Dr. Jiaheng Peng | Data Science | Best Researcher Award

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Dr. Jiaheng Peng | Data Science | Best Researcher Award

PhD Candidate, East China Normal University, China

Jiaheng Peng is a dedicated Ph.D. candidate at East China Normal University, specializing in Open Source Ecosystem, Natural Language Processing, and Evaluation Science. With a strong academic record and a passion for research, he has contributed significantly to understanding Open Source dataset evaluation. His work bridges the gap between academic research and real-world Open Source applications, earning him recognition in the field.

Publication Profile

Google Scholar

🎓 Academic Background

Jiaheng Peng is pursuing his Ph.D. at East China Normal University, focusing on innovative methods to assess Open Source datasets. His research emphasizes citation network analysis, evaluating long-term dataset usage, and developing advanced Natural Language Processing (NLP) models. His academic journey is marked by high-impact publications in top-tier journals and international conferences, reflecting his expertise in computational analysis and data evaluation.

👨‍💼 Professional Experience

Although Jiaheng does not have industry consultancy or ongoing research projects, his scholarly contributions have made a substantial impact on Open Source ecosystem analysis. He actively publishes in high-impact scientific journals and conferences, ensuring that his findings help enhance dataset evaluation metrics. His commitment to advancing data-driven methodologies sets a solid foundation for future research in Open Source analysis.

🏆 Awards and Honors

Jiaheng Peng’s research excellence has been acknowledged with the Best Paper Award at the 1st Open Source Technology Academic Conference (2024). His publications in Q1-ranked journals further highlight his academic impact. His continuous contributions to the Open Source community demonstrate his dedication to advancing research and innovation in Open Source evaluation.

🔬 Research Focus

Jiaheng’s research primarily addresses the limitations of traditional Open Source data insight metrics. His work connects Open Source datasets with their corresponding academic papers, evaluating their significance through citation network mining. By bridging Open Source data with academic insights, he introduces novel evaluation methodologies that enhance dataset usability and long-term impact analysis. His research also extends into Aspect-Based Sentiment Classification, employing advanced Graph Attention Networks and NLP models to extract meaningful insights.

📌 Conclusion

Jiaheng Peng is a rising scholar in the Open Source and NLP domains, with a keen focus on dataset evaluation, citation network analysis, and sentiment classification. His academic contributions, recognized through prestigious awards and top-tier publications, establish him as a promising researcher dedicated to advancing Open Source dataset analytics. With a commitment to scientific excellence, his work continues to influence the global research community.

📚 Publication Top Notes

Evaluating long-term usage patterns of open source datasets: A citation network approach
BenchCouncil Transactions on Benchmarks, Standards and Evaluations (2025)
Cited by: Pending

DRGAT: Dual-relational graph attention networks for aspect-based sentiment classification
Information Sciences (2024)
Cited by: Pending

Data Driven Visualized Analysis: Visualizing Global Trends of GitHub Developers with Fine-Grained Geo-Details
International Conference on Database Systems for Advanced Applications (2024)
Cited by: Pending

ASK-RoBERTa: A pretraining model for aspect-based sentiment classification via sentiment knowledge mining”
Knowledge-Based Systems (2022)
Cited by: Multiple researchers in NLP and sentiment analysis

Prof. Dr. YiSheng Huang | Computer Science | Best Researcher Award

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Prof. Dr. YiSheng Huang | Computer Science | Best Researcher Award

Professor, National Ilan University/EE, Taiwan

Dr. Yi-Sheng Huang is a distinguished Professor at National Ilan University, Taiwan, with a remarkable career in Electrical and Electronic Engineering. Holding a Ph.D. from National Taiwan University of Science and Technology (NTUST), he has significantly contributed to Discrete Event Dynamic Systems, Petri Nets, and Intelligent Transportation Systems (ITS). Recognized among the World’s Top 2% Scientists (1960–2023), his expertise spans both theoretical advancements and real-world applications, shaping the future of smart mobility and automation. With leadership roles in academia and international collaborations, Dr. Huang continues to drive impactful research and innovation in the field of intelligent systems. 🚀

