Assoc. Prof. Dr. Ming Shao | Smart Materials | Research Excellence Award

Beijing Forestry University | China

Assoc. Prof. Dr. Ming Shao is a distinguished researcher in smart materials and intelligent environmental systems, with a strong interdisciplinary background that integrates materials science concepts with urban computing, ecological modeling, and sustainable spatial design. He serves as an Associate Professor at the School of Landscape Architecture, Beijing Forestry University, where his research advances data-driven and smart approaches to understanding and optimizing complex urban material–environment systems. Dr. Shao’s work focuses on the interaction between smart materials concepts, green infrastructure, and ecosystem services, particularly in high-density urban environments where spatial efficiency and environmental performance are critical. His research addresses urban green space optimization, biodiversity enhancement, carbon sequestration, and ecosystem service assessment using advanced computational methods, spatial modeling, and intelligent simulation techniques. By applying system dynamics, urban computing, and multi-scale spatial analysis, he contributes to the development of resilient and adaptive urban environments that respond intelligently to ecological and societal demands. Dr. Shao has led and participated in numerous nationally competitive research projects, demonstrating strong leadership in interdisciplinary research that connects smart material behavior, environmental performance, and urban sustainability. His work supports evidence-based planning strategies that enhance ecological functionality, climate resilience, and human well-being in rapidly urbanizing regions. In addition to his research activities, he actively contributes to academic service through peer review for leading international journals and engagement in professional scientific communities. Dr. Shao is also committed to education and mentorship, guiding students in innovative research that bridges smart materials, ecological systems, and digital technologies. Through his interdisciplinary vision, methodological innovation, and sustained contributions to intelligent and sustainable material–environment systems, Assoc. Prof. Dr. Ming Shao has established a strong scientific profile and is a highly deserving recipient of the Research Excellence Award.

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Featured Publications

Dr. Wentao Zhou | Smart Materials | Research Excellence Award

The College of Intelligent Systems Science and Engineering | Harbin Engineering University | China

Dr. Wentao Zhou is an emerging researcher in smart materials and intelligent systems, recognized for his growing academic impact and innovative contributions to advanced material technologies. He has developed a strong research portfolio with an h-index of 4, supported by 9 published documents and 56 citations across 51 citing documents, reflecting the influence and relevance of his scientific work. Dr. Zhou is affiliated with the College of Intelligent Systems Science and Engineering at Harbin Engineering University, where he has built a multidisciplinary background spanning deep learning, computer vision, and small-object detection with applications in material characterization and intelligent sensing. His research excellence is further demonstrated through the publication of 10 peer-reviewed SCI papers, multiple competition achievements, and significant innovation output, including 3 authorized Chinese patents and several ongoing patent activities. He also contributes to technological development as a key technical backbone in collaborative projects, independently leading planning, algorithm design, personnel coordination, and the establishment of monitoring, identification, and testing standards for air-traffic-control systems. Dr. Zhou’s work is strengthened by academic exposure at globally ranked institutions and active professional engagement as a Graduate Student Member of IEEE. He has also earned more than 20 prestigious honors and scholarships, recognizing both academic excellence and technological innovation. Beyond his research achievements, he has held leadership roles such as Workshop Chair for RAITS, reflecting his commitment to academic service and community contribution. His core research in smart materials integrates intelligent algorithms with material-focused applications, positioning him as a promising young scientist whose innovations align strongly with the objectives of the Research Excellence Award. Dr. Zhou’s scholarly record, technological creativity, and dedication to advancing smart materials collectively underscore his merit as a dynamic and impactful researcher.

Profiles: Scopus | Orcid

Featured Publications

Yang, S., Zhou, W., Qu, S., & Khoo, B. C. (2025, December). Fast and high-accuracy state estimator for some unknown dynamic objects with a stereo camera in aerial tracking.

Wang, R., Qiao, R., Zhou, W., & Cai, C. (2025, November). HACRNet: Hierarchical attention compression for high-speed fine-grained ship recognition.

Zhou, W., Cai, C., Srigrarom, S., & Li, C. (2025, June). SAD-YOLO: A small object detector for airport optical sensors based on improved YOLOv8.

Zhang, Y., Zhao, E., Liang, H., & Zhou, W. (2024, December). MATD3 with multiple heterogeneous sub-networks for multi-agent encirclement-combat task.

Zhou, W., Cai, C., Wu, K., & Gao, B. (2024, June). LAS-YOLO: A lightweight detection method based on YOLOv7 for small objects in airport surveillance.

