Ali Ali | Artificial Intelligence and water Resources Management | Research Excellence Award

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Dr. Ali Ali | Artificial Intelligence and water Resources Management | Research Excellence Award

Brunel University London | United Kingdom

Dr. Ali Ali is an emerging researcher and PhD student in Civil Engineering at Brunel University London, specializing in Artificial Intelligence applications for water resources management. He holds a BEng (Hons) in Civil Engineering from Brunel University and a Diploma with Distinction from Kaplan International College, demonstrating strong academic performance. Dr. Ali has extensive experience with civil engineering and computational software, including AutoCAD, Groundwater Modelling System (GMS), ABAQUS, MATLAB, and programming languages Python and R, enabling advanced modeling, simulation, and data analysis. He has applied his expertise in designing steel and concrete structures while addressing economic, social, and environmental challenges. His professional experience includes multiple Graduate Teaching Assistant roles in Civil Engineering and Computer Science, supporting both undergraduate and master’s students with lab sessions, course material, tutoring, and programming instruction. Additionally, he gained practical industry experience through internships in project management and turbomachinery design, enhancing his skills in project coordination and operational efficiency. Dr. Ali’s research interests focus on optimizing water resources management using AI, sustainable infrastructure design, and hydrological modeling. He has contributed to academic documents, reports, and simulations, with ongoing work aimed at publication in peer-reviewed journals. His work demonstrates a commitment to bridging theoretical research and practical engineering solutions, advancing innovation in civil and environmental engineering.

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Featured Publication

Aquifer-specific flood forecasting using machine learning: A comparative analysis for three distinct sedimentary aquifers – Science of the Total Environment, 2025 | Open Access

Seema Irshad | Psychology | Women Researcher Award

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Dr. Seema Irshad | Psychology | Women Researcher Award

King Faisal University | Saudi Arabia

Dr. Seema Irshad is an Assistant Professor of Psychology with more than a decade of experience in higher education, research, and clinical practice. She earned her PhD in Psychology from Aligarh Muslim University, developing a strong academic foundation in psychological theory, research methodology, and applied practice. Her academic career has been primarily associated with a leading public university in the Kingdom of Saudi Arabia, where she has contributed extensively to undergraduate and graduate teaching, curriculum design, and academic coordination. Her teaching expertise spans abnormal psychology, cognitive processes, work motivation, and applied psychology, with a strong emphasis on student-centered and technology-enhanced learning. In parallel, she has been actively engaged in research through national and international collaborations, including earlier work as a research assistant and junior project fellow on large-scale public health and educational research projects. Her research interests focus on mental health, psychosocial interventions, psychometric assessment, systematic reviews, and the emerging role of artificial intelligence in psychological practice. She has published more than 20 scholarly papers and has presented research at numerous academic conferences. Her contributions include successful involvement in competitive research grants and sustained mentorship of students and early-career researchers. Overall, her work reflects a strong commitment to advancing psychological science, improving mental health outcomes, and fostering academic excellence through teaching, research, and mentorship.

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Ruoyi Li | Government and Public Policy Analysis | Research Excellence Award

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Ms. Ruoyi Li | Government and Public Policy Analysis | Research Excellence Award

Xiamen University | China

Ms. Ruoyi Li is a dedicated Ph.D. candidate in Public Administration at Xiamen University, focusing on government regulation, policy analysis, and sustainable development under the “Dual‑Carbon” goals. She holds dual Master’s degrees from the Australian National University in Digital Humanities & Public Culture and General & Applied Linguistics, and a Bachelor’s degree in Teaching Chinese as a Foreign Language from Jiangxi Normal University, where she graduated with distinction and received multiple scholarships and awards. Her research expertise spans policy evaluation, environmental economics, cross‑strait regional development, and technological impacts on organizational structures. Ms. Li has participated in high-impact research projects, including field investigations and policy reports for local government integration initiatives in Pingtan, and has collaborated with institutions such as the Environmental Economics & Policy Lab (Peking University and University of Chicago), Global Environmental Institute, and major energy corporations. She has authored peer-reviewed articles in Finance Research Letters as first and corresponding author and contributed to numerous working papers on cultural heritage, digital humanities, and museum studies. Skilled in fieldwork, in-depth interviews, data analysis, and ontology engineering, she combines interdisciplinary knowledge with practical research application. Ms. Li’s work bridges public administration, environmental policy, and digital culture, contributing to evidence-based governance, sustainable development, and innovative knowledge representation.

