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

Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

Published on 06/11/202506/11/2025 by Statistics

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

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.

Kiran Sree Pokkuluri | Machine Learning and Statistics | Excellence in Research Award

Published on 31/10/202531/10/2025 by Statistics

Prof. Dr. Kiran Sree Pokkuluri | Machine Learning and Statistics | Excellence in Research Award

Shri Vishnu Engineering College For Women | India

Prof. Dr. Kiran Sree Pokkuluri is a distinguished academician, researcher, and innovator in the field of Artificial Intelligence and Machine Learning with an illustrious career of academic and research excellence. Currently serving as Professor and Head of the Department of Computer Science and Engineering at Shri Vishnu Engineering College for Women, he has significantly contributed to advancing computational intelligence and data-driven innovation in academia and industry. He holds a Ph.D. in Artificial Intelligence from JNTU-Hyderabad and has an impressive scholarly record with over 100 research publications in reputed SCI and Scopus-indexed journals, a citation count exceeding 653, an h-index above 13, and an Documents exceeding 152, reflecting the global impact of his research. His research areas include Deep Learning, Healthcare Analytics, Bioinformatics, IoT Power Optimization, Big Data Analytics, and Cloud Computing. Dr. Sree has authored six textbooks with ISBNs on Artificial Intelligence, Machine Learning, and Deep Learning, and has filed and published six patents in the domains of AI and intelligent systems. His innovations such as the Hybrid Deep Neural ZF Network (HDNZF-Net) have set new benchmarks in real-time speech enhancement for speech-impaired individuals and IoT optimization. He has completed five major funded projects and collaborated with premier institutions including Stanford University through the UIF program, fostering cross-disciplinary innovation. A recognized thought leader, Dr. Sree serves as Editor-in-Chief, editorial board member, and reviewer for multiple international journals. His remarkable achievements have earned him prestigious recognitions like the Bharat Excellence Award and Rashtriya Ratan Award, and he has been featured in Marquis Who’s Who in the World. As Global Vice President of the World Statistical Data Analysis Research Association (WSA) and a member of professional bodies such as IEEE, ISTE, CSI, and IAENG, Dr. Kiran Sree continues to inspire excellence in AI-driven research, education, and technological innovation.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Venkatachalam, B., Pokkuluri, K. S., Suguna Kumar, S., Dhandapani, A., & Bhonsle, M. (2025). Adaptive fuzzy heuristic algorithm for dynamic data mining in IoT integrated big data environments. Journal of Fuzzy Extension and Applications, 6(3), 615–636.

Pokkuluri, K. S., Sarkar, P., Birchha, V., Mathariya, S. K., Veeramachaneni, V., & others. (2025). Intelligent reasonable optimization for virtual machine provisioning in hybrid cloud using fuzzy AHP and cost-effective autoscaling. SN Computer Science, 6(7), 1–15.

Sivanuja, M., Raju, P. J. R. S., Prasad, M., RR, P. B. V., Kumar, K. S., & Pokkuluri, K. S,. (2025). A novel ensemble-based deep learning framework combining CNN and transfer learning models for enhanced wildfire detection. In Proceedings of the 2025 International Conference on Computational Robotics, Testing and Applications.

Alzubi, J. A., Pokkuluri, K. S., Arunachalam, R., Shukla, S. K., Venugopal, S., & others. (2025). A generative adversarial network-based accurate masked face recognition model using dual scale adaptive efficient attention network. Scientific Reports, 15(1), 17594.

Pokkuluri, K. S., Chandanan, A. K., Mishra, A. K., Jyothi, D., Lavanya, M. S. S. L., & others. (2025). Deep learning-enhanced intrusion detection and privacy preservation for IIoT networks. In Proceedings of the 2025 4th International Conference on Distributed Computing and Electrical Systems.

