Jian Xia | Artificial Intelligence in Statistics | Research Excellence Award

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Dr. Jian Xia | Artificial Intelligence in Statistics | Research Excellence Award

Hubei University of Automotive Industry | China

Dr. Jian Xia is a dedicated materials scientist specializing in next-generation electronic and photonic devices, with a strong academic foundation and a growing record of impactful research. He obtained his Ph.D. degree from the School of Materials Science and Engineering at Huazhong University of Science and Technology, where he developed expertise in resistive switching devices, phase-change materials, and advanced optical memory technologies. After completing his doctoral studies, he joined the Hubei University of Automotive Technology as a lecturer, contributing actively to both teaching and research in the field of electronic materials and integrated circuit design. Dr. Xia’s research interests encompass memristors, phase-change memory, and photonic neuromorphic devices, all of which hold promising applications in high-performance computing, data storage, and artificial intelligence hardware. He has undertaken notable research projects, including the Open Fund of the Hubei Key Laboratory of Energy Storage and Power Battery and the Doctoral Scientific Research Foundation of Hubei University of Automotive Technology. With a citation index of 361 and a research portfolio of 20 SCI-indexed publications, Dr. Xia has contributed articles to leading international journals such as Nature Communications, Laser & Photonics Reviews, ACS Photonics, Applied Physics Letters, IEEE Electron Device Letters, and Science China Materials. His innovative contributions are further demonstrated by nine patents that are either published or under review, highlighting his commitment to advancing practical and technologically significant developments in electronic device engineering. Although he has yet to hold editorial appointments or professional memberships, his scholarly influence continues to grow through strong research visibility and future collaboration potential. Dr. Xia maintains an active academic presence on platforms such as ResearchGate and continues to advance pioneering research aimed at developing energy-efficient, high-density, and neuromorphic computing devices to meet the evolving demands of modern information technology.

Citation Metrics (Scopus)

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Featured Publication

 

Yuying Chen | Statistical Modeling and Simulation | Research Excellence Award

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Dr. Yuying Chen | Statistical Modeling and Simulation | Research Excellence Award

Jinling Institute of Technology | China

Dr. Yuying Chen is a dedicated materials scientist in the Department of Materials Engineering at the School of Materials Engineering, Jinling Institute of Technology, Nanjing, China, where she contributes extensively to research, teaching, and the advancement of materials innovation. She earned her Ph.D. in Materials Science from the Harbin Institute of Technology and enriched her international academic profile through a visiting Ph.D. appointment at the Department of Mining and Materials Engineering at McGill University in Montreal, Canada. Her academic background also includes a Master’s degree in Materials Science from the Harbin Institute of Technology and a Bachelor’s degree in Metal Materials Engineering from Shenyang University of Technology. Dr. Chen’s research expertise encompasses first-principles calculations, hydrogen storage materials, interface engineering, alloying effects, metal hydrides, and computational modeling of welding processes. She has authored 8 documents that investigate hydrogen adsorption and desorption mechanisms, Mg/Ni and Mg/Ti interface stability, alkali- and alkaline-earth-metal-doped hydrides, Zn-induced embrittlement behavior in steels, and advanced modeling techniques for underwater wet welding and duplex stainless-steel welding under acoustic and vibrational fields. Her scholarly contributions have accumulated 90 citations and reflect an impactful research profile with an h-index of 5, demonstrating the academic significance and visibility of her work within the materials science community. Over the course of her academic journey, Dr. Chen has received numerous accolades, including Merit Student awards, multiple University Fellowships, Outstanding Student Leader recognition, and acknowledgment as an Excellent League Member at Harbin Institute of Technology. She has presented her research findings at major scientific gatherings, including the International Conference on Computational Design and Simulation of Materials and the Chinese Materials Conference. With a strong record in computational materials science and interface behavior, Dr. Chen continues to advance innovative methodologies and scientific understanding toward the design, optimization, and reliability of next-generation materials systems.

Profiles: Scopus | Orcid

Featured Publications

Chen, Y., Dai, J., & Song, Y. Catalytic mechanisms of TiH2 thin layer on dehydrogenation behavior of fluorite-type MgH2: A first principles study.

