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).

Assoc. Prof. Dr. Guangyuan Xu | Smart Materials | Best Researcher Award

Beijing University of Posts and Telecommunications | China

Assoc. Prof. Dr. Guangyuan Xu is a leading researcher in brain computer interfaces, embodied intelligence, neural sensing, flexible electronics, and cognitively driven robotic systems, serving at the School of Artificial Intelligence at Beijing University of Posts and Telecommunications. His work is recognized internationally for pioneering advances that bridge neuroscience, materials science, artificial intelligence, and robotics, with a strong focus on creating intelligent systems capable of seamless human machine interaction. His scientific influence is reflected in 618 citations drawn from 572 citing documents, supported by 10 research documents and an h-index of 8, demonstrating the growing global relevance and impact of his contributions. Dr. Xu directs the Cognitive and Embodied Intelligence Laboratory, where he leads interdisciplinary teams in developing next-generation task-relevant mental imagery BCIs, high-performance flexible interfaces, multimodal neuro-robotic co-adaptation systems, intelligent sensing materials, and robust human–machine decision-making frameworks. He has published extensively in high-impact journals and international conferences covering biosensing, neurotechnology, flexible electronics, affective computing, and embodied robotics. His leadership extends to major professional societies, including active roles within the Chinese Association for Artificial Intelligence, the China Computer Federation, the China Graphics Society, and the national Brain Computer Interface Industry Alliance, reflecting his prominent standing in the scientific community. Dr. Xu also contributes to national strategic innovation programs and collaborative platforms in artificial intelligence, neurotechnology, and bio-integrated sensing, helping shape the scientific direction of emerging intelligent technologies. His collaborations with global institutions have strengthened international research networks in neuro-robotic integration and intelligent sensing systems, driving forward cutting-edge advancements in BCI-enabled robotics. Through his vision, interdisciplinary expertise, and dedication to advancing neuro-intelligent systems, Dr. Xu continues to push the boundaries of cognitive interaction technologies, flexible neural interfaces, and embodied intelligence, establishing himself as a key contributor to the future of human machine integration and intelligent robotic development.

Profiles: Scopus | Google Scholar

Featured Publications

Fu, Z., Lin, Y., Xu, G., & Zhang, M. (2025). Comparative performance of IMU and sEMG in locomotion mode prediction across transitional and steady-state cyclic/non-cyclic gaits. IEEE Journal of Biomedical and Health Informatics

Li, W., Zhang, J., Guo, J., Wang, X., Xu, G., Peng, Y., & Tu, L. (2025). Automated detection and classification of pediatric middle ear diseases from CT using entropy projection and feature interaction. In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE.

Xu, G. (2018). Laser scribed graphene: fabrication and electrochemical biosensors for neurotransmitters. ResearchSpace@Auckland.

Liu, Y., Xu, G., Li, C., Ma, Y., Ji, N., & Feng, X. (2025). Stretchable multilevel mesh brain electrodes for neuroplasticity in glioma patients undergoing surgery. Advanced Healthcare Materials, e03358.

Xu, G., Chen, Y., Chen, F., Meng, Y., Ma, Y., & Feng, X. (2021). Fabrication of laser scribed graphene stretchable supercapacitor by laser-assisted transfer printing strategy. In 2021 IEEE 16th International Conference on Nano/Micro Engineered and Molecular Systems (NEMS). IEEE.

Dr. Ajay Vikram Singh | Smart Materials | Best Researcher Award

Dr. Ajay Vikram Singh | German Federal Institute for Risk Assessment (BfR) | Germany

Dr. Ajay Vikram Singh is an accomplished scientist and senior researcher in the Department of Chemical and Product Safety at the German Federal Institute for Risk Assessment (BfR) in Berlin, where he contributes to shaping scientific advice for the German federal government on food safety, chemical risks, nanotoxicology, contaminants, and consumer product protection. With an h-index of 57, over 13,550 citations, and more than 200 peer-reviewed publications indexed on Scopus, Dr. Singh is widely recognized as a thought leader in toxicology, nanomedicine, and micro/nanorobotics. His pioneering research integrates experimental toxicology, computational PBK modeling, machine learning, and bioinspired material science, advancing next-generation risk assessment tools and novel therapeutic strategies. His earlier work as a Senior Research Scientist at the Max Planck Institute for Intelligent Systems focused on micro/nanorobotics for drug delivery and biohybrid microswimmers, while his postdoctoral work at RPI, USA, explored micro-nanopatterning for tissue engineering and in vitro models of embryogenesis. Dr. Singh has received prestigious international fellowships including MIUR Young Investigator Fellowship, Umberto Veronesi Foundation PhD Fellowship, and advanced research grants from CiQUS and UCT ChemJets. His editorial roles include Section Editor for Springer-Nature Discover Toxicology, and board memberships for journals such as Cutaneous and Ocular Toxicology, Coatings, and AI Chemistry. His scientific impact is further amplified by leadership in OECD working groups on physiologically based kinetic models and nanomaterial risk assessment guidelines. Dr. Singh’s research vision emphasizes harmonizing innovation in nanotechnology with regulatory frameworks, fostering safe and sustainable materials design. He has supervised students internationally and taught advanced courses on bioinspired materials, empowering the next generation of researchers. With expertise bridging wet lab experimentation, in silico modeling, and translational toxicology, Dr. Singh stands as a global ambassador for predictive toxicology and evidence-based safety science, making him a distinguished nominee for a Best Researcher Award.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Kulkarni, P. G., Paudel, N., Magar, S., Santilli, M. F., Kashyap, S., Baranwal, A. K., Zamboni, P., Vasavada, P., Katiyar, A., & Singh, A. V. (2024). Overcoming challenges and innovations in orthopedic prosthesis design: An interdisciplinary perspective. Biomedical Materials & Devices.

Singh, A. V., Bansod, G., Mahajan, M., Dietrich, P., Singh, S. P., Rav, K., Thissen, A., Bharde, A. M., Rothenstein, D., Kulkarni, S., et al. (2023). Digital transformation in toxicology: Improving communication and efficiency in risk assessment. ACS Omega, 8(24), 20766–20777.

Tripathi, D., Ray, P., Singh, A. V., Kishore, V., & Singh, S. L. (2023). Durability of slippery liquid-infused surfaces: Challenges and advances. Coatings, 13(6), 1095.

Singh, S. L., Chauhan, K., Bharadwaj, A. S., Kishore, V., Laux, P., Luch, A., & Singh, A. V. (2023). Polymer translocation and nanopore sequencing: A review of advances and challenges. International Journal of Molecular Sciences, 24(7), 6153.

Singh, A. V. (2022). Emerging cold plasma treatment and machine learning prospects for seed priming: A step towards sustainable food production. RSC Advances, 12, 8818–8828.

Singh, A. V., Chandrasekar, V., Laux, P., Luch, A., Dakua, S. P., Zamboni, P., Shelar, A., Yang, Y., Pandit, V., Tisato, V., et al. (2022). Micropatterned neurovascular interface to mimic the blood–brain barrier’s neurophysiology and micromechanical function: A BBB-on-CHIP model. Cells, 11(18), 2801.

Singh, A. V. (2022). Self-assembly of DNA-grafted colloids: A review of challenges. Micromachines, 13(7), 1102.