Mr. Fawad Khan | Smart Materials | Best Researcher Award

Shenzhen Institutes of Advanced Technology | Chinese Academy of Sciences | China

Mr. Fawad Khan is a promising researcher at the intersection of smart materials, safe robotics, reinforcement learning, and human–robot collaboration, recognized for his innovative contributions to the development of intelligent, safety-aware autonomous systems. As a PhD Researcher at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, he focuses on advancing constrained reinforcement learning frameworks that enable robots to operate reliably and safely alongside humans in dynamic and safety-critical environments such as collaborative manufacturing, warehouse automation, and assistive robotics. His scholarly output includes 3 research documents, and he has contributed to journal articles, conference papers, and manuscripts addressing precision grasping, adaptive safety constraints, multi-object manipulation, and safety-critical coordination. His research introduces novel approaches that combine tactile–visual perception, adaptive constraint satisfaction, and multi-modal learning to significantly reduce safety violations while maintaining high task performance in robotic systems. He has designed benchmark platforms for safe robotic manipulation and expanded the capabilities of existing tools such as Safety Gym to enable high-fidelity evaluation of robotic arms with multiple degrees of freedom. Prior to his doctoral research, Mr. Khan gained industry experience as a Python developer and data analyst, where he automated logistics operations, designed data-driven decision-support tools, and streamlined complex workflows, demonstrating his ability to integrate practical engineering solutions with theoretical AI advancements. His technical expertise spans reinforcement learning algorithms, constrained optimization, robotics simulation environments, computer vision, multi-modal neural networks, and high-performance computing frameworks. He actively collaborates with interdisciplinary teams working on intelligent manufacturing, safe autonomy, and human-centered robotics. With strong analytical skills, a clear research vision, and a growing academic footprint, Mr. Fawad Khan represents a new generation of AI and robotics researchers dedicated to creating safer, smarter, and more adaptive robotic systems, making him a highly deserving candidate for the Best Researcher Award.

Profile: Scopus

Featured Publications

Khan, F., Feng, W., Wang, Z., Huang, T., Xiao, L., et al. (2025). Safe reinforcement learning for objects manipulation in safety-critical coordinated tasks. In ISARC: Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 42, pp. 334–341). IAARC Publications.

Khan, F., et al. Reinforcement learning for precision grasping and safety-critical coordination in a robotic arm. Journal of Intelligent Service Robotics.

Khan, F., et al. Safe reinforcement learning for vision-based robotic manipulation in human-centered environment. Journal of Intelligent Robotics and Applications.

Khan, F., et al. Safe reinforcement learning for multi-object robotic manipulation with adaptive safety constraint. Expert Systems with Applications.

Mr. Angelos Athanasiadis | Smart Materials | Research Excellence Award

Aristotle University of Thessaloniki | AUTH | Greece

Mr. Angelos Athanasiadis is an emerging researcher in smart materials, embedded intelligence, and high-performance computing architectures, known for his contributions to FPGA-accelerated deep learning and intelligent cyber-physical systems. Currently pursuing his PhD in Electrical and Computer Engineering at Aristotle University of Thessaloniki, he focuses on designing advanced hardware-accelerated frameworks that significantly enhance the speed, efficiency, and energy performance of full-precision Convolutional Neural Networks on modern AMD FPGA platforms. His early scientific influence is reflected in 5 citations, referenced across 4 citing documents, supported by ongoing scholarly outputs and an h-index recorded as 1–2, demonstrating his growing visibility in computational engineering research. Mr. Athanasiadis has contributed to significant EU-funded research initiatives, including the ADVISER and REDESIGN projects, where he developed high-fidelity emulation tools, hardware–software co-design solutions, and distributed embedded intelligence for heterogeneous systems combining CPUs, GPUs, and FPGAs. His work on FUSION an open-source, timing-accurate, multi-node emulation framework integrating QEMU with OMNeT++ using HLA/CERTI synchronization has advanced the ability to accurately prototype next-generation smart systems for robotics, aerial monitoring, real-time analytics, and autonomous decision-making. With a strong background in electronics, embedded systems, and management engineering, he has also completed industry-driven research roles at EXAPSYS, SEEMS PC, and Cadence Design Systems, contributing to R&D for sensor networks, FPGA-based acceleration pipelines, and complex digital-system workflows. In addition to his technical expertise, he maintains interdisciplinary strengths in AI-driven system optimization, hardware–software integration, multiphysics emulation, and intelligent system design. Collaborating with leading academic researchers and contributing to peer-reviewed venues, he continues to expand his research footprint. With strong analytical skills, innovation-oriented thinking, and a commitment to advancing smart materials and high-performance embedded intelligence, Mr. Angelos Athanasiadis stands out as a promising researcher and a deserving candidate for the Research Excellence Award.

