Ali Razban | Artificial Intelligence | Best Researcher Award

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Best Researcher Award

Ali Razban — Purdue University

Ali Razban
Affiliation Purdue University
Country United States
Scopus ID 57202511592
Documents 41
Citations 914
h-index 15
Subject Area Artificial Intelligence
Event The Scientist Global Awards
ORCID 0000-0002-7794-5761

The Best Researcher Award recognizes distinguished scholarly achievement, research productivity, and measurable scientific impact within a specialized academic field. Ali Razban of Purdue University has established a notable research profile in Artificial Intelligence through scholarly publications, interdisciplinary collaborations, and contributions to data-driven computational methodologies. His research output, citation performance, and academic influence provide objective indicators frequently considered in international research recognition programs.[1][2]

Abstract

This academic recognition article presents a scholarly overview of Ali Razban and evaluates his research achievements in Artificial Intelligence. The article summarizes bibliometric indicators, research productivity, publication record, scientific influence, and relevance to international research awards. The assessment follows a neutral academic framework emphasizing measurable scholarly contributions and documented research impact.[1]

Keywords

Artificial Intelligence, Machine Learning, Computational Intelligence, Data Science, Predictive Analytics, Research Excellence, Scientific Impact, Academic Recognition, Citation Analysis, Best Researcher Award.

Introduction

The growing influence of Artificial Intelligence across scientific, industrial, and societal domains has increased the significance of researchers who contribute innovative methodologies and evidence-based solutions. Academic awards provide structured mechanisms for recognizing researchers whose scholarly activities advance knowledge and generate measurable impact. Ali Razban’s research portfolio reflects sustained engagement with computational technologies and interdisciplinary applications within Artificial Intelligence and related analytical disciplines.[1][3]

Research Profile

Ali Razban is affiliated with Purdue University and has developed a scholarly profile characterized by peer-reviewed research publications, interdisciplinary investigations, and contributions to Artificial Intelligence. According to available bibliometric indicators, his publication record includes 41 indexed documents, supported by 914 citations and an h-index of 15. These indicators reflect both research productivity and sustained scholarly influence within his field.[1]

Research Contributions

The research activities of Ali Razban demonstrate engagement with Artificial Intelligence methodologies that support predictive modeling, intelligent decision-making systems, data analytics, and computational problem-solving. His scholarly work contributes to the broader advancement of AI-driven approaches that facilitate improved efficiency, accuracy, and scalability across diverse application environments.[3]

Publications

The publication portfolio of Ali Razban includes peer-reviewed journal articles, conference proceedings, and collaborative research outputs. Such publications contribute to scientific communication and facilitate dissemination of Artificial Intelligence knowledge across academic and professional communities.[1]

Research Impact

Research impact is frequently measured through bibliometric indicators, citation performance, publication visibility, and evidence of scholarly adoption. With 914 citations and an h-index of 15, Ali Razban demonstrates measurable scientific influence that extends beyond publication counts alone. Citation activity suggests that his research outputs have contributed to ongoing academic discussions and subsequent investigations within related areas of Artificial Intelligence.[1]

Award Suitability

The Best Researcher Award recognizes excellence in scientific achievement, scholarly productivity, innovation, and research influence. Based on available bibliometric indicators and documented academic output, Ali Razban demonstrates several attributes frequently associated with research distinction, including an established publication record, notable citation performance, interdisciplinary engagement, and contributions to Artificial Intelligence research.[1][2]

Conclusion

Ali Razban has established a recognized academic profile within the field of Artificial Intelligence through scholarly publications, measurable citation impact, and sustained research activity. His research metrics and documented contributions provide evidence of academic engagement and influence that align with commonly accepted indicators of research excellence. The profile presented in this article supports consideration for recognition within international scientific award frameworks.[1][2]

References

  1. Elsevier. (n.d.). Scopus author details: Ali Razban, Author ID 57202511592. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57202511592
  2. The Scientist Global Awards. (n.d.). International research recognition and academic excellence awards.
    https://thescientists.net/
  3. Journal of Building Engineering. (2025). A review of occupancy detection techniques for HVAC control: Advances and practical challenges.
    https://doi.org/10.1016/j.jobe.2025.113962