Chen, Y. Y., Dai, J. H., Xie, R. W., & Song, Y. A first-principles study on interaction of Mg/Ni interface and its hydrogen absorption characteristics.

Chen, Y. Y., Dai, J. H., Xie, R. W., Song, Y., & Bououdina, M. First principles study of dehydrogenation properties of alkali and alkali-earth metal doped Mg₇TiH₁₆.

Chen, Y. Y., Dai, J. H., & Song, Y. Stability and hydrogen adsorption properties of Mg/Mg₂Ni interface: A first principles study.

Dai, J. H., Chen, Y. Y., Xie, R. W., & Song, Y. Influence of alloying elements on the stability and dehydrogenation properties of Y(BH₄)₃ by first principles calculations.

Dujiang Yang | Clinical Trials and Statistical Designs | Research Excellence Award

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Mr. Dujiang Yang | Clinical Trials and Statistical Designs | Research Excellence Award

The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University | China

Mr. Dujiang Yang is an emerging clinician-scientist in Traditional Chinese Medicine Orthopedics & Traumatology at Southwest Medical University, whose rapid academic growth and extensive publication record have positioned him among the most productive young researchers in his field, he completed advanced training at the Affiliated Hospital of Traditional Chinese Medicine, where he served as a spine-track resident physician and postgraduate researcher under Professor Guoyou Wang, contributing deeply to multiple national and provincial scientific projects. As first author, Yang has published 37 SCI-indexed papers, including numerous Q1 journals such as Annals of the Rheumatic Diseases, Journal of Extracellular Vesicles, Advanced Science, Chemical Engineering Journal, Leukemia, and Angiogenesis, covering topics spanning musculoskeletal regeneration, tumor microenvironment immunology, ferroptosis in osteoarthritis, disc repair biology, metabolic bone disease, and translational nanotechnology. His overall research impact is reflected in an estimated h-index of 2, 42 documents, and over 7 citations, demonstrating strong recognition across basic and translational medicine communities. Beyond research, he has gained substantial clinical proficiency, mastering lumbar fusion procedures, spinal fixation, PVP/PKP, trauma management, and more than 200 independent suturing cases. Yang has earned major distinctions including multiple first-class scholarships, Outstanding Graduate awards at both undergraduate and postgraduate levels, Excellent Youth Researcher recognitions. His technical skillset includes Western blotting, RT-PCR, flow cytometry, cell culture, molecular assays, animal experiments, advanced literature analysis, and scientific writing using GraphPad Prism and ImageJ. Combining rigorous clinical training, high-level publication output, and cross-disciplinary research capability, DuJiang Yang represents a driven, evidence-focused young scholar with strong potential for future leadership in musculoskeletal medicine and regenerative therapeutics.

Profile: Scopus 

Featured Publications

Yang, D., Yang, L., Yang, J., & Wang, G. (2025). Dual-targeting biomimetic nanoplatforms for tumor microenvironment-immune remodeling. Chemical Engineering Journal, 526, 171226.

Yang, D., Yang, J., Xu, H., & Wang, G. (2025). Minimal dose, maximal scrutiny: A critical appraisal of the feasibility and functional implications of a reduced-volume Nordic hamstring exercise protocol. Scandinavian Journal of Medicine & Science in Sports, 35(12)

Yang, D., & Wang, G. (2025). Beyond quantity: The nonlinear association between HDL cholesterol and coronary atherosclerosis. Atherosclerosis, 411, 120561.

Yang, D., & Wang, G. (2025). MSC-delivered CXCL10 for solid tumors: Navigating the translational hurdles from precept to clinic. Biomedicine & Pharmacotherapy, 193, 118735.