Publication Profile

📚 Academic Background

Dr. Yi-Sheng Huang earned his Ph.D. in Electrical Engineering from NTUST in 2001, laying the foundation for his expertise in dynamic systems and intelligent transportation. His commitment to academic excellence led him to various leadership positions, including Chairman of the Department of Electrical Engineering at National Ilan University (2015–2019) and Dean of the Office of Research and Development (2023–2024). He also enriched his global academic experience as a Visiting Professor at the New Jersey Institute of Technology (2008, 2014), fostering cross-border collaborations in intelligent automation. 🎓

🏢 Professional Experience

With over two decades of academic and research contributions, Dr. Huang has completed 30 major research projects and collaborated with industry leaders such as CECI Engineering Consultants, INC., Taiwan. His consultancy work spans 6 completed and 7 ongoing industry projects, demonstrating his ability to bridge theoretical research with practical applications. He is an esteemed member of the IEEE SMC Society and IEEE ITS Society, further solidifying his influence in cutting-edge technological advancements. His extensive publication record includes 114 journal papers indexed in Scopus and SCI, reflecting his commitment to advancing the field of intelligent systems. ⚡

🏆 Awards and Honors

Dr. Huang’s research excellence has earned him a place among the World’s Top 2% Scientists (1960–2023), highlighting his profound impact on electrical engineering and intelligent systems. His work has significantly influenced transportation optimization, making urban mobility smarter and more efficient. As a leading researcher, he continues to push the boundaries of Discrete Event Dynamic Systems and Petri Nets applications, setting new standards in system modeling and control. His contributions have been acknowledged globally, reinforcing his reputation as a pioneering scientist in his field. 🏅

🔬 Research Focus

Dr. Huang’s research revolves around Discrete Event Dynamic Systems, Petri Nets, and Intelligent Transportation Systems (ITS). His work has led to advanced methodologies in traffic flow management, congestion control, and system optimization, improving urban transport networks. By integrating theoretical models with real-world applications, he has contributed to automated decision-making frameworks, enhancing efficiency and sustainability in transportation. His research is instrumental in shaping smart cities and next-generation mobility solutions, fostering safer and more efficient transport ecosystems worldwide. 🚦

🔍 Conclusion

Dr. Yi-Sheng Huang stands at the forefront of intelligent transportation research, driving significant innovations in automation and dynamic systems. His global academic presence, extensive research contributions, and impactful industry collaborations establish him as a leading figure in electrical engineering and intelligent mobility solutions. With a legacy of over 114 publications, numerous research projects, and an enduring influence in academia, Dr. Huang continues to shape the future of intelligent transportation and automated systems. 🌍🚀

📝 Top Publications

A Petri net-based model for real-time traffic control in urban networks.

Optimization of discrete event systems using hybrid Petri nets.

Modeling and performance analysis of ITS using dynamic Petri nets.

Smart transportation planning with discrete event system simulation.

Enhancing automated transport systems using intelligent Petri nets.

 

Prof. Dr. Hamid Arabnia | Data Science | Best Researcher Award

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Prof. Dr. Hamid Arabnia | Data Science | Best Researcher Award

Professor Emeritus, University of Georgia, United States

Dr. Hamid R. Arabnia is a distinguished Professor Emeritus of Computer Science at the University of Georgia, USA 🎓. With a Ph.D. in Computer Science from the University of Kent, England (1987), he has made substantial contributions to the fields of Artificial Intelligence, Data Science, Machine Learning, HPC, and STEM education 🤖📊. Over his career, he has mentored 23 Ph.D. students and played a vital role in advancing computational science and intelligence. He has been an active advocate against cyber-harassment and cyberbullying, winning a landmark lawsuit in 2017–2018, securing a $3 million ruling ⚖️. Prof. Arabnia has an extensive publication record with 300+ peer-reviewed papers and 200+ edited research books, establishing himself among the top 2% most impactful scientists, as recognized by Stanford University 🌍📚.

Publication Profile

🎓 Education

Dr. Arabnia earned his Ph.D. in Computer Science from the University of Kent, England (1987) 🏛️. His research during his doctoral studies laid the foundation for his pioneering contributions in supercomputing and artificial intelligence 🤖💡.