Sultan Ahmad | Artificial Intelligence | Best Researcher Award

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Best Researcher Award

Sultan Ahmad
Prince Sattam bin Abdulaziz University
Sultan Ahmad
Affiliation Prince Sattam bin Abdulaziz University
Country Saudi Arabia
Scopus ID 57194429140
Documents 150
Citations 2389
h-index 27
Subject Area Artificial Intelligence
Event The Scientist Global Awards
ORCID 0000-0002-3198-7974

Sultan Ahmad is affiliated with Prince Sattam bin Abdulaziz University in Saudi Arabia and has established a notable research profile in the field of Artificial Intelligence. His scholarly contributions encompass interdisciplinary applications of intelligent systems, computational methodologies, and data-driven technologies. The academic recognition associated with the Best Researcher Award reflects sustained research productivity, international visibility, and measurable scholarly influence demonstrated through publications, citations, and collaborative scientific engagement.[1] The evaluation criteria for the award emphasize scientific quality, publication consistency, research impact, and contribution to the advancement of contemporary Artificial Intelligence research.[2]

Abstract

The Best Researcher Award recognizes academic excellence, sustained scholarly productivity, and impactful scientific contributions within the field of Artificial Intelligence. Sultan Ahmad has demonstrated a substantial record of publication activity and citation performance, supported by a recognized international research profile. His work reflects engagement with advanced computational methodologies, intelligent systems, and interdisciplinary innovation in Artificial Intelligence research.[1] The award evaluation framework considers research visibility, scholarly influence, citation metrics, and the broader significance of contributions to scientific and technological advancement.[2]

Keywords

Artificial Intelligence, Intelligent Systems, Machine Learning, Computational Research, Data Analytics, Scientific Publications, Citation Impact, Academic Excellence, Research Recognition, Interdisciplinary Innovation

Introduction

The field of Artificial Intelligence has experienced significant expansion in recent decades due to rapid advancements in computational infrastructure, algorithmic development, and data-driven technologies. Researchers in this domain contribute to diverse applications including automation, predictive analytics, intelligent decision-making, and human-computer interaction. Academic awards in this area are intended to recognize researchers whose work demonstrates measurable scholarly influence and long-term scientific value.[3]

Research Profile

The research profile of Sultan Ahmad demonstrates interdisciplinary engagement in Artificial Intelligence with a focus on computational methods, intelligent systems, and applied analytical frameworks. His scholarly output includes peer-reviewed publications indexed within internationally recognized databases. According to Scopus author metrics, the researcher has produced 150 indexed documents and accumulated 2389 citations, resulting in an h-index of 27.[1]

Research Contributions

Research contributions associated with Sultan Ahmad include scholarly investigations related to intelligent computation, data processing methodologies, and Artificial Intelligence-based analytical systems. Such contributions are significant within contemporary research environments where computational intelligence supports scientific modeling, automation, and predictive technologies.[3]

Publications

The publication record associated with Sultan Ahmad reflects sustained academic productivity and contribution to internationally indexed scientific literature. Publication activity in Artificial Intelligence commonly involves interdisciplinary collaboration, computational experimentation, and theoretical development supported by peer review processes.[1]. Representative DOI references relevant to Artificial Intelligence research and computational studies include internationally accessible digital identifiers that facilitate long-term scholarly retrieval and citation tracking.[5]

Research Impact

Research impact is commonly evaluated through citation analysis, publication indexing, scholarly visibility, and interdisciplinary relevance. The citation record of Sultan Ahmad demonstrates measurable influence within the research community and indicates that published work has contributed to ongoing scientific discussions and technological development.[1] An h-index of 27 reflects a balanced combination of publication productivity and citation performance. Such indicators are frequently used in academic assessment processes to evaluate long-term scientific contribution and the broader dissemination of research findings within international scholarly networks.[6]

Award Suitability

The Best Researcher Award emphasizes scientific quality, measurable research performance, publication consistency, and contribution to the advancement of knowledge. Sultan Ahmad’s research metrics and scholarly record demonstrate alignment with these evaluation principles. His publication output, citation influence, and academic engagement collectively support recognition within the context of international scientific achievement.[2]

Conclusion

Sultan Ahmad has established a recognized academic profile through sustained research activity in Artificial Intelligence and related computational disciplines. The documented publication record, citation performance, and scholarly visibility demonstrate ongoing contribution to scientific advancement and interdisciplinary technological research. The Best Researcher Award represents an acknowledgment of these achievements within an international academic framework dedicated to recognizing research excellence and scientific impact.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Sultan Ahmad, Author ID 57194429140. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57194429140
  2. The Scientist Global Awards. (n.d.). Research recognition and academic excellence award framework.
    https://thescientists.net/
  3. Measurement. (2026). An enhanced control strategy using ANFIS-FOPID for grid-tied DFIG based WECS with battery storage for better Sustainable Urban Environments. https://doi.org/10.1016/j.measurement.2025.120145
  4. Frontiers in Medicine. (2026). A scalable and reliable deep learning framework for enhanced brain tumor detection and diagnosis using AI-based medical imaging. https://doi.org/10.3389/fmed.2026.1738796
  5. Journal of Advances in Information Technology. (2026). PolyVision: Optimising Retinal Disease Detection Through Collaborative Neural Networks. https://doi.org/10.12720/jait.17.1.55-64
  6. Blockchain, Artificial Intelligence, and Future Research (2026). Can Generative AI Be a Solution or a Threat to Creative Industry Professionals? Assessing Readiness with the Rasch Model. https://doi.org/10.70211/bafr.v2i1.410

 

Jamal Zraqou | Machine Learning | Research Excellence Award

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Assoc. Prof. Dr. Jamal Zraqou | Machine Learning | Research Excellence Award

Associate Professor | University of Petra | Jordan

Assoc. Prof. Dr. Jamal S. Zraqou is an active researcher with demonstrated contributions across data-driven engineering, machine learning, cybersecurity, and digital transformation. He has authored 45 scholarly documents indexed in Scopus, accumulating 202 citations with an h-index of 9, reflecting consistent academic impact. His recent work addresses optimization techniques for engineering design, advanced machine learning methods for phishing detection, cybersecurity vulnerability analysis, and the strategic role of business intelligence in digital transformation. Dr. Zraqou has collaborated with a broad international network of over 60 co-authors, highlighting interdisciplinary and cross-sector engagement. His research supports practical problem-solving in engineering systems, information security, and decision intelligence, contributing to improved technological resilience, safer digital environments, and enhanced organizational competitiveness at societal and industrial levels.

Citation Metrics (Scopus)

202
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202

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45

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9

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View Google Scholar Profile
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 View ORCID Profile

Featured Publications

Nurhadhinah Nadiah Ridzuan | Artificial Intelligence | Research Excellence Award

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Ms. Nurhadhinah Nadiah Ridzuan | Artificial Intelligence | Research Excellence Award

Student | Universiti Brunei Darussalam | Brunei Darrussalam

Ms. Nurhadhinah Nadiah Ridzuan is a researcher at Universiti Brunei Darussalam specializing in artificial intelligence applications in the financial sector, with a strong focus on regulation, ethics, and governance. Her research examines the balance between technological innovation and responsible financial practices, particularly in FinTech ecosystems and regulatory sandboxes. She has authored multiple peer-reviewed publications, accumulating 141 citations, with an h-index of 3 and an i10-index of 2. Her highly cited 2024 work on AI, regulation, and ethical responsibility reflects significant scholarly impact. Ridzuan actively collaborates with international researchers across finance, digital governance, and Industry 4.0 studies. Her work contributes to policy-relevant insights that support ethical AI adoption, inclusive financial systems, and sustainable digital transformation in emerging and global financial markets.

Citation Metrics (Google Scholar)

141
100
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0

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141

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3

i10-index

2

Citations

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i10-index

View Google Scholar Profile
 View Scopus Profile View ResearchGate Profile

Featured Publications


Modelling Individual Performance in Industry 4.0 with Artificial Intelligence and Organisational Strategies in the Financial Sector

– In Multi-Industry Digitalization and Technological Governance in the AI Era (2025). | Citations: 2


Exploratory Study of the FinTech Regulative Sandbox: Opportunities and Challenges

– In Promoting Inclusivity and Accessibility with FinTech (2026).

Francesco Inchingolo | Artificial Intelligence | Research Excellence Award

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Prof. Dr. Francesco Inchingolo | Artificial Intelligence | Research Excellence Award

Professor in Odontostomatological Diseases (Scientific Sector MED/28) | University of Bari “Aldo Moro” – University Hospital “Policlinico di Bari” | Italy

Prof. Dr. Francesco Inchingolo is a leading Italian clinician-scientist in oral and maxillofacial sciences, renowned for his multidisciplinary contributions spanning dentistry, oral surgery, orthodontics, implantology, regenerative medicine, and public health. A Full Professor of Odontostomatological Diseases and long-standing director of major specialization programs, he has significantly advanced clinical training and translational research at the University of Bari “Aldo Moro.” His global scholarly impact is exemplified by 12,137 citations from 6,038 documents, 489 publications, and an h-index of 66, demonstrating sustained excellence and international recognition. A Principal Investigator in multiple funded projects, he has driven innovation in stem-cell applications, platelet-derived biomaterials, piezosurgery, bone regeneration, orthodontic biomechanics, geriatric dentistry, pediatric oral care, and complex maxillofacial pathologies. His extensive editorial and reviewer roles, together with collaborations across Europe, Asia, and the United States, emphasize his position as a central figure in global dental research networks. Prof. Inchingolo has delivered numerous invited lectures worldwide and serves as Visiting Professor in several international institutions, strengthening academic exchanges and capacity building. His work has been recognized through an exceptional series of national and international distinctions—including the prestigious Sant’Apollonia Award, multiple CDUO, SIDO, and SIOH honors, international research incentive awards, and cultural and scientific excellence recognitions such as the Carthage 2.0 Prize, “Tribute to Life,” and best research poster awards across diverse dental disciplines. Through his high-impact publications, clinical innovations, and leadership in advanced oral surgery and implantology programs, he has contributed substantially to improving patient outcomes, advancing therapeutic technologies, and shaping modern dental and maxillofacial practice on a global scale.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

Ahmed Hamza Osman Ahmed | Data Science | Best Researcher Award

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Prof. Dr. Ahmed Hamza Osman Ahmed | Data Science | Best Researcher Award

Professor of Computer Science | King Abdulaziz University | Saudi Arabia

Prof. Dr. Ahmed Hamza Osman Ahmed is a distinguished computer scientist and cybersecurity expert whose research bridges artificial intelligence, information security, and data privacy. With over 70 peer-reviewed publications in prestigious journals such as IEEE, Elsevier, and Springer, his scholarly impact is evidenced by 709 citations across 658 documents and an h-index of 14, underscoring his significant contributions to the field. His research encompasses AI-driven cybersecurity systems, intrusion detection, digital forensics, and blockchain-based data integrity, with several funded projects advancing intelligent threat prediction and misinformation detection. Prof. Ahmed has played a pivotal role in developing ABET-aligned curricula, integrating machine learning into cybersecurity education, and supervising more than 25 postgraduate theses in cybersecurity and data science. Internationally recognized for academic excellence, he has received awards such as the Gold Medal at PECIPTA 2011 and Best Postgraduate Student at Universiti Teknologi Malaysia. His extensive collaborations across Saudi Arabia, Malaysia, and Sudan reflect his commitment to fostering global research partnerships and advancing secure, AI-empowered digital ecosystems. Through his leadership in teaching, research, and academic service, Prof. Ahmed continues to contribute to shaping the future of cybersecurity and artificial intelligence with profound educational and societal impact.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Elssied, N. O. F., Ibrahim, O., & Osman, A. H. (2014). A novel feature selection based on one-way ANOVA F-test for e-mail spam classification. Research Journal of Applied Sciences, Engineering and Technology, 7(3), 625–638. Citations: 223

  2. Elhadi, A. A. E., Maarof, M. A., & Osman, A. H. (2012). Malware detection based on hybrid signature behaviour application programming interface call graph. American Journal of Applied Sciences, 9(3), 283–293. Citations: 137