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Rasha Fouad | Healthcare Data Analysis | Research Excellence Award

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Assist. Prof. Dr. Rasha Fouad | Healthcare Data Analysis | Research Excellence Award

 Kind Saud Bin Abdulaziz University for Health Sciences | Saudi Arabia

Prof. Dr. Rasha Fouad is a distinguished nursing scholar with a PhD in Nursing, specializing in community and gerontological health nursing. She earned her Bachelor’s, Master’s, and Doctorate degrees from Alexandria University. With over 29 years of professional experience, she has held key academic and leadership roles, including Professor of Gerontological Nursing, Head of Gerontological Nursing Department, Vice Dean for Community Service and Environmental Development, and Acting Dean. She has also contributed internationally as an Assistant Professor in Jordan and Saudi Arabia. Prof. Fouad has taught a wide range of courses at undergraduate and postgraduate levels, including gerontological nursing, psychosocial aspects of aging, nursing administration, community health, and research methodology. Her research interests focus on gerontological nursing, psychosocial well-being of older adults, community health, and nursing education. She is an active reviewer for several international journals and has conducted numerous workshops on research methods, academic enhancement, and simulation-based learning. Her dedication to advancing nursing education and research has earned her recognition for excellence, inspiring future generations of nursing professionals through her teaching, mentorship, and scholarly contributions.

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Yang Peng | Public Health Analytics | Research Excellence Award

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Dr. Yang Peng | Public Health Analytics | Research Excellence Award

The University of Queensland | Australia

Dr. Yang Peng is an accomplished epidemiologist and Research Fellow at the Epidemiology Division, Cancer Council Victoria, with a strong focus on population health, cardiovascular disease, and cancer epidemiology. He earned his PhD in Epidemiology from The University of Queensland, Australia, following an MMed in Epidemiology and Biostatistics from Guangdong Pharmaceutical University, China, and a BMed in Clinical Medicine from Hebei Medical University, China. Dr. Peng’s professional experience spans roles in cardiovascular outcomes research, Indigenous cancer survival studies, and large-scale international cohort analyses, involving data from over 2 million participants across multiple cohorts. His expertise includes statistical analysis of large-scale linked data, systematic review, meta-analysis, and advanced use of statistical software such as Stata, SAS, and SPSS. He has authored 41 documents, cited 667 times across 649 documents, and holds an h-index of 17, with publications in high-impact journals including Scientific Reports, Stroke, European Heart Journal, and Cancer Epidemiology, Biomarkers & Prevention. His research interests focus on lifestyle factors, cardiovascular health metrics, cancer prevention adherence, stroke and heart failure outcomes, and population-level survival disparities. Through rigorous research, collaborative projects, and evidence-based analyses, Dr. Peng contributes significantly to understanding disease epidemiology, improving public health policies, and guiding preventative strategies globally.

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Erika Azorin-vega | Qualitative Research | Women Researcher Award

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Dr. Erika Azorin-vega | Qualitative Research | Women Researcher Award

Instituto Nacional De Investigaciones Nucleares | Mexico

Erika Patricia Azorín Vega is a distinguished researcher at the Instituto Nacional de Investigaciones Nucleares, specializing in radiopharmaceuticals and their applications in cancer diagnosis and therapy. She holds a Bachelor’s degree in Experimental Biology from Universidad Autónoma Metropolitana-Iztapalapa, a Master’s in Sciences, and a Ph.D. in Bio-Physiology and Neurosciences from CINVESTAV-IPN, with research focused on cellular interactions with extracellular matrices and tumor cell biology. Since 2013, she has led pioneering work in radiopharmaceutical development, resulting in four international patents and over 80 scientific publications with nearly a thousand citations. Her academic contributions include directing multiple undergraduate and graduate theses, reviewing projects for national and international funding agencies, and participating in thesis evaluations globally. She has served as a professor and invited lecturer at several universities, delivering specialized courses in radiobiology, dosimetry, and medical physics, both nationally and internationally. Her research interests encompass radiopharmaceutical design, cancer radiotherapy, metabolomics, and radiation protection. Recognized for her excellence in scientific communication, she has delivered numerous conferences and outreach presentations to advance knowledge in nuclear medicine. Erika’s work continues to shape the field of radiopharmaceutical research and the training of highly skilled professionals in medical physics and biotechnology.

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Featured Publications

Jiménez-Mancilla, N., Ferro-Flores, G., Santos-Cuevas, C., & et al. (2013). Multifunctional targeted therapy system based on 99mTc/177Lu-labeled gold nanoparticles-Tat(49–57)-Lys3-bombesin internalized in nuclei of prostate cancer. Journal of Labelled Compounds and Radiopharmaceuticals, 56(13), 663–671.

Ferro-Flores, G., Luna-Gutiérrez, M., Ocampo-García, B., Santos-Cuevas, C., & et al. (2017). Clinical translation of a PSMA inhibitor for 99mTc-based SPECT. Nuclear Medicine and Biology, 48, 36–44.

Mendoza-Nava, H., Ferro-Flores, G., Ramírez, F. M., Ocampo-García, B., & et al. (2016). 177Lu-dendrimer conjugated to folate and bombesin with gold nanoparticles in the dendritic cavity: A potential theranostic radiopharmaceutical. Journal of Nanomaterials, 2016(1), 1039258.

Azorín-Vega, A. V. E., Ferro-Flores, G., & Ocampo-García, B. E. (2014). Multifunctional radiolabeled nanoparticles for targeted therapy. Current Medicinal Chemistry, 21(1), 124–138.

Mendoza-Nava, H., Ferro-Flores, G., Ramírez, F. M., Ocampo-García, B., & et al. (2017). Fluorescent, plasmonic, and radiotherapeutic properties of the 177Lu–dendrimer-AuNP–folate–bombesin nanoprobe located inside cancer cells. Molecular Imaging, 16, 1536012117704768.

Helmi Ayari | Data processing | Research Excellence Award

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Mr. Helmi Ayari | Data processing | Research Excellence Award

Université Ibn khaldoun | Tunisia

Helmi Ayari is a doctoral researcher in Artificial Intelligence at the École Polytechnique de Tunisie, focusing on machine learning, deep learning, and intelligent imaging systems, with a research track record that includes 3 published documents, an h-index of 1, and citations from 33 documents. He holds a Master of Research in Intelligent Systems for Imaging and Computer Vision and a fundamental Bachelor’s degree in Computer Science. His work includes contributions to medical image analysis, explainable AI, and optimization techniques, with notable publications such as a comparative study of classical versus deep learning-based computer-aided diagnosis systems published in Knowledge and Information Systems (Q2), and a study integrating genetic algorithms with ensemble learning for enhanced credit scoring presented at the International Conference on Business Information Systems (Class B). Professionally, he has served as a Maître Assistant and teaching assistant, delivering courses in machine learning, deep learning, Python, R, computer architecture, and automata theory, while supervising Master’s research, coordinating academic programs, and contributing to hackathons and university events. His research interests span AI-driven decision systems, interpretable machine learning, evolutionary optimization, and applied deep learning. Recognized for both academic and pedagogical engagement, he continues advancing impactful AI research and education.

Profile : Scopus

Featured Publications

Computer-Aided Diagnosis Systems: A Comparative Study of Classical Machine Learning Versus Deep Learning-Based Approaches. Knowledge and Information Systems, published May 23, 2023. https://doi.org/10.1007/s10115-023-01894-7

Integrating Genetic Algorithms and Ensemble Learning for Improved and Transparent Credit Scoring. International Conference on Business Information Systems, June 25, 2025. (Class B)

Chang Liu | Digital Data | Research Excellence Award

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Mr. Chang Liu | Digital Data | Research Excellence Award

 Xi ’ an University of Posts & Telecommunications Shaanxi | China

Mr. Chang Liu is a researcher specializing in data analysis and its application in the digital economy, enterprise value creation, and policy evaluation. He holds advanced degrees in economics and finance, and has contributed extensively to academic research, national-level projects, and provincial research initiatives. His expertise encompasses the full data analysis workflow, including data collection, cleaning, modeling, and empirical verification, with proficiency in Python, STATA, and SPSS. He has co-authored multiple SSCI and PKU Core papers focusing on digital finance, enterprise digital transformation, ESG impact, and the role of data elements in promoting new quality productive forces. In addition, he has led key data analysis tasks in projects evaluating corporate performance, large model spillovers, digital asset accounting, and the internal transmission mechanism of the digital economy. His research integrates advanced econometric methods, spatial models, panel regressions, and complex system coordination frameworks to ensure robust and scientifically reliable conclusions. Through practical work, including enterprise audits and data asset evaluations, he has applied analytical techniques to real-world problems, producing actionable insights. Mr. Liu’s work demonstrates a commitment to precision, academic rigor, and the advancement of data-driven research, contributing significantly to both theoretical understanding and practical application in the field of digital economy and enterprise innovation.