💼 Experience

Dr. Arabnia has been with the University of Georgia since 1987, contributing as a Professor, Graduate Coordinator, and Research Director 🏫. He has served as Editor-in-Chief of The Journal of Supercomputing (Springer) and is the book series editor for Transactions of Computational Science and Computational Intelligence (Springer) 📖. His leadership has also extended to roles as a senior adviser for global corporations and National Science Foundation (NSF) committees for over 10 years 🏆.

🏅 Awards and Honors

Prof. Arabnia has received numerous prestigious awards, including recognitions from IEEE BIBE, ACM SIGAPP, and IMCOM 🏅. His legal victory against cyber-harassment was a landmark case, setting an important precedent in the U.S. legal system ⚖️. His contributions to STEM education and securing $12 million in funding for graduate research at UGA have also been widely recognized 💰📚.

🔬 Research Focus

Dr. Arabnia’s research spans Data Science, AI, HPC, Machine Learning, Imaging Science, and Compute-Intensive Problems 🤖📊. He has been actively involved in cybersecurity legislation advocacy, focusing on cyberstalking and online harassment 🔒. His latest work integrates deep learning, upsampling techniques, and AI-driven smart city applications 🌍.

🔚 Conclusion

Dr. Hamid R. Arabnia is a highly influential researcher, educator, and advocate for ethical AI and cybersecurity 🏆. With over 500 publications and millions in research funding, his contributions have shaped modern supercomputing, artificial intelligence, and digital security 🔬. Recognized among the top 2% impactful scientists globally, his work continues to inspire the next generation of AI and computer science researchers 🚀.

📚 Publications

Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Upsampling under Varying Imbalance Levels (2025) – Preprint

A New Efficient Hybrid Technique for Human Action Recognition Using 2D Conv-RBM and LSTM with Optimized Frame Selection (2025) – Technologies | DOI 📑

SWAG: A Novel Neural Network Architecture Leveraging Polynomial Activation Functions for Enhanced Deep Learning Efficiency (2024) – IEEE Access | DOI 📖

Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis (2023) – Technologies | DOI 📜

A Review of Deep Transfer Learning and Recent Advancements (2023) – Technologies | DOI 📘

Embodied AI-Driven Operation of Smart Cities: A Concise Review (2021) – TechRxiv | DOI 🌍

Mr. André Guimarães | Computer Science | Best Researcher Award

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Mr. André Guimarães | Computer Science | Best Researcher Award

Researcher, University of Beira Interior, Portugal

Andre Guimarães is a dedicated researcher and educator in the fields of Engineering Sciences, Industrial Engineering, and Management. With a strong academic background, he has contributed significantly to various research projects related to Industry 4.0 and digital transformation. He currently holds research positions at the University of Beira Interior and the Polytechnic Institute of Viseu, Portugal. Alongside his academic work, Andre has accumulated practical experience in industrial environments, particularly in production management and technical consulting, where he focuses on quality management, lean methodologies, and engineering innovations. He is also a passionate educator, teaching engineering and management-related courses at the higher education level. 📚🔬

Publication Profile

ORCID

Education:

Andre’s educational journey includes a Master’s degree in Mechanical Engineering and Industrial Management from the Polytechnic Institute of Viseu. He is currently pursuing a PhD in Industrial Engineering and Management at the University of Beira Interior. Additionally, Andre holds several postgraduate qualifications, including a specialization in Industry 4.0 and Digital Transformation from the Polytechnic Institute of Porto. His training also includes certifications in quality management, Six Sigma, lean manufacturing, and other engineering disciplines. 🎓📖

Experience:

Andre’s professional career spans both academia and industry. He has worked as a researcher at the University of Beira Interior and the Polytechnic Institute of Viseu, contributing to cutting-edge research in mechanical and industrial engineering. Additionally, Andre has extensive industrial experience, having served as the Production Manager at IPROM – Products Industry Metallics Ltd, where he oversaw production processes and managed technical operations. As a consultant and facilitator at the Welding and Quality Institute, Andre applies his expertise in quality management systems and continuous improvement. 🏭⚙️

Awards and Honors:

Andre Guimarães has been recognized for his contributions to both research and industry. He is a full member of the Order of Engineers in Portugal and a fellow at FCT Research. His work has been acknowledged through various academic and industry accolades, cementing his reputation as a skilled professional and educator in his field. 🏅🌟

Research Focus:

Andre’s research interests are deeply rooted in Industry 4.0 technologies, digital transformation, lean management, and quality systems in industrial engineering. His research aims to bridge the gap between theoretical frameworks and practical applications in engineering, with a focus on improving production efficiency, implementing digital technologies, and optimizing management processes in industrial environments. His recent projects explore advanced methodologies in electromechatronics and systems research. 🔍📊

Conclusion:

With a rich academic background and a wealth of practical experience, Andre Guimarães stands at the intersection of research and industry, contributing to the evolution of engineering practices. His work, driven by a passion for innovation and education, continues to shape the future of industrial engineering and management in Portugal and beyond. Andre’s ongoing commitment to advancing the field through both research and practical applications makes him a valuable asset to the academic and industrial communities. 🚀🌍

Publications:

The influence of consumer, manager, and investor sentiment on US stock market returnsInvestment Management and Financial Innovations

Effects of Lean Tools and Industry 4.0 technology on productivity: An empirical studyJournal of Industrial Information Integration

Método Delphi modificado para abordar a transformação digital na gestão de ativosRevista de Ativos de Engenharia

Lean philosophy and Value Engineering methodologies. Their relations and synergy using Bert a natural language processing modelCongrEGA 2024 – Sustainable and Digital Innovation in Engineering Asset Management

Modificação do Método Delphi para Aplicação num Questionário sobre a Transformação Digital na Gestão de AtivosCongrEGA 2024 – Sustainable and Digital Innovation in Engineering Asset Management

Overview of the use of data assets in the context of Portuguese companies: Comparison between SMEs and large companiesCongrEGA 2024 – Sustainable and Digital Innovation in Engineering Asset Management

Comparative analysis of welding processes using different thermoplasticsInternational Journal of Integrated Engineering

Chunling Bao | Data Science | Best Researcher Award

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Ms. Chunling Bao | Data Science | Best Researcher Award

PhD Candidates, Shanghai Normal University, China

Chunling Bao is a dedicated Ph.D. candidate at Shanghai Normal University, specializing in environmental and geographical sciences 🌍. With a strong academic background and research focus on dust storms, climate change, and land surface interactions, she has contributed significantly to understanding environmental dynamics in East Asia. Her scholarly work is widely recognized, with multiple publications in high-impact journals 📚.

Publication Profile

ORCID

🎓 Education

Chunling Bao embarked on her academic journey at Inner Mongolia Normal University, earning her undergraduate degree (2014-2018) and later obtaining her master’s degree (2018-2021) 🎓. She expanded her expertise through an exchange program at the Center for Agricultural Resources Research, Chinese Academy of Sciences (2023), before pursuing her doctoral studies at Shanghai Normal University (2023-present) 🏫.

💼 Experience

With a deep passion for environmental research, Chunling Bao has explored dust storms, vegetation interactions, and land-atmosphere processes. Her experience includes field studies, satellite data analysis, and interdisciplinary research collaborations 🌪️. Her academic training at leading Chinese institutions has enriched her expertise in remote sensing, environmental monitoring, and climate analysis.

🏆 Awards and Honors

Chunling Bao has been recognized for her outstanding research contributions in environmental science 🏅. Her work has been published in top-tier journals, and she has actively participated in academic exchanges and research collaborations. Her efforts in studying dust storm dynamics have positioned her as an emerging scholar in the field 🌿.

🔬 Research Focus

Her research primarily focuses on the spatial and temporal dynamics of dust storms, their drivers, and their environmental impacts in East Asia 🌫️. Using remote sensing and geospatial analysis, she investigates the effects of land surface changes on atmospheric conditions. Her studies contribute to climate adaptation strategies and sustainable environmental management.

📌 Conclusion

As an emerging environmental researcher, Chunling Bao is making significant strides in understanding dust storm dynamics and their broader ecological implications. With her growing academic contributions and research excellence, she continues to shape the field of environmental science and atmospheric studies 🌏.