  3. Osman, A. H., Salim, N., Binwahlan, M. S., Alteeb, R., & Abuobieda, A. (2012). An improved plagiarism detection scheme based on semantic role labeling. Applied Soft Computing, 12(5), 1493–1502. Citations: 128

  4. Osman, A. H., & Aljahdali, H. M. (2020). An effective ensemble boosting learning method for breast cancer virtual screening using neural network model. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2976149 Citations: 93

  5. Abuobieda, A., Salim, N., Albaham, A. T., Osman, A. H., & Kumar, Y. J. (2012). Text summarization features selection method using pseudo genetic-based model. In Proceedings of the 2012 International Conference on Information Retrieval & Knowledge Management (pp. 84–89). Citations: 84

Nur Intan Raihana Ruhaiyem | Machine Learning | Best Researcher Award

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Dr. Nur Intan Raihana Ruhaiyem | Machine Learning | Best Researcher Award

Senior Lecturer | Universiti Sains Malaysia | Malaysia

Dr. Nur Intan Raihana Ruhaiyem is a highly accomplished researcher and Senior Lecturer at the School of Computer Sciences, Universiti Sains Malaysia, with notable expertise in computational biology, image processing, data visualization, and artificial intelligence applications. Her research spans deep learning, computer vision, and biomedical informatics, focusing on developing intelligent systems that enhance healthcare diagnostics, cultural heritage preservation, and data-driven decision-making. She has authored over 50 scholarly publications in reputable international journals and conferences, including IEEE Access, Biomedical Signal Processing and Control, Intelligence-Based Medicine, Diagnostics (Basel), Image and Vision Computing, and Scientific Reports. Her works have collectively garnered more than 230 citations and an h-index of 7, underscoring her growing impact in the computational and data science research community. Recent contributions such as the development of Mamba-based UNet architectures for medical image segmentation and hybrid restoration models for historical murals reflect her capacity to integrate advanced AI models into multidisciplinary domains. Dr. Ruhaiyem’s collaborative research extends internationally, with partnerships involving scholars from Australia, China, and the broader ASEAN region. Her role as a technical committee member for several prominent conferences—such as the International Visual Informatics Conference and Soft Computing in Data Science—demonstrates her leadership in promoting innovation and research excellence in data science and visual analytics. A Certified Professional Trainer recognized by Malaysia’s Human Resources Development Fund, she has also played a key role in professional education, serving as a lead instructor for national Data Science Certification programs. Through her research, mentorship, and active academic engagement, Dr. Ruhaiyem contributes significantly to advancing digital transformation, fostering analytical literacy, and bridging computational intelligence with societal needs.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

1. Younis, H. A., Ruhaiyem, N. I. R., Ghaban, W., Gazem, N. A., & Nasser, M. (2023). A systematic literature review on the applications of robots and natural language processing in education. Electronics, 12(13), 2864. Citations: 75

2. Salisu, S., Ruhaiyem, N. I. R., Eisa, T. A. E., Nasser, M., Saeed, F., & Younis, H. A. (2023). Motion capture technologies for ergonomics: A systematic literature review. Diagnostics, 13(15), 2593. Citations: 63

3. Goni, M. R., Ruhaiyem, N. I. R., Mustapha, M., Achuthan, A., & Nassir, C. M. N. C. M. (2022). Brain vessel segmentation using deep learning—A review. IEEE Access, 10, 111322–111336. Citations: 42

4. Yang, J., & Ruhaiyem, N. I. R. (2024). Review of deep learning-based image inpainting techniques. IEEE Access, 12, 138441–138482. Citations: 17

5. Younis, H. A., Ruhaiyem, N. I. R., Badr, A. A., Abdul-Hassan, A. K., Alfadli, I. M., & others. (2023). Multimodal age and gender estimation for adaptive human-robot interaction: A systematic literature review. Processes, 11(5), 1488. Citations: 16

Saifullah Khalid | Artificial Intelligence | Innovative Research Award

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Dr. Saifullah Khalid | Artificial Intelligence | Innovative Research Award

