Aytac Aydın | Data Science | Best Researcher Award

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Assoc. Prof. Dr. Aytac Aydın | Data Science
| Best Researcher Award

Karadeniz Technical University | Turkey

Assoc. Prof. Dr. Aytac Aydın Research contributions focus on advancing quality management and performance appraisal within the forest products and wood-based materials industry through the systematic application of statistical analysis and artificial intelligence techniques. The work integrates process optimization, quality control modeling, and ergonomics to enhance productivity, material efficiency, and workplace safety across manufacturing environments. Multiple completed and ongoing research projects have addressed critical challenges in the evaluation of production performance, defect reduction, operational efficiency, and sustainable resource utilization. Innovative methodologies, including multi-criteria decision-making models, predictive analytics, and machine learning algorithms, have been applied to improve decision accuracy in material selection, process planning, and quality inspection. Published studies in SCI-indexed journals contribute valuable data-driven insights to both academia and industry, strengthening standards in forest industrial engineering and promoting evidence-based operational strategies. Research outcomes support environmentally responsible production while increasing economic efficiency in wood-based manufacturing systems. In addition, contributions extend to guiding postgraduate and doctoral-level research focused on quality systems, process improvement, and industrial performance metrics. These efforts collectively strengthen the scientific foundation of forest industry management, drive technological advancement, and support sustainable and intelligent manufacturing practices with a long-term impact on both regional and international forestry-based production sectors.

 Profile: Orcid | Scopus 

Featured Publications

Aydın, A., Temel, B. A., Semercioğlu, İ. N., Başağa, H. B., Toğan, V., & Ağcakoca, E. (2025). Evaluating performance appraisal effects on employee motivation and productivity: Insights from the Turkish construction industry via covariance-based structural equation modeling. Buildings. https://doi.org/10.3390/buildings15224040

Tiryaki, S., Aydın, A., & Ondaral, S. (2025). Process monitoring with individual measurements: A case study in corrugated cardboard industry. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi. https://doi.org/10.17474/artvinofd.1722824

Tiryaki, S., & Aydın, A. (2024). Orta yoğunlukta liflevha üretiminde çekme direncinin iki aşamalı izlenmesi. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi. https://doi.org/10.58816/duzceod.1563540

Aydın, A., & Nemli, G. (2023). Yonga levha endüstrisinde zımparalama sorunlarının ve çözüm önerilerinin belirlenmesine yönelik bir çalışma. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi. https://doi.org/10.58816/duzceod.1394936

Shahina Begum | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Shahina Begum | Artificial Intelligence
| Best Researcher Award

Prof. Dr. Shahina Begum | Mallardalen University | Sweden

Prof. Dr. Shahina Begum, a Swedish national, is a distinguished Professor of Artificial Intelligence at the Artificial Intelligence and Intelligent Systems Research Group, Mälardalen University, Sweden, where she has been serving since 2019 after progressing through roles as Senior Lecturer  and Postdoctoral Researcher . She holds a Ph.D. ,Licentiate (2009), and Docent (2015) in Artificial Intelligence from Mälardalen University, as well as an M.Sc. in Computer Science (Intelligent Systems) from Dalarna University (2005). With over a decade of teaching and research experience, she has made significant contributions to AI through pioneering work in machine learning, intelligent decision support, and AI applications for health and well-being. Prof. Begum has demonstrated exceptional leadership in academia by securing substantial external research funding, amounting to approximately 224.4 MSEK as Principal Investigator and Co-PI, enabling groundbreaking multidisciplinary projects bridging AI with real-world applications. Her scholarly impact is reflected in her Google Scholar record, with an h-index of 30 and around 3,689 citations, and her Scopus record, with 88 indexed publications, 1,497 citations from 1,292 documents, and an h-index of 18. A passionate educator and mentor, she continues to inspire the next generation of AI researchers while actively contributing to the global AI research community through publications, collaborations, and leadership in funded projects.

 Profile: Scopus | Google Scholar | Linked IN | Staff Page | Orcid

Featured Publications

Islam, M. R., Ahmed, M. U., Barua, S., & Begum, S. (2022). A systematic review of explainable artificial intelligence in terms of different application domains and tasks. Applied Sciences, 12(3), 1353.

Begum, S., Ahmed, M. U., Funk, P., Xiong, N., & Folke, M. (2010). Case-based reasoning systems in the health sciences: A survey of recent trends and developments. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(2), 241–257.

Barua, S., Ahmed, M. U., Ahlström, C., & Begum, S. (2019). Automatic driver sleepiness detection using EEG, EOG and contextual information. Expert Systems with Applications, 115, 121–135.