Profiles: Google Scholar | Orcid

Featured Publications

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). Energy-efficient FPGA framework for non-quantized convolutional neural networks. arXiv Preprint, arXiv:2510.13362.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). An efficient open-source design and implementation framework for non-quantized CNNs on FPGAs. Integration, Article 102625.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2024). An open-source HLS fully parameterizable matrix multiplication library for AMD FPGAs. WiPiEC Journal—Works in Progress in Embedded Computing Journal, 10(2).

Katselas, L., Jiao, H., Athanasiadis, A., Papameletis, C., Hatzopoulos, A., … (2017). Embedded toggle generator to control the switching activity during test of digital 2D-SoCs and 3D-SICs.

Katselas, L., Athanasiadis, A., Hatzopoulos, A., Jiao, H., Papameletis, C., … (2017). Embedded toggle generator to control the switching activity.

Mr. Prashant Kishor Sharma | Smart Materials | Research Excellence Award

National Cheng Kung University | Taiwan

Mr. Prashant Kishor Sharma is an emerging researcher in smart materials, microfluidics, and AI-integrated engineering, recognized for his rapidly growing contributions to advanced flow control, active matter systems, and micro-robotics for biomedical applications. As a doctoral researcher in Mechanical Engineering at National Cheng Kung University, Taiwan, he focuses on magnetically actuated artificial cilia, swarm microrobots, and intelligent microfluidic platforms that enable high-precision particle manipulation, drug delivery studies, and biomimetic flow control. His work spans computational hemodynamics, physics-informed neural networks, CFD-based flow modeling, and advanced lab-on-a-chip design, with research outcomes published in reputable SCI and Scopus-indexed journals covering microfluidic mixing, cognitive dynamics of zebrafish, AI-integrated magnetic microrobots, and solar-energy-based engineering systems. Mr. Sharma has successfully completed and contributed to several innovative research projects, including the development of artificial-cilia-based microactuation systems, computational hemodynamics of aneurysm flows using AI-accelerated models, a natural-zeolite oxygen concentrator, solar-powered thermal drying solutions, and bio-inspired drag-reduction strategies for energy-efficient flow systems. He actively collaborates across multidisciplinary teams involving mechanical engineering, biomedical engineering, microfabrication, and computational sciences, integrating simulation, experimentation, and AI-driven optimization. His work has earned recognition such as the Veritas Conscientia Scholarship, Excellence Research Work Award at CTAM, and Best Poster distinction for his contributions to thermal fluidics and micro-robotic navigation. In addition to scientific research, he contributes to technological innovation through a patent on a natural zeolite-based oxygen concentrator and participates in professional societies such as the Chinese Society of Theoretical and Applied Mechanics. His dedication to advancing smart materials, microfluidic intelligence, and biomedical engineering underscores his commitment to impactful, interdisciplinary research. With strong technical proficiency, innovative thinking, and a rapidly expanding academic footprint, Mr. Prashant Kishor Sharma stands out as a promising young scientist and an excellent candidate for the Research Excellence Award.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Sharma, P. K., Wei, P.-W., Loganathan, D., Lu, Y.-H., & Chen, C.-Y. (2025, November 18). Microflow switching using artificial cilia for on‐demand particle manipulation. Advanced Intelligent Systems.

Sharma, P. K., Wei, P.-W., Loganathan, D., Lu, Y.-H., & Chen, C.-Y. (2025, July 24). Microflow switching using artificial cilia for on‐demand particle manipulation. Advanced Intelligent Systems.

Sharma, P. K., Loganathan, D., Chen, M.-L., Lu, Y.-H., Wang, P.-H., & Chen, C.-Y. (2025, May 1). Cognitive dynamics of drug-mediated zebrafish under sound stimuli in a microfluidic environment. Biomicrofluidics.

Kandukuri, K. S., Sharma, P. K., & Arun, R. K. (2024). A comparative assessment of distributive mode active solar dryers: Flat plate collector vs evacuated tube collector with thermal energy storage and perforated baffled trays. Solar Energy, 112421.

Sharma, P. K., Arun, R. K., Lata, A., & Shiva, S. (2023, June 15). A natural zeolite based oxygen concentrator for supplying pure oxygen [Patent].

Sharma, P. K., Shukla, R. K., & Oberoi, A. S. (2022, August 25). Understanding of drag reduction & biofouling using shark skin denticle through CFD simulation (Master’s thesis or dissertation).