Principal Scientist | IBMM RESEARCH | Sudan

Dr. Saifullah Khalid is a distinguished aviation and aerospace researcher renowned for his groundbreaking work in AI-driven aviation systems, air traffic management optimization, and unmanned aerial systems. With dual PhDs in engineering and a career spanning advanced aeronautical research, he serves as Principal Scientist at IBMM Research, Sudan. His academic background includes a PhD in Electronics and Communication Engineering from SN University, India (2013). Dr. Khalid’s expertise encompasses autonomous UAV systems, quantum-inspired optimization algorithms, sustainable aviation power systems, and digital tower operations. He has authored over 270 publications, including 38 Web of Science-indexed papers, and holds an impressive portfolio of 85 patents—50 as sole inventor—setting two world records for patent achievements. His professional experience extends to teaching and mentoring, supervising PhD candidates in AI-based air route optimization and guiding over 200 engineering projects. A committed academic leader, he has developed ICAO-compliant curricula and serves as Vice Chairman of the Academic Council Asia at NextGen University International. His awards include world records for patent excellence and international recognition for research innovation. His technical skills span MATLAB/Simulink, Python, UAV system design, and AI applications in aviation. 305 Citations; 67 Documents; h-index: 10

Profiles: Google scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Khalid, S., & Dwivedi, B. (2011). Power quality issues, problems, standards & their effects in industry with corrective means. International Journal of Advances in Engineering & Technology, 1(2), 1–11. Citations: 167

  2. Nishad, D. K., Tiwari, A. N., Khalid, S., Gupta, S., & Shukla, A. (2024). AI-based UPQC control technique for power quality optimization of railway transportation systems. Scientific Reports, 14(1), 17935. Citations: 31

  3. Khalid, S. (2018). Performance evaluation of Adaptive Tabu Search and Genetic Algorithm optimized shunt active power filter using neural network control for aircraft power utility of 400 Hz. Journal of Electrical Systems and Information Technology, 5(3), 723–734. Citations: 30

  4. Khalid, S., Dwivedi, B., Kumar, N., & Agrawal, N. (2007). A review of state-of-art techniques in active power filters and reactive power compensation. National Journal of Technology, 3(1), 10–18. Citations: 26

  5. Khalid, S., & Dwivedi, B. (2010). Power quality: An important aspect. International Journal of Engineering Science and Technology, 2(11), 6485–6490. Citations: 25

Rong Wang | Artificial Intelligence | Best Researcher Award

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Mrs. Rong Wang | Artificial Intelligence | Best Researcher Award

Postdoc | University of Tuebingen | Germany

Mrs. Rong Wang is a postdoctoral researcher at the Eberhard Karls University of Tübingen, Germany, specializing in computational linguistics and the evaluation and optimization of large language models (LLMs). She holds an M.Sc. in Computational Linguistics (NLP) from the University of Stuttgart (Grade: 1.7, 2024) and a Ph.D. in Digital Humanities from Zhejiang University, China (2016). Her interdisciplinary academic background bridges computer science, linguistics, and AI-driven humanities research, reflecting her ability to apply quantitative and symbolic methods to linguistic and cognitive studies. Professionally, she has served as a Postdoctoral Fellow at the University of Tübingen, AI Engineer at Telus International Digital AI, AGI Engineer Intern at Deepseek AI, Data Scientist at DEKRA GmbH, and Assistant Professor of Linguistics at Hangzhou Dianzi University. Her research focuses on language model evaluation metrics, neural-symbolic reasoning, multimodal semantics, and automated linguistic assessment. She has contributed to projects on enhancing spatial reasoning in LLMs, multi-agent AI systems, and personality recognition models, alongside authoring several publications on machine learning applications in cognitive linguistics and NLP evaluation. Technically proficient in Python, R, JavaScript, and SQL, she is experienced with frameworks such as LangChain, Autogen, Hugging Face, and PyTorch, and cloud platforms including Azure ML and AWS SageMaker. Her certifications include Azure Certified Data Scientist Associate and AWS Certified AI Practitioner. Mrs. Wang is fluent in English, German, and Chinese, with working knowledge of Japanese, and is recognized for her strong teamwork, communication, and leadership abilities. Her recent works have appeared in Data Intelligence, Psychology Methods, and TMLR, demonstrating her innovative contributions to the AI and NLP research community. (0 Citations ; 2 Documents ; 0 h-index.)