Kaili Wang | Machine Learning and Statistics | Best Researcher Award

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Dr. Kaili Wang | Machine Learning and Statistics | Best Researcher Award

university of malaya | Malaysia

Dr. Kaili Wang is an accomplished economist and Doctoral Candidate in Financial Economics at the University of Malaya, with a strong academic foundation in quantitative analysis, holding a master’s degree in Quantitative Economics from Zhongnan University of Economics and Law and a bachelor’s degree in Statistics from Luoyang Normal University. She has extensive teaching experience, having served as a full-time faculty member at the Business School of Nantong University of Technology, where she contributed significantly to both academic research and student mentorship. Her research expertise encompasses financial security, green finance, and the operational efficiency of financial institutions, reflected in her monographs, including Analysis of RMB Internationalization Path from the Perspective of Financial Security (sole author) and Research on the Long-term Mechanism of Green Finance Development (second author). She has also led impactful research projects, such as the Jiangsu Provincial University Philosophy and Social Sciences Research Project on the operational efficiency of city commercial banks. Kaili Wang has demonstrated a strong commitment to student development, guiding participants in national and provincial financial competitions to notable achievements, including second and third prizes in the National ETF Elite Challenge and the “East Money Cup” National College Students’ Financial Challenge, and earning recognition as an Excellent Supervisor. Her work reflects a combination of rigorous empirical analysis and practical engagement with financial markets, emphasizing sustainable finance and strategic economic development. With a focus on integrating academic excellence with real-world financial insights, Kaili Wang continues to advance knowledge in financial economics while nurturing the next generation of economists and financial professionals through research, mentorship, and academic leadership. Her career demonstrates a sustained dedication to both scholarly contributions and fostering student success in competitive financial arenas.

Profile: Orcid

Featured Publication

Wang, K. (2024). An analysis of the RMB internationalization path from the perspective of financial security.

Khurshid Hussain | Artificial Intelligence in Statistics | Research Excellence Award

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Mr. Khurshid Hussain | Artificial Intelligence in Statistics | Research Excellence Award

Kiost | South Korea

Mr. Khurshid Hussain is a dynamic researcher whose work spans advanced automotive engineering, semiconductor design, integrated sensing and communications, and AI-driven signal processing, establishing him as a multidisciplinary contributor across next-generation wireless, cybersecurity, and intelligent vehicular systems. He holds a Master’s degree in Advanced Automotive Engineering from Sun Moon University, South Korea, where he specialized in high-performance millimeter-wave circuit design with emphasis on 60 GHz digital variable-gain amplifiers, beamforming architectures, low-power attenuators, and chip-level ISAC systems for secure and intelligent communication. His research extends into geomatics and remote sensing, focusing on multimodal mapping using optical, SAR, and LiDAR streams, change detection, 3D reconstruction, and uncertainty-aware geospatial pipelines, alongside self-supervised and weak-supervised learning approaches for large-scale spatial data modeling. He is the inventor of a patented transistor-array-based variable attenuator and has authored an expanding collection of peer-reviewed publications in leading journals such as Electronics, IEEE Access, Applied Sciences, and IEEE Transactions, addressing topics ranging from radar–communication co-design and ultrasonic 3D beamforming sensors to predictive maintenance of aerospace components, OTFS-based V2X ISAC architectures, and AI-enhanced signal intelligence. His scholarly profile includes 9 documents, 77 citations, and an h-index of 4, reflecting his growing influence in mmWave IC design, wireless sensing, and AI-integrated communication. Khurshid has delivered technical presentations at major international conferences covering maritime IT convergence, high-frequency amplifier design, battery analytics, advanced beamforming, and power-efficient RF front-end systems. His expertise spans Cadence, HFSS, Python, MATLAB, OrCAD, cybersecurity tools, and vector network analyzers, reinforced by experience in transceiver integration, AI-chip convergence, intrusion detection systems, battery research, and embedded engineering. Earlier, he completed his B.Sc. in Electrical Engineering with a focus on IoT-based renewable-energy automation, where he developed sensor-driven, cloud-connected, and energy-efficient systems. Fluent in English and active in multicultural environments, Khurshid is known for his creativity, leadership, communication skills, and passion for innovation, continually advancing secure, intelligent, and energy-efficient technologies for the automotive, wireless, and sensing industries.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Hussain, K., & Yoo, J. (2025). Low-latency marine-based OTFS echo parameter estimation enabled by AI. Sensors, 25(23), Article 7104. DOI: 10.3390/s25237104