Tao Zhong | Machine Learning and Statistics | Best Researcher Award

Published on 31/10/202531/10/2025 by Statistics

Dr. Tao Zhong | Machine Learning and Statistics | Best Researcher Award

Sun Yat-sen University | China

Dr. Zhong Tao is a dedicated interdisciplinary researcher specializing in environmental engineering, material science, and computational modelling. A native of Chongqing, China, he is a member of the Communist Party of China and currently based in Guangzhou. He earned his Bachelor’s degree in Environmental and Ecological Engineering with a minor in Computer Science and Technology from Sichuan Agricultural University, followed by a Master’s in Environmental Science and Engineering from Guangxi University under Prof. Yu Zebin, and is pursuing his Doctor of Engineering (Ph.D.) in Resources and Environment at Sun Yat-sen University under Prof. He Chun. His research focuses on the design and development of high-activity environmental functional materials for atmospheric and water pollutant removal, catalytic ozonation, and clean-energy catalysis, including hydrogen production via water splitting. He also employs Density Functional Theory (DFT) to analyze catalytic materials and pollutant molecular structures, building structure–property relationships to guide experiments. Dr. Zhong has contributed to 31 SCI-indexed papers, including 11 as first or co-first author, and applied for 5 patents, with 4 granted. His ongoing research includes national and provincial projects as principal investigator or key contributor. He has received multiple national and university-level scholarships and awards for academic excellence, innovation, and leadership. His Scopus metrics reflect a growing international influence, with an h-index of 10, 22 documents, and over 343 citations, underscoring his strong academic productivity. Known for his rigorous research approach, interdisciplinary collaboration, and mentoring of peers and students, Dr. Zhong also pursues interests in history, literature, and sports, maintaining an optimistic, resilient, and disciplined outlook that complements his scientific career.

Profiles: Scopus

Featured Publications

Guo, X., Yao, Z., Long, X., Zeng, L., Wang, C., Fang, Z., Zhong, T., Tian, S., Shu, D., & He, C. (2025). Recent advances in tailored nanostructured ozonation catalysts for enhanced VOCs removal: Synergistic optimization of scale configuration and electronic microenvironment.

Zhong, T., Yao, Z., Zeng, L., Zhao, H., Long, X., Li, T., Tian, S., & He, C. (2025). Manipulating spin-configuration via electron reverse overflow to dynamically tune the adsorption behavior of sulfur-containing intermediates for enhanced sulfur resistance.

Ahmad Abuhani | Machine Learning and Statistics | Best Researcher Award

Published on 27/10/202527/10/2025 by Statistics

Assist Prof. Dr. Ahmad Abuhani | Machine Learning and Statistics | Best Researcher Award

Middle East University | Jordan

Assist Prof. Dr. Ahmad Abuhani is an accomplished Assistant Professor of Interior Design at the Faculty of Engineering and Design, Middle East University, Amman, Jordan, known for his distinguished academic, artistic, and research achievements. He earned his B.Sc., M.Sc., and Ph.D. in Interior Design from the Moscow State University of Applied Arts named after S.G. Stroganov, Russia, where he specialized in interior composition, architectural planning, and the artistic formation of traditional Jordanian housing. His doctoral research, titled “The Construction System and Technical Composition of the Interior Design of the Jordanian Home,” demonstrates his dedication to blending cultural identity with modern design principles. Dr. Abu Hani has held several academic and administrative positions, including Head of the Interior Design Department at Middle East University, Amman University, and Yarmouk University. His teaching expertise covers a wide range of design areas such as architectural drawing, color theory, space planning, and professional practice. His scholarly contributions include publications in international journals and conferences focusing on design aesthetics, visual communication, and creative methodology. His research interests span fine and applied arts, architectural design, descriptive geometry, and color theory. A member of the Fine Artists Association (Amman) and the International Council of Societies of Industrial Design (ICSID), he also serves as a reviewer for Horizon Research Publishing and participates in multiple academic committees and juries. Dr. Abu Hani has exhibited his work in 15 personal and 9 collective exhibitions across Jordan and Russia, receiving prestigious awards including first place in national art competitions and recognition from the Ministry of Culture, Dr. Abu Hani continues to make impactful contributions to the fields of interior design, applied arts, and creative education, combining innovation with cultural and academic excellence.