Profiles: Scopus | ResearchGate

Featured Publications

Wang, R., Sun, K., & Kuhn, J. (2024, Dec). Dspy-based neural-symbolic pipeline to enhance spatial reasoning in LLMs [Preprint]. arXiv. https://arxiv.org/abs/2411.18564

Wang, R., Sun, K., & Kuhn, J. (2024, Nov). A pipeline of neural-symbolic integration to enhance spatial reasoning in large language models [Preprint]. arXiv. https://arxiv.org/abs/2411.18564

Sun, K., & Wang, R. (2024, Oct). The roles of contextual semantic relevance metrics in human visual processing [Preprint]. arXiv. https://arxiv.org/abs/2410.09921

Wang, R., & Sun, K. (2024, Jul). A novel dependency framework for enhancing discourse data analysis [Preprint]. arXiv. https://arxiv.org/abs/2407.12473

Wang, R., & Sun, K. (2024, Jun). Continuous output personality detection models via mixed strategy training [Article]. arXiv. https://arxiv.org/abs/2406.16223

Feng Mao | Cognitive Science | Best Researcher Award

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Assist. Prof. Dr. Feng Mao | Cognitive Science | Best Researcher Award

Associate Professor | Shanghai University of International Business and Economics | China

Assist. Prof. Dr. Feng Mao is a distinguished Associate Professor and Senior Translator at Shanghai University of International Business and Economics with 28 years of higher education experience, specializing in country and area studies, translation studies, and foreign language education. He is currently pursuing a Ph.D. at Shanghai International Studies University (since 2022) and serves as a Master’s supervisor for MA in Linguistics and MTI programs. Over his career, he has led 11 research projects, including a national-level Social Science Fund project, and collaborated with international scholars from Singapore, Canada, the UK, and Germany, reflecting his strong global research network. His professional experience includes mentoring MA students, peer reviewing for SSCI and AHCI journals, editorial committee service, and contributions to national professional assessments, including CATTI examination grading. His research interests focus on translation and interpreting studies, foreign language education and policy, cross-cultural communication, applied linguistics, audiovisual translation, and country and area studies. MAO Feng has developed advanced research skills in big data analysis for textbook and material compilation, literary analysis, audiovisual translation methods, and educational program evaluation, supporting both theoretical and applied projects. He has authored 64 academic publications, including 20 SSCI/AHCI journal papers with 5 in Q1 journals, and published 5 academic monographs and translations totaling over 2 million words, alongside textbooks and review articles that serve thousands of students. He has also contributed to industry and government consultancy projects such as cultural brand promotion and copyright export research. His awards and honors include national-level recognition for professional degree assessment and leadership roles within the China Association for Educational Linguistics and the Translators Association of China. Overall, MAO Feng’s extensive research, teaching, publications, and international collaborations highlight his exceptional academic leadership and ongoing potential to advance translation studies and foreign language education globally. 7 Citations, 8 Documents, h-index 2.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

Mao, F., & Yang, X. (2024). Literary therapy based on positive psychology: Impact on college students’ happiness. Journal of Poetry Therapy, 37(1), 16–34. https://doi.org/[insert DOI] (Citations: 5)

Mao, F., & Liu, S. (2024). Book review: Networked feminism: How digital media makers transformed gender justice movements (R. Clark-Parsons, California, University of California Press, 2022). Feminist Media Studies, 24(2), 404–406. (Citations: 3)

Feng, M., Wenhui, L., Xinle, Y., & Biyu, W. (2022). Romantic narrative in the film The Battle at Lake Changjin. International Journal of English and Comparative Literary Studies, 3(1), 19–27. (Citations: 3)

Feng, M., Quan, L., & Wu, B. (2021). A review on the compilation of college English textbooks in China based on big data. Sino-US English Teaching, 18(3), 60–65. (Citations: 3)

Feng, M., Quan, L., & Biyu, W. (2021). Exploration of the compilation of English learning materials for Chinese college students based on big data under the guidance of complex dynamic theory. International Journal of Linguistics, Literature and Translation, 4(3), 22–32. (Citations: 2)