Chen, R. Y., Kung, V. L., Das, S., Hossain, M. S., Hibberd, M. C., Guruge, J., … & Begum, S. (2020). Duodenal microbiota in stunted undernourished children with enteropathy. New England Journal of Medicine, 383(4), 321–333.

Begum, S., Ahmed, M. U., Funk, P., Xiong, N., & Von Schéele, B. (2009). A case-based decision support system for individual stress diagnosis using fuzzy similarity matching. Computational Intelligence, 25(3), 180–195.

Degas, A., Islam, M. R., Hurter, C., Barua, S., Rahman, H., Poudel, M., Ruscio, D., … & Begum, S. (2022). A survey on artificial intelligence (AI) and explainable AI in air traffic management: Current trends and development with future research trajectory. Applied Sciences, 12(3), 1295.

Cyrille Feybesse | Data Science | Best Researcher Award

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Dr. Cyrille Feybesse | Data Science | Best Researcher Award

Dr. Cyrille Feybesse | Guillaume Regnier Hospital | France

Dr. Cyrille Feybesse, a French psychologist and Maître de Conférences (MCF, 2022), is a researcher and clinical psychologist specializing in love, creativity, and cross-cultural psychology. He earned his Ph.D. in Psychology (2015) at Université Paris Descartes under the supervision of Professors Geneviève Coudin and Todd Lubart, with additional mentorship from Professor Elaine Hatfield (University of Hawai‘i). His academic background spans clinical, health, and social psychology, with training in France, Portugal, and Brazil. Dr. Feybesse has held postdoctoral fellowships at Université Paris Descartes and the University of Porto, collaborating with the Portuguese Foundation for Science and Technology (FCT) on pioneering research into passionate love and creativity. Since 2022, he has been conducting clinical and research activities at the Centre Hospitalier Guillaume Régnier, Rennes, focusing on child psychiatry, creativity, and high-potential intelligence. He has authored over 14 publications, contributed to multiple book chapters, and presented at numerous international conferences. His work has been cited more than 350 times (Scopus Author ID: 57191835134; ORCID: 0000-0001-7795-568X) with an h-index of 6. Multilingual in English, Portuguese, and Spanish, Dr. Feybesse also serves as Assistant Editor for Interpersona and reviewer for leading journals including Sexuality & Culture and European Psychologist, advancing research at the intersection of love, creativity, and human development.

 Profile: Scopus | Orcid  | Researchgate 

Featured Publications

Feybesse, C., Forthmann, B., Neto, F., Holling, H., & Hatfield, E. (2025). Measuring love around the world: A cross-cultural reliability generalization. Sexuality & Culture. Advance online publication.

Feybesse, C. (2024). Social representation of passionate love among Brazilian and French youngsters. Trends in Psychology. Advance online publication.

Feybesse, C., Fu, S., Lubart, T., Rasa, L., Ossom, C., Cavasino, V., Jacob, J., & Lemonnier, T. (2020). Social representation of fair price among professional photographers. PLOS ONE, 15(12), e0243547.

Feybesse, C. (2018). Assessing passionate love: Italian validation of the PLS (reduced version). Sexual and Relationship Therapy, 33(2), 221–229.

Feybesse, C. (2016). Sensory values in romantic attraction in four European countries: Gender and cross-cultural comparison. Journal of Cross-Cultural Research, 50(2), 109–123.

Hao Zhang | Artificial Intelligence | Best Researcher Award

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Dr. Hao Zhang | Artificial Intelligence
| Best Researcher Award

Associate professor at Carnegie Mellon University, United States.

Hao Zhang is a Research Associate at Carnegie Mellon University (CMU), conducting postdoctoral research at the Safe AI Lab under Prof. Ding Zhao. He also serves as the Associate Director of the ETAIC Research Lab at the University of Texas at Arlington, led by Prof. Eric Tseng (NAE Member). He holds a Ph.D. from Tsinghua University, co-advised by Prof. Zhi Wang and Prof. Shengbo Eben Li. With over 35 SCI/EI publications and 17 patents, his research advances multi-agent reinforcement learning and closed-loop LLMs for real-world AI deployment in autonomous vehicles, robotics, and smart energy systems. He collaborates globally with academic and industrial leaders such as BYD, SAIC, Dongfeng Motor, and UCL, making impactful contributions to intelligent mobility.

🌍 Professional Profile:

ORCID

Google Scholar 

Scopus 

🏆 Suitability for the Best Researcher Award :

Dr. Hao Zhang is an exceptional candidate for the Best Researcher Award due to his groundbreaking work at the intersection of artificial intelligence and real-world applications. His achievements in scalable AI for micro-mobility and autonomous vehicles have led to industrial deployments across leading automotive manufacturers. With a strong publication record, global collaborations, and 17 patents, he exemplifies innovation, impact, and leadership. He bridges theory and practice, pushing the boundaries of safe and trustworthy AI agents. His dual appointments at CMU and UTA and contribution to both academia and industry reflect his versatile excellence. Dr. Zhang’s work not only enhances technological advancement but also fosters a responsible and intelligent future for mobility and energy systems.

🎓 Education :

Hao Zhang received his Ph.D. in Mechanical Engineering from Tsinghua University, one of China’s most prestigious institutions, where he was co-advised by renowned scholars Prof. Zhi Wang and Prof. Shengbo Eben Li. During his Ph.D., he focused on reinforcement learning and its applications to intelligent vehicle systems. Prior to that, he completed his undergraduate and master’s studies with distinction, developing a strong foundation in robotics, automation, and control systems. His education also included collaborative learning experiences with industry, which laid the groundwork for his multidisciplinary approach to research. Currently, he is expanding his expertise through postdoctoral research at Carnegie Mellon University, contributing to the development of safe AI systems under the mentorship of Prof. Ding Zhao.

🏢 Work Experience :

Dr. Zhang has a rich portfolio of academic and industrial experience. As a Research Associate at Carnegie Mellon University, he works at the forefront of AI safety, while simultaneously serving as Associate Director at the ETAIC Lab at UTA. He has led or participated in five major government-funded research projects and four OEM-sponsored industry projects. His efforts have directly supported intelligent system development for companies such as BYD Auto, SAIC Motor, and Dongfeng. His engineering solutions have real-world applications in autonomous driving, energy management, and mobile robotics. His academic roles, coupled with his industrial consultancy, enable him to effectively translate research into practice. Dr. Zhang’s interdisciplinary experience sets him apart as a leader in applied AI and automation.

🏅Awards and Honors

Hao Zhang’s research excellence has earned him notable recognition across academia and industry. He has published over 35 SCI/EI-indexed journal articles, authored a technical book (ISBN: 9780443329845), and holds 17 patents related to intelligent control and autonomous systems. His work has been cited nearly 500 times, demonstrating significant influence. His research contributions have been integrated into industrial platforms at BYD and Dongfeng, marking a rare crossover between lab and large-scale deployment. Although he is still early in his postdoctoral career, his consistent innovation and impact have made him a rising leader in AI-powered mobility. His contributions position him for prestigious honors such as the Best Researcher Award and similar recognitions for scientific leadership.

🔬 Research Focus :

Dr. Zhang’s research focuses on scalable and trustworthy AI for autonomous systems and smart energy applications. His core expertise includes multi-agent reinforcement learning, closed-loop large language models (LLMs), and intelligent motion control. He develops AI algorithms that can be safely deployed in micro-mobility devices (assistive and mobile robots), connected vehicles, and distributed energy platforms. His work contributes to both algorithmic innovation and real-world adoption, ensuring AI agents are reliable, interpretable, and responsive to dynamic environments. He is particularly interested in bridging theory with practice by collaborating with top-tier institutions and OEMs. Dr. Zhang’s interdisciplinary approach merges robotics, automotive systems, control engineering, and deep learning to create adaptive, secure, and energy-efficient intelligent agents.

📊 Publication Top Notes:

📘 Impact of ammonia addition on knock resistance and combustion performance in a gasoline engine with high compression ratio
📅 Year: 2023 | 📊 Cited by: 75 | 🛠️ Energy efficiency, combustion

📘 Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine
📅 Year: 2021 | 📊 Cited by: 42 | ⚡ Hybrid vehicles, control systems

📘 Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles
📅 Year: 2023 | 📊 Cited by: 40 | 📡 Connected vehicles, optimization

📘 Experimental study on combustion and emission characteristics of ethanol-gasoline blends in a high compression ratio SI engine
📅 Year: 2023 | 📊 Cited by: 36 | 🔬 Fuel science, engine performance

📘 Learning-based supervisory control of dual mode engine-based hybrid electric vehicle with reliance on multivariate trip information
📅 Year: 2022 | 📊 Cited by: 34 | 🤖 AI control, mobility systems

📘 Integrated thermal and energy management of connected hybrid electric vehicles using deep reinforcement learning
📅 Year: 2023 | 📊 Cited by: 30 | 🧠 Deep learning, hybrid energy systems

Elahe Karampour | Artificial Intelligence | Best Researcher Award

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Ms. Elahe Karampour | Artificial Intelligence| Best Researcher Award

 

Ms. Elahe  Karampour  K.N.Toosi University Of Technology, Iran

Elahe Karampour is a dedicated researcher specializing in Geodesy and Geomatics Engineering with a focus on spatial-temporal data analysis and network science. She is currently pursuing her Master of Science at K.N. Toosi University of Technology, Tehran, Iran, with a thesis on analyzing location-based social networks using geometric curves. She has served as a research assistant, contributing to advancements in community detection and link prediction in spatial networks. Elahe is also an experienced educator, having taught GIS, spatial databases, and social network analysis. She has received national recognition for her academic excellence and was awarded a fully funded research visit to ScaDS.AI in Germany. Her expertise spans programming, spatial modeling, and AI-driven geospatial analytics.

🌍 Professional Profile:

Orcid

🏆 Suitability for the Best Researcher Award

 

Elahe Karampour is an outstanding researcher with significant contributions to geospatial data analysis, particularly in network science and location-based social networks. Her pioneering research integrates Ricci curvature and hyperbolic geometry for community detection and link prediction, leading to novel advancements in spatial-temporal modeling. She has published influential research in high-impact journals, demonstrating her expertise and innovation. Elahe’s research excellence is further recognized through a prestigious fully funded research visit to ScaDS.AI in Germany. With strong technical proficiency in R, Python, PostgreSQL, and GIS software, she bridges theory and application, developing intelligent recommender systems for urban planning. Her exceptional academic record, teaching excellence, and innovative research make her a prime candidate for the Best Researcher Award.

🎓 Education 

Elahe Karampour holds a Master of Science in Geodesy and Geomatics Engineering from K.N. Toosi University of Technology, Tehran, Iran (2021–2024). Her research focuses on spatial-temporal data analysis and social network modeling, with a thesis titled “Analysis of Location-Based Social Networks with Geometric Curves,” receiving a perfect grade of 20/20. She completed her Bachelor of Science in Geodesy and Geomatics Engineering at the University of Zanjan, Iran (2015–2020). Elahe ranked 23rd nationwide in the Iranian Master’s University Entrance Exam, showcasing her academic excellence. Her strong analytical and technical skills, coupled with expertise in GIS, AI, and network analysis, enable her to make significant contributions to the field of geospatial research and urban data science.

🏢 Work Experience 

Elahe Karampour has extensive research and teaching experience in geospatial data analysis and network science. As a Research Assistant (2022–2024) at K.N. Toosi University of Technology, she developed advanced models for community detection and link prediction in location-based social networks using geometric methods. She also worked as a Lecturer (2024) at Babol Noshirvani University of Technology, teaching spatial analysis and visualization to undergraduate students. Additionally, she served as a Teaching Assistant (2023–2024), guiding master’s and PhD students in GIS, social network analysis, and spatial databases. Her technical expertise in R, Python, PostgreSQL, and QGIS, combined with her ability to integrate AI with geospatial analysis, has positioned her as a leader in her research domain.

🏅 Awards and Honors 

Elahe Karampour has received multiple accolades for her academic and research excellence. She ranked 23rd nationwide in the Iranian Master’s University Entrance Exam (2021), demonstrating her strong academic foundation. She was awarded a fully funded research grant for a short-term visit to ScaDS.AI Center for Scalable Data Analytics and Artificial Intelligence in Germany (2023), recognizing her contributions to AI-driven geospatial analysis. Additionally, she was listed among the top-ranked teachers by her students for her exceptional teaching performance. Her work in network science and geospatial modeling has led to publications in high-impact journals, further cementing her status as a leading researcher. These achievements underscore her dedication and outstanding contributions to the field of geospatial and network science.

🔬 Research Focus 

Elahe Karampour’s research centers on spatial-temporal data analysis, network science, and AI-driven geospatial modeling. She specializes in analyzing location-based social networks using advanced mathematical frameworks such as Ricci curvature and hyperbolic geometry for community detection and link prediction. Her work integrates graph-based modeling with GIS technologies to enhance urban planning, mobility analysis, and personalized recommender systems. Elahe has developed innovative approaches to analyzing complex data structures, utilizing machine learning and AI techniques for geospatial applications. She is particularly interested in the intersection of mathematics, computer science, and geospatial technologies, aiming to create data-driven solutions for urban analytics and smart city development. Her research has been recognized internationally, reinforcing her expertise in geospatial data science.

📊 Publication Top Notes:

  • Karampour, E., Malek, M.R., & Eidi, M. (2025). Discrete Ricci flow: A powerful method for community detection in location-based social networks. Computers and Electrical Engineering, 123, 110302.