Profiles: Orcid | Google Scholar

Om Sambhaji Shelke | Operations Research and Statistical Optimization | Excellence in Research Award

Published on 24/10/202524/10/2025 by Statistics

Dr. Om Sambhaji Shelke | Operations Research and Statistical Optimization | Excellence in Research Award

Sinomune Pharmaceutical Co. Ltd | China

Dr. Om Sambhaji Shelke is a highly accomplished pharmaceutical scientist with extensive expertise in the formulation and development of topical, semisolid, solid, and liquid drug products. Holding a Ph.D. in Pharmacy and an M. Pharmacy from Savitribai Phule Pune University, he has over a decade of experience across global markets including the US, EU, China, Hong Kong, and India. Currently serving as Chief Scientist at Sinomune Pharmaceutical Co. Ltd. in Wuxi, Jiangsu, China, Dr. Shelke leads the development of generic topical products for the NMPA market. He is skilled in Quality by Design (QbD)-based development of creams, ointments, gels, shampoos, and solutions, with specialization in novel formats such as emulgel, organogel, nanogel, solid lipid nanoparticles, suspensions, and toothpaste. He also holds three granted patents and has published multiple scientific and technical articles. Throughout his career, he has received multiple awards recognizing his innovation, knowledge sharing, and leadership, including the StarFire Award. Dr. Shelke has held pivotal roles at leading pharmaceutical and consumer healthcare companies such as Prinbury Biopharm Co. Ltd., Encube Ethicals Pvt Ltd., Bright Future Pharmaceuticals Ltd., Unilever Industries Pvt Ltd., Abbott Healthcare Pvt Ltd., Dr. Reddy’s Laboratories, and Glenmark Pharmaceuticals Ltd., contributing to both regulatory-compliant NDA, ANDA, and 505(b)(2) products as well as OTC, cosmetic, and personal care formulations. His professional journey reflects a consistent commitment to scientific excellence, innovative product development, and leadership in cross-functional teams, positioning him as a prominent figure in global pharmaceutical research and development.

Profiles: Google Scholar | Orcid

Featured Publications

F. Jie, O. Shelke, Z. Yijie, C. Yulan, & L. Yongbo. (2025). Q1 and Q2 selection, Q3, IVRT, IVPT, pharmacokinetic and pharmacodynamic evaluation of topical generic product. Drug Development and Industrial Pharmacy, 51(6), 555–565.

J. Feng, O. S. Shelke, Y. Chen, Z. Zhang, X. Tang, & Y. Zhu. (2025). IVRT and IVPT of desonide lotion and cream: Correlation with human bioequivalence study. Journal of Pharmaceutical Innovation, 20(5), 196.

S. Krishna Phani Chandra, S. Om Sambhaji, & N. Shorgar. (2025). Quantification of leniolisib in rat plasma using LC-MS/MS: Method development, validation, and pharmacokinetic study. African Journal of Biological Sciences, 7(7), 121–142.

S. Om. (2025). Editorial article: The transformative impact of AI in pharmaceutical drug product development. Insights of Pharmatech, 1(2), 1.

S. Gadge, O. S. Shelke, R. Pingale, P. Palande, S. Tandale, P. Sonawane, … (2025). Development and evaluation of a polyherbal neem-based emulgel enriched with herbal oils for enhanced topical delivery and antibacterial efficacy. Journal of Chemical Health Risks, 15(3), 2189–2209.

Fatih UCUN | Regression and Correlation Analysis | Best Researcher Award

Published on 21/10/202521/10/2025 by Statistics

Prof. Dr. Fatih UCUN | Regression and Correlation Analysis | Best Researcher Award

Suleyman Demirel University | Turkey

Prof. Dr. Fatih Ucun is a distinguished physicist specializing in atomic and molecular physics, with expertise in electron paramagnetic resonance (EPR), nuclear magnetic resonance (NMR), and infrared (IR) spectroscopy. He completed his B.Sc. and M.Sc. in Physics at Atatürk University and earned his Ph.D. from Ondokuz Mayıs University. He is a full professor in the Department of Physics at Suleyman Demirel University in Isparta, Turkey. Prof. Ucun has made significant contributions to computational chemistry, molecular modeling, and quantum mechanics, bridging theoretical insights with practical applications in material science and nanotechnology through pioneering studies on molecular electronic structures and quantum chemical simulations. He has authored 91 publications, cited 883 times, and holds an h-index of 16, reflecting his substantial impact in the field. In addition to his research, he has published four books and serves on editorial boards of scientific journals, demonstrating his leadership and influence in the academic community. His work has advanced the understanding of atomic-level interactions and energy transfer mechanisms, while mentoring future scientists and enriching scientific progress in physical chemistry and computational modeling.

Profiles: Scopus | Google Scholar

Featured Publications

Ucun, F., Isik, Y. E., & Tiryaki, O. (2025). An approach to description of isotropic hyperfine interaction constants in the fluorinated nitrobenzene and nitrophenol radical anions: DFT calculations vs. experiment. Russian Journal of Physical Chemistry A, 99, 2498–2505.

Yolburun, H., & Ucun, F. (2023). EPR analysis of dinitrobenzoic acid anion radicals. International Journal of Computational and Experimental Science and Technology.

Ucun, F. (2023). EPR analysis of dinitrobenzoic acid anion radicals. International Journal of Computational and Experimental Science and Technology.

Ucun, F., & Alakuş, N. (2022). Enthalpies and activation energies of several gas reactions by intrinsic reaction coordinate (IRC) calculations. El-Cezeri, 9(2), 576–583.

Ucun, F., & Küçük, S. (2022). Triafulvalen, pentafulvalen ve heptafulvalenin katyon ve anyon radikallerinin EPR aşırı ince-yapı yapıları: Bir teorik çalışma. Süleyman Demirel University Faculty of Arts and Science Journal of Science.

Oleg Selyugin | Big Data and Statistical Analytics | Big Data Analytics Award

Published on 21/10/202521/10/2025 by Statistics

Dr. Oleg Selyugin | Big Data and Statistical Analytics | Big Data Analytics Award

Joint Institute for Nuclear Research | Russia

Dr. Oleg Selyugin is a Russian empirical and theoretical physicist with a distinguished career in high-energy hadron scattering and the structure of hadrons. After completing his studies at the Physics Department of Moscow State University, he joined the Joint Institute for Nuclear Research (JINR), first as a probationer and researcher at the Laboratory of Nuclear Problems, and later at the Bogoliubov Theoretical Laboratory (BTL), where he now serves as a leading scientist. At BTL, he earned his Ph.D. with the thesis “High energy elastic hadron-hadron scattering in a wide momentum transfer region,” and later obtained his Doctor of Physics and Mathematics degree with the thesis “The structure of high-energy amplitude of the elastic hadron-hadron scattering in the diffraction region.” Dr Selyugin has been recognized with multiple International Prizes of JINR for his outstanding contributions to polaron physics, hadron physics, and high-energy physics. His primary research interests include the structure of hadrons (PDFs, GPDs, form-factors), phenomenology of high-energy physics (differential cross sections, spin phenomena), models of extra dimensions (d-brane gravity), and nonlinear effects. He has been actively involved in interpreting experimental results from the CERN LHC, particularly the TOTEM and ATLAS Collaborations. His theoretical work integrates electromagnetic and gravitational form-factors derived from novel t-dependent GPDs, as well as soft and cross-even pomeron contributions, within dispersion-relation-based frameworks. With over 180 scientific papers, Dr Selyugin has made a profound and lasting impact on the understanding of elastic hadron scattering at high energies. Although specific bibliometric indicators such as 17 h-index, 78 documents, and 815 citations vary across databases, his scientific influence is widely recognized within the international physics community. He continues his pioneering research at JINR in Dubna, Russia.

Profiles: Scopus | Orcid

Featured Publications

Selyugin, O. V. (2024). Unified description of elastic hadron scattering at low and high energies. Physics of Atomic Nuclei, 87(S2), S349–S362.

Selyugin, O. V., & Teryaev, O. (2024). GPDs and gravitational form factors of nucleons (quark and gluon contributions). Physics of Atomic Nuclei, 87(4), 518–522.

Selyugin, O. V. (2024). New properties of elastic pp and ppbar scattering at high energies. Journal Name, Volume(Issue)

Selyugin, O. V. (2023). Hadron interaction at large distances and new features of differential cross sections at the energies of the Large Hadron Collider. Physics of Particles and Nuclei Letters, 20(3), 395–399.