Jian Xia | Artificial Intelligence in Statistics | Research Excellence Award

Published on 12/12/202512/12/2025 by Statistics

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)

400

300

200

100

Citations
361

Documents
7

h-index
5

Citations

Documents

h-index

View Scopus Profile

Featured Publication

Molecular Self-Assembly of Au Nanoparticles on a Porous Cellulose Membrane Template for Highly Sensitive Colorimetric Detection of Glucose

– Chemistry select, 2023

Kaili Wang | Machine Learning and Statistics | Best Researcher Award

Published on 28/11/202528/11/2025 by Statistics

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.

Ki Ryong Kwon | Artificial Intelligence in Statistics | Best Faculty Award

Published on 20/11/202520/11/2025 by Statistics

Prof. Ki Ryong Kwon | Artificial Intelligence in Statistics | Best Faculty Award

Pukyong National University | South Korea

Professor Ki-Ryong Kwon is an eminent and highly respected scholar in electronics engineering, artificial intelligence, and computer science, serving as a leading Professor in the Division of Computer Engineering and AI at Pukyong National University, South Korea. he pursued his academic journey at Kyungpook National University, where he completed his bachelor’s, master’s, and doctoral degrees in Electronics Engineering, later advancing his research expertise through a prestigious postdoctoral fellowship at the University of Minnesota under the mentorship of Prof. Ahmed H. Tewfik, followed by a visiting scholar appointment at Colorado State University that further broadened his international academic exposure. Throughout his long-standing academic career, Professor Kwon has demonstrated exceptional leadership by serving as Dean of the College of Engineering, Vice-Dean of Engineering, Director of the Artificial Intelligence Lab, Director of the Center for Start-up Foundation, Director of the Center of IP Transfer, and Vice-Director of Industry-University Foundation Cooperation, contributing extensively to academic development, research infrastructure advancement, and student innovation ecosystems. Beyond the university environment, he has played influential strategic roles as Chairman of the Global Fintech Industry Promotion Center, Vice-Chairman of the Korea Cloud Association, Vice-Chairman of the Busan Federation of Service Industry, and Chair of the 4th Industrial Revolution Leadership Promotion Team for Busan City, where he has been instrumental in guiding regional and national initiatives in AI-driven transformation, digital economy growth, and emerging technology integration. His contributions to the global research community include major leadership roles in IEEE, the Korea Multimedia Society, the Korea Information Processing Society, and multiple international conferences where he has served as General Chair, Industrial Chair, Program Chair, and Organizing Chair. His research encompasses deep learning, digital watermarking, image forensics, signal processing, marine AI applications, smart digital-twin systems, cybersecurity, and intelligent multi-agent architectures. With approximately 118 published research documents, around 1,309 citations, and an estimated h-index in the mid-20s, he maintains a strong and influential academic footprint. Over his career, he has been honored with numerous Best Paper Awards, national recognitions, institutional leadership awards, and international distinctions that reflect his outstanding dedication to research excellence, innovation leadership, and the advancement of technology-driven societal development.

Profile: Scopus

Featured Publications

Tran, V. N., Choi, P., Le, H. S., Lee, S. H., & Kwon, K. R. (2025). DiffCoR: Exposing AI-generated images by using stable diffusion model based on consistent representation learning. IEEE Open Journal of the Computer Society, 1–12.

Tran Anh, T., Le, H.-S., Lee, S.-H., & Kwon, K. (2024). PointCT: Point central transformer network for weakly-supervised point cloud semantic segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024)

Lee, H.-G., Pham, N., Hoan, N. V., & Kwon, K.-R. (2024). Image-based outlet fire causing classification using CNN-based deep learning models. IEEE Access, 12(99),

Pham, N., Hoan, N. V., & Kwon, K. R. (2024). Improved YOLOv5 based deep learning system for jellyfish detection. IEEE Access, 12, 1–1.