Profile : Orcid

Featured Publication

How data elements fuel new quality productive forces in enterprises: A perspective from digital finance, 2026

Rozana Ghandy | Digital Data | Best Researcher Award

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Dr. Rozana Ghandy | Digital Data | Best Researcher Award

 Islamic Azad University, Tehran | Iran

Dr. Rozana Ghandy is an academic researcher in physical oceanography, pursuing doctoral studies at the Science and Research Branch of Islamic Azad University, where her work focuses on analyzing heat fluxes over the North Indian Ocean and the Persian Gulf using advanced weather reanalysis datasets. She holds a master’s degree in oceanography from Islamic Azad University, North Tehran Branch, where she investigated geostrophic currents through field measurements and satellite observations, and a bachelor’s degree in science from Isfahan University. With extensive teaching experience across multiple educational levels—including physics instruction at pre-university, high-school, and laboratory settings—she has contributed significantly to academic development and educational leadership. Her research interests include air–sea interactions, ocean circulation, climate variability, regional sea dynamics, and the application of remote sensing in marine studies. She has presented and published studies on physical parameters in coastal regions, surface flux behavior, and standardized approaches to air–sea heat flux assessment across key marine basins. Her academic achievements include competitive rankings in national entrance examinations and scholarships recognizing her scientific potential. Overall, her work reflects a dedication to advancing understanding of ocean–atmosphere processes and contributing to the broader field of marine and climate science.

Profile : Orcid

Featured Publication

Study of surface fluxes over the Northern Indian Ocean Seas, 2023

Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

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Prof. Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

SUNY Upstate Medical University | United States

Prof. Bardia Rodd is an Associate Professor at SUNY Upstate Medical University and Associate Director of AI Innovation at the AI for Health Equity, Analytics, and Diagnostics (AHEAD) Center. He completed dual PhDs in Electrical & Computer Engineering from Université Laval and in Computer Science from the University of Malaya, alongside a postdoctoral fellowship at the University of Pennsylvania Perelman School of Medicine, focusing on precision medicine, artificial intelligence, and image-guided systems. His academic career spans faculty positions at SUNY Upstate, the University of Maryland, and Laval University, with extensive experience in biocomputational engineering, machine learning, medical image analysis, and data-driven healthcare solutions. He has led numerous grants, including initiatives in AI in education, biomedical research, and curriculum development. Prof. Rodd has supervised multiple graduate and postgraduate students and contributed to open educational resources, advancing accessible teaching in machine learning and bioengineering. He is an active member of professional societies including IEEE, SPIE, and the Association of Pathology Informatics, serving on multiple technical committees and leadership roles. His research interests include AI-driven biomedical imaging, predictive modeling, and health equity applications. Prof. Rodd has authored a textbook on machine learning for data analysis and continues to integrate research, teaching, and innovation to advance computational medicine and AI education.

Profile : Scopus

Featured Publications

Siezen, H., Awasthi, N., Rad, M. S., Ma, L., & Rodd, B. (2025). “Low-rank distribution embedding of dynamic thermographic data for breast cancer detection.” Journal of Thermal Biology, 104303.

Usamentiaga, R., Fidanza, A., Yousefi, B., Iacutone, G., Logroscino, G., & Sfarra, S. (2025). “Advancing knee injury prevention and anomaly detection in rugby players through automated processing of infrared thermography: A novel biothermodynamics approach.” Thermal Science and Engineering Progress, 103782.

Rad, M. S., Huang, J. V., Hosseini, M. M., Choudhary, R., Siezen, H., Akabari, R., et al. (2025). “Deep learning for digital pathology: A critical overview of methodological framework.” Journal of Pathology Informatics, 100514.

Yousefi, B., Khansari, M., Trask, R., Tallon, P., Carino, C., Afrasiyabi, A., Kundra, V., Ma, L., Ren, L., Farahani, K., & Hershman, M. (2025). “Measuring subtle HD data representation and multimodal imaging phenotype embedding for precision medicine.” IEEE Transactions on Instrumentation and Measurement, TIM-24-01821.

Cao, Y., Sutera, P., Mendes, W. S., Yousefi, B., Hrinivich, T., Deek, M., et al. (2024). “Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.” Radiotherapy and Oncology, 199, 110443.