis mechanisms, long-duration energy storage, and zero-carbon emission technologies, integrating experimental investigation with simulation-based analysis, leading to multiple peer-reviewed journal publications and an authorized invention patent. She has also served as a lead contributor to national industrial standards for solid oxide electrolysis cell and stack performance testing and is a professional member of the High-Temperature Fuel Cell Standards Committee of China. Her scholarly impact includes 106 citations, an h-index of 4, and an i10-index of 2.

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Featured Publications

Hengrui Ma | Renewable Energy Systems | Research Excellence Award

Published on by Electrical Engineering

Prof. Hengrui Ma | Renewable Energy Systems | Research Excellence Award

Associate Professor | Wuhan University of Technology | China

Prof. Hengrui Ma is an Associate Professor at Wuhan University of Technology and a leading researcher in renewable energy systems, new power systems, and integrated energy systems. He received his Ph.D. from Wuhan University and completed his bachelor’s and master’s degrees in electrical engineering at North China Electric Power University, with a strong specialization in power and energy systems. His professional experience spans academic teaching, advanced research, and technical leadership, including roles as sub-task leader and project leader in major national and industry-funded smart grid and energy system projects. Prof. Ma’s research focuses on renewable energy integration, smart grids, and energy system regulation, resulting in more than 80 peer-reviewed publications, the majority indexed in SCI/EI, high citation impact across major databases, multiple highly cited and influential papers, and substantial contributions to theory and practice in modern power systems.  His scholarly impact is evidenced by 1,561 citations, an h-index of 20, and an i10-index of 34, reflecting the quality, consistency, and international visibility of his research contributions.

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Bo Wang | Smart Grids and Microgrids | Research Excellence Award

Published on by Electrical Engineering

Prof. Bo Wang | Smart Grids and Microgrids | Research Excellence Award

Professor | Wuhan University | China

Prof. Bo Wang is a Professor and doctoral supervisor at the School of Electrical Engineering, Wuhan University, and a recognized authority in smart grids and microgrids, with expertise in the digitalization of power distribution and utilization. He earned advanced degrees in electrical engineering with specialization in power systems, smart grid technologies, and coordinated operation of distribution networks and microgrids. His research focuses on intelligent energy management, load scheduling, renewable energy integration, risk assessment, and data-driven operation of power systems, leading to more than 120 peer-reviewed journal publications, a scholarly monograph, and multiple granted patents, with strong academic impact reflected by over 3,054 citations, an h-index of 29, and an i10-index of 64. His contributions have been recognized through major science and technology awards, a Gold Medal at the International Exhibition of Inventions of Geneva, and selection among China’s most influential academic papers. Prof. Wang is an IET Fellow, IEEE Senior Member, serves on editorial boards of leading power engineering journals, and actively contributes to international professional societies and technical committees.

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Featured Publications

Improving interdependent networks robustness by adding connectivity links

– Physica A: Statistical Mechanics and its Applications, 2016 (121 citations)