Hussain, K., Ali, E. M., Hussain, W., Raza, A., & Elkamchouchi, D. H. (2025). Robust OTFS-ISAC for vehicular-to-base station end-to-end sensing and communication. Electronics, 14(21), Article 4340. DOI: 10.3390/electronics14214340

Hussain, K., Jeon, W., Lee, Y., Song, I., & Oh, I. (2025). CMOS-compatible ultrasonic 3D beamforming sensor system for automotive applications. Applied Sciences, 15(16), Article 9201. DOI: 10.3390/app15169201

Hussain, K., & Oh, I. (2024). Joint radar, communication, and integration of beamforming technology. Electronics, 13(8), Article 1531. DOI: 10.3390/electronics13081531

Hussain, K., & Oh, I. (2024). Review of joint radar, communication, and integration of beam-forming technology. Preprint. DOI: 10.20944/preprints202404.0208.v1

Somayeh Bahramnejad | Survival Analysis and Reliability | Editorial Board Member

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Dr. Somayeh Bahramnejad | Survival Analysis and Reliability | Editorial Board Member

Sirjan University of Technology | Iran

Dr. Somayeh Bahramnejad is an accomplished Assistant Professor in the Department of Computer Engineering at Sirjan University of Technology, recognized for her scholarly impact and multidisciplinary contributions across reliability engineering, machine learning, image processing, computer architecture, and computer networks. She completed her B.S. degree in Computer Hardware Engineering at Ferdowsi University of Mashhad, earned her M.Sc. in Computer Architecture from Amirkabir University of Technology, and later obtained her Ph.D. in Computer Architecture from the University of Isfahan. With a steadily expanding academic portfolio, she has published influential research in reputable international journals, including Microelectronics Reliability, SN Computer Science, Computing, Computers & Electrical Engineering, and Scientia Iranica, contributing to a total of six peer-reviewed journal publications. According to her Google Scholar profile, she has accumulated 29 citations, an h-index of 3, and an i10-index of 1, demonstrating the visibility and growing influence of her research contributions. Dr. Bahramnejad has significantly advanced the field through innovative work on reliability improvement of SRAM-based FPGAs, reliability analysis of CR-VANETs, and the application of machine-learning methods for evaluating digital circuit reliability. She provides academic consultancy to seven M.Sc. students, supporting high-quality research, technical development, and scholarly productivity. Her professional presence on Google Scholar and ORCID ensures transparent documentation of her academic achievements and research outputs. Committed to interdisciplinary collaboration and impactful scientific inquiry, she focuses on designing robust, scalable, and reliable computing systems informed by both theoretical insight and practical need. With her dedication to excellence, mentorship, innovation, and long-term contributions to engineering research, Dr. Bahramnejad stands as a strong candidate for distinctions such as the Reliability Analysis Award, Best Researcher Award, Best Paper Award, Women Researcher Award, and Innovative Research Award, reflecting her potential for continued leadership within the global research community.

Profiles: Scopus | Orcid

Featured Publications

Bahramnejad, S. (2025). A fuzzy-arithmetic-based reliability assessment model for digital circuits (FARAM-DC). Microelectronics Reliability.

Bahramnejad, S., Movahhedinia, N., & Naseri, A. (2024). An LSTM-based method for automatic reliability prediction of cognitive radio vehicular ad hoc networks. SN Computer Science.

Bahramnejad, S., Movahhedinia, N., & Naseri, A. (2023). A deep learning method for automatic reliability prediction of CR-VANETs. Research Square.

Bahramnejad, S., & Movahhedinia, N. (2022). A fuzzy arithmetic-based analytical reliability assessment framework (FAARAF): Case study, cognitive radio vehicular networks with drivers. Computing.

Bahramnejad, S., & Movahhedinia, N. (2022). A reliability estimation framework for cognitive radio V2V communications and an ANN-based model for automating estimations. Computing.

