Zhenghua Qian | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Prof. Dr. Zhenghua Qian | Machine Learning | Research Excellence Award

Professor | Nanjing University of Aeronautics and Astronautics | China

Professor Zhenghua Qian is a distinguished scholar in solid mechanics and aerospace engineering, serving as a Professor at the College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, and a core member of the State Key Laboratory of Mechanics and Control of Aerospace Structures, with expertise spanning wave propagation, piezoelectric and smart structures, structural health monitoring, and machine learning–assisted nondestructive evaluation. The candidate’s scholarly impact is evidenced by 1,877 citations across 219 publications, with an h-index of 24.

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Qingguo Lü | Electrical Engineering  | Research Excellence Award

Published on by Electrical Engineering

Dr. Qingguo Lü | Electrical Engineering  | Research Excellence Award

Associate Researcher | Chongqing University | China

Qingguo Lü is an Associate Researcher at Chongqing University specializing in distributed optimization, privacy-preserving machine learning, and smart grid systems. He earned his doctoral degree in computer science and technology with a strong focus on optimization theory and networked systems and subsequently advanced his expertise through postdoctoral research and international academic collaboration.  His scholarly contributions have delivered both theoretical advances and practical engineering solutions, achieving strong international visibility and impact. His academic influence is reflected by 1,174 citations, an h-index of 18, and an i10-index of 26. In recognition of his expertise and service, he holds multiple editorial and guest editorial appointments in international journals, serves on conference program committees, contributes extensively to peer review, holds innovation patents, and maintains professional standing as an IEEE Senior Member, demonstrating sustained excellence in research, leadership, and academic service suitable for a prestigious research award.

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Featured Publications

Distributed projection subgradient algorithm over time-varying general unbalanced directed graphs
H. Li, Q. Liu, T. Huang – IEEE Transactions on Automatic Control · Citations: 115
Accelerated convergence algorithm for distributed constrained optimization under time-varying general directed graphs
H. Li, Q. Lü, X. Liao, T. Huang – IEEE Transactions on Systems, Man, and Cybernetics: Systems · Citations: 104
Achieving acceleration for distributed economic dispatch in smart grids over directed networks
Q. Liu, X. Liao, H. Li, T. Huang – IEEE Transactions on Network Science and Engineering · Citations: 100
Convergence analysis of a distributed optimization algorithm with a general unbalanced directed communication network
H. Li, Q. Liu, T. Huang – IEEE Transactions on Network Science and Engineering · Citations: 96
Privacy masking stochastic subgradient-push algorithm for distributed online optimization
Q. Lü, X. Liao, T. Xiang, H. Li, T. Huang – IEEE Transactions on Cybernetics · Citations: 80

Mingxu Wang | Wireless Communication | Young Scientist Award

Published on by Electrical Engineering

Dr. Mingxu Wang | Wireless Communication | Young Scientist Award

Research Associate | Fudan University | China

Mingxu Wang is a Research Associate at Fudan University specializing in photonic terahertz and free-space optical communications within advanced radio access networks. He holds advanced academic training in electronic and optical engineering with a strong focus on high-capacity fiber–wireless integrated systems, developed through rigorous graduate-level education and collaborative research at leading international institutions. His scholarly output has achieved 687 citations with an h-index of 15 and an i10-index of 22, reflecting sustained impact in the field. He holds multiple patents related to advanced modulation and fiber–wireless integration techniques and actively contributes to the research community through professional memberships with IEEE, Optica, and SPIE. His excellence has been recognized through highly competitive international scholarships and distinctions, underscoring his leadership, innovation, and growing influence in electrical and photonic communications research.

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Featured Publications

Low complexity neural network equalization based on multi-symbol output technique for 200+ Gbps IM/DD short reach optical system
B. Sang, W. Zhou, Y. Tan, M. Kong, C. Wang, M. Wang, L. Zhao, J. Zhang, J. Yu – Journal of Lightwave Technology · Citations: 78
Integrated high-resolution radar and long-distance communication based-on photonic in terahertz band
Y. Wang, W. Li, J. Ding, J. Zhang, M. Zhu, F. Zhao, M. Wang, J. Yu – Journal of Lightwave Technology · Citations: 64
Joint communication and radar sensing functions system based on photonics at the W-band
Y. Wang, J. Liu, J. Ding, M. Wang, F. Zhao, J. Yu – Optics Express · Citations: 56
W-band simultaneous vector signal generation and radar detection based on photonic frequency quadrupling
Y. Wang, J. Ding, M. Wang, Z. Dong, F. Zhao, J. Yu – Optics Letters · Citations: 46
Photonics-assisted joint high-speed communication and high-resolution radar detection system
Y. Wang, Z. Dong, J. Ding, W. Li, M. Wang, F. Zhao, J. Yu – Optics Letters · Citations: 45