Farhodov, X., Lee, S.-H., Lim, S.-J., & Kwon, K.-R. (2024, November). Flood risk simulation and mitigation in coastal areas: An ArcGIS Pro and deep learning approach. In 2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI)

Yohanna Kusuma | Multivariate Statistical Analysis | Best Researcher Award

Published on 10/11/202510/11/2025 by Statistics

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

Kusuma, Y., Che, B., Pillai, P., Nie, X., LL, L. Y., Sharma, V. K., Wong, A., Riley, P., et al. (2025). CT perfusion identified potential treatment opportunities in one in five mild strokes. Neurological Research and Practice, 7(1), 85.

Sharobeam, A., Shokri, M. J., Desai, N., Rao, A. S., Kusuma, Y., Palaniswami, M., et al. (2025). Detecting atrial fibrillation by artificial intelligence-enabled neuroimaging examination. Cerebrovascular Diseases.

Barker, J. L., Swarup, O., Kusuma, Y., Churilov, L., Donnan, G., Davis, S. M., et al. (2025). Thrombectomy with bridging thrombolytic may benefit Asian patients more than non-Asian patients: Insights from DIRECT-SAFE sub-analysis. Journal of Stroke, 27(1), 118–121.

Lee, T. H., Uchiyama, S., Kusuma, Y., Chiu, H. C., Navarro, J. C., Tan, K. S., et al. (2024). A systematic search and review of registered pharmacological therapies investigated to improve outcomes after a stroke. Cerebrovascular Diseases Extra, 14(1), 158–164.

Nie, X., Che, B., Qin, X., Kusuma, Y., Houlihan, C., Pan, Y., Yan, H., Yang, J., Wei, Y., et al. (2024). A pragmatic equation to predict outcome for endovascular thrombectomy in time-sensitive settings: Development and validation in RESCUE-RE registry and RMH-EVT registry. International Journal of Stroke, 19(2), 82–8

Orhan Dalkilic | Fuzzy Statistics and Uncertainty Modelling | Best Researcher Award

Published on 10/11/202510/11/2025 by Statistics

Mr. Orhan Dalkilic | Fuzzy Statistics and Uncertainty Modelling | Best Researcher Award

Bingol University | Turkey

Mr. Orhan Dalkilic is a distinguished mathematician and academic at Bingol University, Faculty of Arts and Sciences, Department of Mathematics. His research focuses on general topology, decision-making algorithms under uncertainty, hybrid set theories including fuzzy, rough, soft, neutrosophic, and virtual fuzzy parameterized soft (VFP-soft) sets, as well as data analysis and decision-support systems. He earned his Bachelor’s degree in Mathematics from Marmara University, followed by a Master’s and Ph.D. in Mathematics from Mersin University, specializing in the mathematical modelling of uncertainty and intelligent decision-making. Mr. Dalkilic has made substantial contributions to the development of mathematical models that enhance decision-making processes, providing innovative theoretical frameworks for handling uncertainty in real-world problems. With a prolific publication record, he has authored 49 academic works, including 20 papers indexed in the Science Citation Index (SCI), 243 Citation in Scopus, and 13 in other international journals. Among his SCI-indexed studies, 9 appear in Q1, 8 in Q2, and 3 in Q3 journals, reflecting his strong impact on the scientific community. His research often bridges mathematical theory and practical application, focusing on relationship analysis, topological structures, and soft computing techniques. Mr. Dalkilic has achieved an h-index of 14, an i10-index of 19, and has accumulated more than 650 citations, underscoring the influence and relevance of his scholarly output. His recent works, such as studies on decision-making approaches focusing on parameter-object relationships within soft set frameworks, highlight his continued commitment to advancing the fields of uncertainty theory and decision analysis. Through his innovative methods and interdisciplinary approach, Mr. Orhan Dalkilic has established himself as a leading researcher contributing significantly to the global development of modern applied mathematics and intelligent decision systems.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Dalkilic, O. (2025). Unifying relationships in uncertain environments: Examining relations in binary soft sets for expressing inter-object correspondence. The Journal of Supercomputing, 81(16), 1–29.

Stojanović, N., Vučićević, N., & Dalkilic O. (2025). Decision-making algorithm based on the scored-energy of neutrosophic soft sets. Afrika Matematika, 36(3), 1–22.

Demirtaş, N., Dalkilic, O., & Şimşekler Dizman, T. (2025). Optimal parameter identification in soft set frameworks: A decision support model. Boletim da Sociedade Paranaense de Matemática, 43(3), 1–10.

Dalkilic, O. (2025). Decision-making approaches focusing on best parameter-object pair for soft set. Neural Computing and Applications, 1–19.

Dalkilic, O. (2025). Nötrosofik parametreli aşırı-esnek kümelerin karar vericiler için bir genellemesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 25(1), 81–88.

Palidan Muhetaer | Statistical Computing and Programming | Best Researcher Award

Published on 05/11/202505/11/2025 by Statistics

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

Xinjiang University of Finance & Economics | China

Assoc Prof. Dr. Palidan Muhetaer is an Associate Professor at Xinjiang University, a distinguished laureate of the “Tianshan Talent” and “Tianshan Doctor” programmes, and an active council member of the Xinjiang Doctoral Association as well as a certified Science & Technology Commissioner. She is a research fellow of the Chinese Association for Artificial Intelligence, a senior member of CCF, and a standing member of the IMLIP Committee on Multi-lingual Intelligent Processing. As a core researcher of the Xinjiang Key Laboratory of Multi-lingual Processing and an external master-supervisor at the Aviation College of Changji University, she contributes significantly to AI education and research capacity-building while serving as a reviewer for several academic journals. Her research primarily focuses on artificial intelligence, cyber-security, and speech/signal processing, integrating multilingual computing with data-driven intelligence. She has led over ten undergraduate innovation and entrepreneurship projects, research-training programmes, and computer-design competitions. To date, she has published more than thirty peer-reviewed papers, including several indexed by SCI and EI, and over twenty in Chinese core journals. Her intellectual property portfolio includes one invention patent and six registered software copyrights. She has served as Principal Investigator for multiple national and provincial-level research grants in areas such as large-model-driven sentiment–semantic alignment, university-enterprise AI training, neural-network-based low-resource speech synthesis, and intelligent e-commerce for agri-tourism. Her research outputs have advanced multilingual intelligent processing and innovation-driven education, contributing to the technological and economic growth of Xinjiang and beyond. Her representative publications span hyperspectral-LiDAR fusion, BERT-based sentiment analysis, multilingual named-entity recognition, and deep-learning-driven speech synthesis. Currently, her research record comprises 2 documents with 12 citations across international and Chinese journals, reflecting an h-index of 2 and demonstrating her growing scholarly impact in artificial intelligence and computational linguistics.

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

Moumita Mukherjee | Machine Learning and Statistics | Best Researcher Award

Published on 04/11/202504/11/2025 by Statistics

Dr. Moumita Mukherjee | Machine Learning and Statistics | Best Researcher Award

Charite-University Medicine Berlin | Germany

Dr. Moumita Mukherjee is an accomplished health economist and digital health researcher with expertise in health systems research, machine learning applications in healthcare, and interdisciplinary teaching. She holds a PhD in Economics from the University of Calcutta, an MBA in Entrepreneurship, Innovation and Project Development from International Telematic University, and an MSc in Data Science from the University of Europe for Applied Sciences, Germany. Her professional experience spans both academic and applied research environments, including positions at Charite-University Medicine Berlin, the Indian Institute of Public Health in Shillong, and the Berlin School of Business and Innovation. She has contributed extensively to global health research focusing on digital transformation, equity in healthcare access, and the use of data-driven methods for improving health outcomes. Her body of work includes numerous peer-reviewed publications in leading journals such as Scientific Reports, Journal of Health, Population and Nutrition, Journal of Health Management, and International Journal for Equity in Health, as well as book chapters and authored volumes addressing child health, nutrition, and health equity. In her current role at Charite-University Medicine Berlin, she lectures on digital health and artificial intelligence, supervises master’s theses, and mentors students. With advanced technical proficiency in Python, STATA, and NVivo, she applies econometric, machine learning, and deep learning models to address complex public health and policy questions. Her interdisciplinary approach integrates health economics, digital innovation, and policy analysis to support equitable and sustainable health systems worldwide. Through her research, teaching, and mentorship, Dr. Moumita Mukherjee continues to bridge data science and health economics to shape the future of evidence-based global health policy and digital healthcare transformation.

Profiles: Google Scholar | Orcid

Featured Publications

Fountis, D. A., & Mukherjee, D. M. N26: A fintech-revolutionizing the banking sector’s supply chain. JournalNX, 7(2), 389-404.

Mukherjee, M. Childhood under-nutrition and SES gradient in India: Myth or reality. Science Journal of Public Health, 3(1), 120-137.

Mukherjee, M., & Ghosh, S. Biomass fuel as an important correlate of childhood morbidity and under-nutrition in India. Essays on Indian Economic Problems, 63.

Mukherjee, M., & Fountis, A. Ensuring higher efficiency in the M&E operational process to improve the nutrition service delivery effectiveness during disaster. IOSR Journal of Humanities and Social Science, 25(12), 47-55.

Mukherjee, M., & Batta, A. (2024). Possibility of the optimum monitoring and evaluation (M&E) production frontier for risk-informed health governance in disaster-prone districts of West Bengal, India. Journal of Health, Population and Nutrition, 43(1), 148.

Ching Chih Tsai | Fuzzy Statistics and Uncertainty Modelling | Best Researcher Award

Published on 07/10/202507/10/2025 by Statistics

Prof. Ching Chih Tsai | Fuzzy Statistics and Uncertainty Modelling | Best Researcher Award

Prof. Ching Chih Tsai | National Chung Hsing University | Taiwan

Prof. Ching Chih Tsai is a distinguished academic in electrical engineering and control systems, currently serving as a Life Distinguished Professor at the Department of Electrical Engineering, National Chung Hsing University (NCHU), Taiwan. He earned his Ph.D. from Northwestern University in 1991. Dr. Tsai has held significant leadership roles, including serving as the President of the Chinese Automatic Control Society (CACS), the Robotics Society of Taiwan (RST), and the International Fuzzy Systems Association (IFSA). He has also been a Board of Governors member of IEEE Systems, Man, and Cybernetics Society (SMCS) and is currently the Dean of the College of Electrical Engineering and Computer Science at NCHU. An IEEE Fellow, his research focuses on intelligent control systems, mobile robotics, and automation intelligence. Dr. Tsai has published over 700 technical articles, with more than 20 in the International Journal of Fuzzy Systems since 2005. His recent work includes a 2025 paper on intelligent adaptive formation control for multi-quadrotors, introducing a hybrid controller combining Output Recurrent Fuzzy Broad Learning Systems (ORFBLS), reinforcement learning, and adaptive backstepping sliding mode control. According to Scopus, he has an h-index of 29, with 3,902 citations from 272 documents.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Rospawan, A., Tsai, C.-C., & Hung, C.-C. (2025). Two-layer intelligent learning control using output recurrent fuzzy neural long short-term memory broad learning system with RMSprop. IEEE Access.

Tsai, C.-C., Hung, C.-C., Mao, C.-F., Wu, H.-S., & Chen, C.-H. (2025). Fuzzy neural LSTM-RBLS for fractional-order PID sliding-mode motion control of autonomous mobile robots with four ISID wheels. International Journal of Fuzzy Systems.

Tsai, C.-C., Mao, C.-F., & Hussain, K. (2025). Intelligent adaptive formation control using ORFBLS and reinforcement learning for uncertain tilting multi-quadrotors. International Journal of Fuzzy Systems. =

Rospawan, A., Tsai, C.-C., & Hung, C.-C. (2025). Intelligent MIMO ORFBLS-based setpoint tracking control with its application to temperature control of an industrial extrusion barrel. International Journal of Fuzzy Systems.

Tsai, C.-C., Huang, H.-C., Chen, H.-Y., Hung, C.-C., & Chen, S.-T. (2024). Intelligent collision-free formation control of ball-riding robots using output recurrent broad learning in industrial cyber-physical systems. IEEE Transactions on Industrial Cyber-Physical Systems.