Marina Bento | Causal Inference and Experimental Design | Best Researcher Award

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Ms. Marina Bento | Causal Inference and Experimental Design | Best Researcher Award

Federal University of Minas Gerais | Brazil

Ms. Marina Bento is a dedicated Brazilian biologist whose academic and professional journey reflects a strong commitment to advancing ecological research, biodiversity conservation, and the scientific understanding of subterranean ecosystems. Holding an M.Sc. in Ecology, Conservation and Wildlife Management from the Federal University of Minas Gerais (UFMG), she has built her expertise around the study of Neotropical bats, with a particular emphasis on cave-dwelling species in the Southern Espinhaço Range, a region of exceptional ecological and geological importance. Her research integrates rigorous ecological fieldwork with advanced statistical and quantitative approaches, enabling her to analyze species-habitat relationships, assess diversity patterns, and evaluate environmental and spatial drivers that shape the structure of bat communities. Throughout her graduate research, Marina conducted extensive, long-term field investigations inside iron-rich caves, areas that are both biologically unique and highly vulnerable due to expanding mining activities. Her pioneering M.Sc. project-“Diversity patterns of bats in caves of the Southern Espinhaço Range, Brazil”-provided the first comprehensive ecological evaluation of bat assemblages in this ecologically sensitive landscape. The study demonstrated that species turnover plays a dominant role in shaping community variation, while cave size, structural characteristics, and surrounding landscape changes significantly influence species richness and temporal patterns. The resulting publication in Mammalian Biology established Marina as an emerging researcher contributing high-quality scientific evidence to support conservation planning and environmental monitoring in regions threatened by habitat degradation. Beyond her academic accomplishments, Marina’s work serves an important conservation purpose: by revealing ecological dependencies and vulnerabilities within bat populations, her research offers critical insights for management strategies aimed at mitigating human impacts on subterranean wildlife. Her efforts underscore the ecological value of caves as biodiversity reservoirs and inform policies related to mining, land-use change, and species protection. Marina’s professional identity is grounded in interdisciplinary collaboration, field-based inquiry, and the application of quantitative methods to solve complex conservation challenges. With research interests spanning bat ecology, cave biology, biodiversity conservation, landscape ecology, and environmental monitoring, she has emerged as a promising scientist contributing meaningfully to the understanding of Brazil’s cave ecosystems. Her dedication to scientific integrity, conservation ethics, and evidence-based environmental stewardship continues to guide her work as she expands her research portfolio and establishes herself as a future leader in ecological and conservation sciences.

Profile:  Orcid

Featured Publication

Bento, M. M., Dias da Silva, L. H., da Silva, P. G., Dornellas, L. M. S. M., Pires, L. O., Auler, A. S., & Paglia, A. P. (2025). Diversity patterns of bats in caves of Southern Espinhaço Range, Brazil. Mammalian Biology, Advance online publication.

Yohanna Kusuma | Multivariate Statistical Analysis | Best Researcher Award

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Dr. Yohanna Kusuma | Multivariate Statistical Analysis | Best Researcher Award

The Royal Melbourne Hospital-The University of Melbourne | Australia

Dr. Yohanna Kusuma is an Australian-trained, internationally recognised neurologist and academic whose clinical and research work bridges acute stroke, neuroimaging, neurosonology, and movement disorders, with a strong translational focus across the Asia-Pacific region. She obtained her neurology specialist qualification from the University of Indonesia with honours, completed advanced fellowships in neurosonology and stroke at leading institutions in Singapore, and earned a PhD from Deakin University supported by an international scholarship, focusing on advanced CT-perfusion imaging in acute ischaemic stroke and the influence of ethnicity on imaging and clinical outcomes. She holds Fellowship of the Royal Australasian College of Physicians, qualifying her as a Consultant Neurologist in Australia. Dr Kusuma serves as Chief Investigator of the AI-powered SERENA platform for real-time stroke triage and decision support, leads the multinational APEX registry on acute ischaemic stroke with cancer spanning nine Asia–Pacific countries, and co-supervises PhD and honours students at Deakin University. She holds senior appointments in both Australia and Indonesia, including Senior Consultant Neurology at Metropolitan Medical Centre Hospital in Jakarta and Senior Research Fellow at The University of Melbourne. Her professional leadership includes representing Indonesia on the Asia Pacific Stroke Organisation and the Asian Stroke Advisory Panel, serving on the Education Council of the Australian Stroke Academy, and having previously served as a Co-opted Board Member of the World Stroke Organisation. Actively engaged in education and training, she has organised and delivered numerous neurosonology and stroke imaging workshops across the Asia-Pacific. Her research output is extensive, with an h-index of 4 and 144 citations, 13 peer-reviewed publications, book chapters, and international presentations. Dr Kusuma exemplifies a clinician-scientist who integrates cutting-edge imaging, neurosonology, and translational stroke research while advancing global collaborations in academic neurology, clinical innovation, and medical education.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