📚 Publications

Dust Intensity Across Vegetation Types in Mongolia: Drivers and Trends. Remote Sensing, 17(3), 410. 🔗 DOI

Analyses of the Dust Storm Sources, Affected Areas, and Moving Paths in Mongolia and China in Early Spring. Remote Sensing, 14, 3661. 🔗 DOI

Impacts of Underlying Surface on Dusty Weather in Central Inner Mongolian Steppe, China. Earth and Space Science, 8, e2021EA001672. 🔗 DOI

Regional Spatial and Temporal Variation Characteristics of Dust in East Asia. Geographical Research, 40(11), 3002-3015. 🔗 DOI (in Chinese)

Analysis of the Movement Path of Dust Storms Affecting Alxa. Journal of Inner Mongolia Normal University (Natural Science Mongolian Edition), 04, 39-47.

Evaluation of the Impact of Coal Mining on Soil Heavy Metals and Vegetation Communities in Bayinghua, Inner Mongolia. Journal of Inner Mongolia Normal University (Natural Science Mongolian Edition), 40(1), 32-38.

 

 

Huan Zhao | Machine Learning | Best Researcher Award

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Assoc. Prof. Dr . Huan Zhao | Machine Learning | Best Researcher Award

Associate Professor, School of Aeronautics, Northwestern Polytechnical University, China

Huan Zhao is an associate professor at the School of Aeronautics, Northwestern Polytechnical University (NPU), China. He specializes in aerodynamics, multidisciplinary design optimization, uncertainty quantification, and machine learning, focusing on CFD simulation, AI-based global optimization, and surrogate modeling. He is also the executive deputy director of the Institute of Digital Intelligence for Flight Mechanics and Aerodynamic Design (IDIFMAD). Zhao has made significant contributions to the fields of aerodynamic shape optimization, high-dimensional global optimization, and uncertainty-based robust design. He holds several patents and has authored many high-impact publications. 🌐✈️

Publication Profile

Education

Huan Zhao completed his Ph.D. in Fluid Dynamics at Northwestern Polytechnical University (NPU) in 2020, following a B.Eng. in Aircraft Design and Engineering from the same university in 2014. 📚🎓

Experience

Zhao served as a tenure-track assistant professor at Sun Yat-sen University (SYSU) before joining NPU as a tenure-track associate professor in 2023. He has directed and participated in numerous research projects focusing on aerodynamic design optimization, high-speed rotor airfoil design, and surrogate-assisted design techniques. He is a principal investigator (PI) for multiple projects funded by the National Natural Science Foundation of China (NSFC). 👨‍🏫🔬

Awards and Honors

Huan Zhao has received several awards and honors, including recognition as part of the “Hundred Talents Plan” Young Academic Backbone at SYSU and multiple patents for his innovative contributions to aerodynamic design. 🏆🎖️

Research Focus

Zhao’s research interests lie in aerodynamics, including multi-fidelity polynomial chaos-Kriging models, aerodynamic shape optimization, and uncertainty quantification. His work has contributed significantly to the design and optimization of high-lift airfoils, laminar flow airfoils, and robust design methods under uncertainty. His expertise also includes machine learning, AI-based global optimization, and the application of surrogate models in complex design scenarios. 🔍🧑‍💻

Conclusion

Huan Zhao’s innovative work has had a profound impact on the field of aerodynamics and optimization. His research has not only advanced the understanding of aerodynamic design but has also led to practical improvements in the development of high-performance aircraft and related technologies. He continues to drive forward cutting-edge research in aerodynamics and multidisciplinary design optimization. 🚀🌍

Publications

An efficient adaptive forward–backward selection method for sparse polynomial chaos expansion, Computer Methods in Applied Mechanics and Engineering, 2019.

Review of robust aerodynamic design optimization for air vehicles, Archives of Computational Methods in Engineering, 2019.

Effective robust design of high lift NLF airfoil under multi-parameter uncertainty, Aerospace Science and Technology, 2017.

Adaptive multi-fidelity sparse polynomial chaos-Kriging metamodeling for global approximation of aerodynamic data, Structural and Multidisciplinary Optimization, 2021.

Uncertainty-based design optimization of NLF airfoil for high altitude long endurance unmanned air vehicles, Engineering Computations, 2019.

 Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model, Computers & Fluids, 2022.

Research on efficient robust aerodynamic design optimization method of high-speed and high-lift NLF airfoil, Acta Aeronautica et Astronautica Sinica, 2021.

Research on Novel High-Dimensional Surrogate Model-Based Aerodynamic Shape Design Optimization, Acta Aeronautica et Astronautica Sinica, 2022.

Research on novel multi-fidelity surrogate model assisted many-objective global optimization method, Acta Aeronautica et Astronautica Sinica, 2022.

Adaptive multi-fidelity polynomial chaos-Kriging model-based efficient aerodynamic design optimization method, Chinese Journal of Theoretical and Applied Mechanics, 2023.

 

Md. Emran Biswas | Data science | Best Researcher Award

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Mr. Md. Emran Biswas | Data science | Best Researcher Award

Research Assistant, Hajee Mohammad Danesh Science and Technology University, Bangladesh

🌟 Md. Emran Biswas, hailing from Dinajpur, Bangladesh, is a passionate researcher and technologist specializing in machine learning, optimization algorithms, and their societal applications. He has actively contributed to predictive analysis, bioinformatics-based drug discovery, and developing AI solutions for global good. As a skilled programmer and researcher, Emran’s work has earned recognition through multiple publications, accolades, and groundbreaking projects in his field.

Publication Profile

Scopus

Education

🎓 Md. Emran Biswas completed his B.Sc. in Electronics and Communication Engineering at Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur, Bangladesh, from March 2019 to November 2024, with an impressive CGPA of 3.412/4.00. His academic journey is marked by a focus on deep learning, predictive modeling, and optimization algorithms.

Experience

💼 Emran served as a Research Assistant at Petarhub and DIOT Lab, HSTU, contributing to machine learning, predictive modeling, and optimization projects. His notable achievements include developing the ApexBoost Regression model, managing large datasets, and publishing impactful research in reputed journals like IEEE and Electronics.

Research Interests

🔍 Emran’s research focuses on machine learning, optimization algorithms, and their transformative applications in areas like bioinformatics-based drug discovery, predictive analysis, and societal challenges. His work aligns with the vision of ‘AI for Good,’ driving impactful innovation.

Awards

🏆 Emran has earned recognition for his innovative projects, including First Runner-Up at the Project Exhibition 2022 for his “Face Detection-Based Attendance System” and Second Runner-Up in 2023 for his “AI-Based Health Checking System.” These awards reflect his technical expertise and creative problem-solving skills.

Publications

Machine Learning Approach to Estimate Requirements for Target Productivity of Garments Employees. IEEE ICEEICT 2024 (Cited by: 5)

An Effective Data-Driven Approach to Predict Bike Rental Demand. Google Scholar (Cited by: 12)

Spatio-Temporal Feature Engineering and Selection-Based Flight Arrival Delay Prediction Using Deep Feedforward Regression Network. Electronics, 13(24), p.4910 (Cited by: 9)

 

PETROS PATIAS | Data science | Best Researcher Award

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Prof. PETROS PATIAS | Data science | Best Researcher Award

CEO, KIKLO – GEOSPATIAL INFORMATION TECHNOLOGIES P.C., Greece

Prof. Petros Patias is a prominent expert in photogrammetry and remote sensing, serving as Professor and Director at the Laboratory of Photogrammetry & Remote Sensing at Aristotle University of Thessaloniki (AUTH), Greece. A leader in his field, he has held esteemed roles, including Vice Rector at the University of Western Macedonia and former President of the Hellenic Society for Photogrammetry & Remote Sensing. Prof. Patias has made groundbreaking contributions internationally through the ISPRS and CIPA, cementing his legacy as an Honorary President and Fellow of these global scientific communities. His impact continues through extensive research, teaching, and scientific guidance worldwide.

Publication Profile

ORCID

Education 🎓📚

Prof. Patias holds a MEng from Aristotle University (1981), an MSc (1985), and a PhD (1987) in Geodetic Science and Surveying from The Ohio State University, USA. His extensive education laid the foundation for his international recognition and contributions in geospatial sciences.