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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View Scopus Profile View ORCID Profile

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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Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Published on by Electrical Engineering

Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Associate Professor | Shanghai Electric Power University | China

Dr. Li Xiaohua, a distinguished Professor at Sichuan University and leading expert in materials science and structural engineering, is renowned for advancing high-performance composite materials and sustainable structural systems. She holds advanced degrees in materials engineering with specialization in composite behavior and structural performance, complemented by extensive experience in academic leadership, project supervision, and collaborative research initiatives. Her professional portfolio includes directing major institutional projects, mentoring interdisciplinary teams, and contributing to engineering innovations that strengthen the reliability and resilience of modern structures. Dr. Li’s research focuses on composite structures, fire-resistant materials, mechanical behavior, and performance optimization, supported by 297 citations, 34 scholarly documents, and an h-index of 11, reflecting her growing global impact. She has authored influential publications, contributed to high-level research panels, and advanced knowledge dissemination through editorial responsibilities and membership in professional engineering societies. Recognized for excellence in research, innovation, and service, she also holds relevant professional certifications that underscore her commitment to scientific rigor and continued advancement in the engineering sciences.

Profile: Scopus

Featured Publications

Li Xiaohua*, Probabilistic forecasting of coal consumption for power plants under deep peak shaving conditions using Informer with DDPM-based uncertainty modeling. Int. J. Electr. Power Energy Syst., 2025.

Li Xiaohua*, Electromagnetic vibration characteristics of permanent magnet synchronous motors with segmented grain-oriented electrical steel teeth–yoke.

Li Xiaohua, Research on core loss prediction of low-frequency transformer based on Grey Wolf optimisation algorithm optimised Back Propagation neural network. IET Electr. Power Appl., 2025.

 










































































 




	


	

	






	
	

		


Mr. Xiangqi Dong | Nanoelectronics & Nanomaterials | Best Researcher Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Mr. Xiangqi Dong | Nanoelectronics & Nanomaterials | Best Researcher Award


PhD Candidate | Fudan University | China






























Xiangqi Dong is a researcher in microelectronics at the School of Microelectronics and the National Key Laboratory of Integrated Chips and Systems at Fudan University, specializing in two-dimensional semiconductors, integrated circuit fabrication, and device–circuit co-optimization. He is pursuing a direct doctoral degree and holds an undergraduate background in Microelectronics Science and Engineering from Northwestern Polytechnical University, with focused academic training in microelectronics and solid-state electronics. His professional experience includes optimizing wafer-scale 2D transistor processes, supervising laboratory tape-out workflows, establishing quality-control procedures, integrating advanced fabrication tools, and leading a research team working on analog circuits and DTCO-driven circuit innovation. His research contributions encompass high-performance 2D gate-stack engineering, sensing-memory-computing fusion devices, neuromorphic electronics, RF systems, and next-generation computing architectures, resulting in significant publications in leading journals, invited conference talks, and contributions to landmark achievements such as 2D microprocessors, high-linearity flash ADCs, and wafer-scale integrated RF transmitters. He has co-filed patents on transistor structures and semiconductor process optimization, and actively participates in academic outreach to promote integrated circuit education. His recognitions include multiple merit-based scholarships and academic excellence awards, reflecting strong research capability and leadership. Citations 92 by 88 documents, 17 documents, h-index 5.


Profile: Scopus


Featured Publications


Xiangqi Dong, Radiation resistant atomic layer scale radio frequency system for spaceborne communication. Nature, Under review.


Xiangqi Dong, A RISC-V 32-bit microprocessor based on two-dimensional semiconductors. Nature, Published.


Xiangqi Dong, High-linearity flash ADC achieved through design-technology co-optimization based on two-dimensional semiconductors. Science Bulletin, Online.


Xiangqi Dong, A bio-inspired neuron with intrinsic plasticity based on monolayer molybdenum disulfide. Nature Electronics, Published.