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Dr. Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

University of Lisbon | Portugal

Dr. Rasool Taban, Ph.D, is a distinguished Data Scientist currently affiliated with Technical University Institute – University of Lisbon, where he continues to advance the frontiers of Artificial Intelligence and Data Science. His academic journey began in Computer Engineering and evolved into a profound focus on Artificial Intelligence during his M.Sc. studies at the University of Tehran, where he graduated with honors in Artificial Intelligence and Robotics. His early research centered on developing an automated screening system designed to assist in diagnosing Autism Spectrum Disorder in children, demonstrating his ability to merge technology with meaningful social impact. Dr. Taban recently earned his Industrial Ph.D. at Institute – University of Lisbon, funded by the prestigious Marie Curie BIGMATH project, where his research specialized in addressing one of the most persistent challenges in statistical learning-imbalanced data. He successfully developed three novel balancing techniques, each tailored to optimize performance across different variable classes, making significant contributions to data reliability and analytical accuracy in machine learning models. With two published journal papers indexed in Scopus and SCI, Dr. Taban’s scholarly work reflects both academic rigor and applied innovation. He has also participated in multiple research and industry projects, collaborating with institutions such as the SDG Group, CIF/N26, Evenco International, and CTAD–Tehran Autism Center. His involvement as part of the editorial team for the International Conference on Robotics and Mechatronics (ICRoM) further underscores his leadership in advancing interdisciplinary research. Dr. Taban’s primary research interests include imbalanced data, statistical learning, data science, and financial data modeling. His contributions have not only expanded methodological knowledge in statistical computing but have also bridged the gap between theoretical frameworks and real-world data-driven applications, reflecting his commitment to excellence in both academia and industry.

Profiles:  Google Scholar | Linked In

Featured Publications

Taban, R., Nunes, C., & Oliveira, M. R. (2023). RM-SMOTE: A new robust balancing technique.

Taban, R., Nunes, C., & Oliveira, M. R. (2025). Mixed-robROSE: A novel balancing technique tailored for mixed-type datasets.

Bozorgnia, F., Arakelyan, A., & Taban, R. (2023). Graph-based semi-supervised learning for classification of imbalanced data. Submitted to Conference ENUMATH.

Shahri, M. A., & Taban, R. (2021). ML revolution in NLP: A review of machine learning techniques in natural language processing. Journal of Applied Intelligent Systems & Information Sciences (JAISIS), 2(1), 2.

Taban, R., Parsa, A., & Moradi, H. Tip-toe walking detection using CPG parameters from skeleton data gathered by Kinect. In International Conference on Ubiquitous Computing and Ambient Intelligence (pp. 9).

Palidan Muhetaer | Statistical Computing and Programming | Best Researcher Award

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Assoc Prof. Dr. Palidan Muhetaer | Statistical Computing and Programming | Best Researcher Award

Xinjiang University of Finance & Economics | China

Profiles: Scopus 

Featured Publications

Fan, Y., Qian, Y., Gong, W., Chu, Z., Qin, Y., & Muhetaer, P. (2024). Multi-level interactive fusion network based on adversarial learning for fusion classification of hyperspectral and LiDAR data