Experience 🏛️🌍

Prof. Patias has held numerous prestigious academic and leadership roles, such as ex-Chairman of the School of Rural and Surveying Engineering at AUTH, board member of the Department of Urban Planning, and Vice Rector at the University of Western Macedonia. He served as President of the Hellenic Society for Photogrammetry & Remote Sensing and led ISPRS Working Groups and Commissions. His experience extends globally as a Visiting Professor at renowned institutions like TU Delft, ETH Zurich, and Universidad del País Vasco.

Research Focus 🔍🌐

Prof. Patias’s research focuses on photogrammetry, remote sensing, and geospatial sciences, with applications in architectural photogrammetry and urban planning. He collaborates internationally, advising institutions such as ETH Zurich, University of Maine, Politecnico di Milano, and IIT Roorkee, and leads impactful projects through European and National organizations.

Awards and Honors 🏆🌟

Prof. Patias has received numerous honors, including an ISPRS Fellowship (2016) and lifetime honorary presidencies with both CIPA and ISPRS. His leadership contributions have earned him esteemed positions, reflecting his commitment to advancing photogrammetry and remote sensing worldwide.

Publications Top Notes 📝📅

“Aerial Photogrammetry for Urban Planning” (2020) published in Remote Sensing; cited by 48 articles.

“Geospatial Data Applications in Urban Development” (2018) published in Geodetic Science Journal; cited by 32 articles.

“Remote Sensing in Archaeological Mapping” (2017) published in International Journal of Archaeology; cited by 45 articles.

“Photogrammetric Techniques for Heritage Conservation” (2016) published in Heritage Science Review; cited by 60 articles.

 

Rania Sefti | Data Science | Best Researcher Award

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Ms. Rania Sefti | Data Science | Best Researcher Award

Phd student, Université Mohammed Premier Oujda, Morocco

Sefti Rania is a passionate researcher specializing in numerical analysis, optimization, and image processing. With a robust academic background and extensive teaching experience, she is currently pursuing a Ph.D. in a joint program between Morocco and France. Her research focuses on developing advanced methods for medical image segmentation using deep learning techniques.

Profile

Scopus

 

Education 🎓

Ph.D. in Mathematics and Computer Science (Specialization: Numerical Analysis and Optimization, Image Processing, Deep Learning), Mohammed First University, Oujda, Morocco, University of Orleans, France (Since 2020). Master in Numerical Analysis and Optimization (Honors: Good), Mohammed First University, Oujda, Morocco (2019). Bachelor’s Degree in Mathematical Sciences and Applications (Honors: Fairly Good), Mohammed First University, Oujda, Morocco (2017). High School Diploma in Experimental Sciences (Honors: Good), Ibn El Haytam High School, Nador, Morocco (2012)

Experience 💼

Adjunct Lecturer at Mohammed First University, Oujda, Morocco (2020 – Present). Higher School of Technology (Specialty: MCT and LPMI). Faculty of Sciences (Specialty: SVT and SMPC). Modules taught include Mathematics and Analysis with a total of over 200 hours of instruction. Reviewer for numerous articles in Mathematics and Computer Science since 2022

Research Interests 🔬

Numerical Analysis and Optimization, Image Processing, Deep Learning, Medical Image Segmentation.

Awards 🏆

Numerous Publications in renowned journals and conferences in the field of numerical analysis and optimization. Presentation Awards for contributions at international conferences such as MACMAS, NT2A, and SMAI-SIGMA

Publications

A CNN-based spline active surface method with an after-balancing step for 3D medical image segmentation, Mathematics and Computers in Simulation. Link – Cited by:

C2 composite spline methods for fitting data on the sphere, Springer special volume of the SEMA-SIMAI Springer Series. (Accepted in June 2023) – Cited by:

PID-Snake: Progressive Iterative Deformation of a Snake model for segmentation of a variety of images, Journal of Computational and Applied Mathematics. (Submitted in June 2024) – Cited by:

Fine-tuned cubic generalized composite spline interpolation with optimal parameter, Mathematics in Computer Science. (Submitted in June 2024) – Cited by:

A deep network-based spline active contour method for medical image segmentation, Springer special volume of the SEMA-SIMAI Springer Series. (Submitted in 2024) – Cited by: