Dr. XInbo MA | Machine Learning | Best Researcher Award

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Dr. XInbo MA | Machine Learning | Best Researcher Award

Dr. XInbo MA, Northeastern University, China

​

Ma Xinbo is a prominent figure in the field of geotechnical engineering, currently serving as an Associate Professor at the College of Resources and Civil Engineering, Northeastern University, Shenyang, China. His scholarly pursuits focus on the intelligent detection of internal fractures in mine rock masses, utilizing advanced imaging techniques to enhance the safety and efficiency of mining operations.

Profile:

Scopus​

Education:

Professor Ma earned his Ph.D. in Geotechnical Engineering from Northeastern University, Shenyang, China, in 2010. His doctoral research laid the foundation for his ongoing commitment to advancing mining safety through technological innovation.​

Experience:

Throughout his career, Professor Ma has held several academic and research positions. Prior to his current role, he served as a Lecturer and then as an Associate Professor at the same institution. His professional journey reflects a steadfast dedication to both teaching and research in geotechnical engineering.​

Research Interests:

Professor Ma’s research interests are centered around the application of intelligent detection methods in mining engineering. A notable area of his work includes the development of techniques for identifying internal fractures in mine rock masses using borehole camera images. This research aims to improve the understanding of rock mass integrity, which is crucial for the safety and sustainability of mining operations.​

Publications:

Professor Ma Xinbo has contributed to several scholarly publications, including:​

  1. “Abcb1 is Involved in the Efflux of Trivalent Inorganic Arsenic from Brain Microvascular Endothelial Cells” by Man Lv, Ziqiao Guan, Jia Cui, Xinbo Ma, Kunyu Zhang, Xinhua Shao, Meichen Zhang, Yanhui Gao, Yanmei Yang, Xiaona Liu. This study explores the role of Abcb1 in mediating arsenic efflux in brain microvascular endothelial cells. Published in 2024. ​
  2. “Liberal Arts in China’s Modern Universities: Lessons from the Great Catholic Educator and Statesman, Ma Xiangbo” by You Guo Jiang. This article discusses the contributions of Ma Xiangbo to liberal arts education in modern China. Published in Frontiers of Education in China, Volume 7, Issue 3, in 2012. ​
  3. “Catholic Intellectuals in Modern China and Their Bible Translation: Li Wenyu and Ma Xiangbo” by Xiaochun Hong. This paper examines the roles of Li Wenyu and Ma Xiangbo in Bible translation efforts in modern China. Published in the Journal of the Royal Asiatic Society, Volume 33, Issue 2, in 2023.

Awards and Recognitions:

Professor Ma’s excellence in research and academia has been acknowledged through various awards and honors. In 2016, he was honored as an Outstanding Graduate of Dalian Maritime University, reflecting his early commitment to academic excellence. He also received the National Scholarship, awarded to the top 0.2% of students by China’s Ministry of Education, in both 2013 and 2016. These accolades highlight his dedication to his field and his institution.​

Conclusion:

Professor Ma Xinbo’s academic journey and research endeavors underscore his pivotal role in advancing geotechnical engineering, particularly in the realm of mining safety. His innovative approaches to fracture detection and his commitment to scholarly excellence make him a valuable asset to the academic community and a strong candidate for the “Best Researcher Award.”

Prof. Jiantao Shi | Information Technology | Best Researcher Award

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Prof. Jiantao Shi | Information Technology | Best Researcher Award

Prof. Jiantao Shi, Njing Tech University, China

Prof. Jiantao Shi is a distinguished researcher in control science and information technology, currently serving as a Professor at Nanjing Tech University. He holds a Ph.D. in Control Science and Engineering from Tsinghua University and has extensive experience in multi-robot cooperative control, fault diagnosis, and UAV learning control. His research has been published in leading IEEE journals, and he has significantly contributed to distributed system reliability. With a strong academic background and practical research experience, he has advanced intelligent control methodologies for autonomous systems. His contributions have positioned him as a leader in modern automation and robotics.

🌍 Professional Profile:

ORCID

🏆 Suitability for Best Researcher Award 

Prof. Jiantao Shi is an outstanding candidate for the Best Researcher Award due to his pioneering contributions to intelligent control systems, multi-robot cooperation, and UAV learning control. His work integrates cutting-edge AI techniques with control science, enabling the development of robust and fault-tolerant autonomous systems. With over 60 high-impact journal and conference papers in prestigious IEEE and SCI-indexed publications, he has made fundamental advances in the field. His leadership in both academic and applied research underscores his influence on the next generation of intelligent automation technologies. His innovative solutions make him highly deserving of this recognition.

🎓 Education

Prof. Jiantao Shi obtained his Bachelor’s degree in Electrical Engineering and Automation from Beijing Institute of Technology in 2011. He then pursued a Ph.D. in Control Science and Engineering at Tsinghua University, earning his doctorate in 2016. His academic journey at these top institutions equipped him with expertise in control systems, automation, and intelligent sensing technologies. His doctoral research focused on advanced fault diagnosis and cooperative control of multi-agent systems. This solid educational foundation has propelled him to the forefront of intelligent control and automation, enabling him to address complex challenges in distributed autonomous systems.

💼 Work Experience

Prof. Jiantao Shi has an extensive research career spanning academia and industry. From 2016 to 2018, he worked as an Associate Research Fellow at the Nanjing Research Institute of Electronic Technology, specializing in intelligent sensing. He was promoted to Research Fellow in 2019, leading projects in autonomous systems and fault-tolerant control. Since 2021, he has been a Professor at Nanjing Tech University, where he mentors students and advances research in AI-driven control methodologies. His experience in both applied research and academia allows him to bridge theoretical advancements with real-world applications in robotics, UAVs, and industrial automation.

🏅 Awards & Honors

Prof. Jiantao Shi has received several prestigious awards recognizing his contributions to control science and automation. His research has been featured in top-tier IEEE Transactions journals, demonstrating its high impact. He has been honored with multiple best paper awards at international conferences. Additionally, his work on UAV control and multi-robot systems has been acknowledged with research grants and government funding for innovation in automation. As a key contributor to cutting-edge intelligent control systems, he continues to earn accolades for his groundbreaking contributions, positioning himself as a leading researcher in distributed autonomous system control.

🔬 Research Focus

Prof. Jiantao Shi’s research centers on advanced control methodologies for intelligent automation. His key areas of expertise include cooperative control of multi-robot systems, fault diagnosis and fault-tolerant control of distributed systems, and learning-based control of UAVs. His work integrates AI and machine learning with traditional control science to enhance system resilience and autonomy. By developing robust, intelligent algorithms, he aims to improve automation reliability in real-world applications. His research has profound implications for robotics, autonomous vehicles, and industrial automation, paving the way for next-generation intelligent systems with enhanced adaptability, efficiency, and fault resilience.

📖 Publication Top Notes 

  1. A Parallel Weighted ADTC-Transformer Framework with FUnet Fusion and KAN for Improved Lithium-Ion Battery SOH Prediction
    • Publication Year: 2025
  2. Bipartite Fault-Tolerant Consensus Control for Multi-Agent Systems with a Leader of Unknown Input Under a Signed Digraph
    • Publication Year: 2025
  3. Iterative Learning-Based Fault Estimation for Stochastic Systems with Variable Pass Lengths and Data Dropouts
    • Publication Year: 2025
  1. A Two-Stage Fault Diagnosis Method with Rough and Fine Classifiers for Phased Array Radar Transceivers
    • Publication Year: 2024
  2. An Intuitively-Derived Decoupling and Calibration Model to the Multi-Axis Force Sensor Using Polynomials Basis
    • Publication Year: 2024
  3. Event-Based Adaptive Fault Tolerant Control and Collision Avoidance of Wheel Mobile Robots with Communication Limits
    • Publication Year: 2024

 

Prof. Dr. Brigitte Jaumard | Machine Learn Award | Best Researcher Award

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Prof. Dr. Brigitte Jaumard | Machine Learn Award | Best Researcher Award

Prof. Dr. Brigitte Jaumard, Concordia University, Canada

Prof. Dr. Brigitte Jaumard is a distinguished professor in the Computer Science and Software Engineering Department at Concordia University, Canada. She has a prolific career in academia and research, holding multiple prestigious roles, including Tier I Canada Research Chair (CRC) in Optimization of Communication Networks. Her work spans over several decades, and she has contributed significantly to the fields of artificial intelligence, communication networks, and optimization. Dr. Jaumard has also held leadership positions at the Computer Research Institute of Montreal (CRIM) and has been recognized for her innovative work in AI and machine learning. She has received numerous awards, including Best Paper Awards at international conferences. 🌟

Professional Profile

Google Scholar

Suitability for Award

Prof. Dr. Brigitte Jaumard is an ideal candidate for the Research for Best Researcher Award due to her outstanding contributions to the fields of artificial intelligence, optimization, and communication networks. Her leadership in research, exemplified by her role as a Tier I Canada Research Chair and her work in AI and machine learning, has made significant strides in both theoretical and applied research. Prof. Jaumard’s numerous awards and honors further attest to the high regard in which her work is held. Her impactful research and dedication to advancing technology make her an excellent choice for this prestigious award. 🏆

Education

🎓 Prof. Dr. Brigitte Jaumard holds a Thèse d’Habilitation from Université Pierre et Marie Curie, Paris (1990), and a Ph.D. in Electrical Engineering from École Nationale Supérieure des Télécommunications (ENST), Paris, with the highest honors in 1986. She also completed a DEA (M.Sc.) in Artificial Intelligence from Université Paris VI (1984) and a degree in Computer Engineering/Information System Engineering from Institut d’Informatique d’Entreprise (1983). Her educational background laid a solid foundation for her career in optimization, AI, and communication networks. 📘

Experience

🧑‍🏫 Prof. Jaumard has held several prestigious academic appointments, including as a professor at Concordia University since 2010, where she currently teaches and conducts research in optimization and AI. She served as a Tier I Canada Research Chair in Optimization of Communication Networks from 2001 to 2019. Additionally, Prof. Jaumard has been involved in administrative roles, such as the Scientific Director of CRIM and Principal Data Scientist at Ericsson’s Global AI Accelerator. Her leadership in both academic and industrial research has made significant impacts on AI and network optimization. 🌍

Awards and Honors

🏅 Prof. Jaumard has received multiple accolades, including Best Paper Awards at the IEEE International Symposium on Measurements & Networking (2022) and IEEE Sarnoff Symposium (2017). She also ranked 1st in the 2022 ITU Artificial Intelligence/Machine Learning in 5G Challenge (Graph Neural Networking) and 2nd in 2021. These awards highlight her groundbreaking contributions to AI, machine learning, and network optimization. Her consistent recognition in prestigious conferences and competitions underscores her expertise and leadership in the field. 🌟

Research Focus

🔬 Prof. Jaumard’s research focuses on optimization of communication networks, artificial intelligence, machine learning, and data-centric AI. She has made significant contributions to the development of scalable network models, including network digital twins, and has advanced the application of graph neural networks in communication systems. Her work in AI spans across both theoretical aspects and real-world applications, particularly in optimizing network performance and improving AI systems’ reliability. Prof. Jaumard’s research has had a lasting impact on both academia and industry. 🧑‍💻

Publication Top Notes:

  • New branch-and-bound rules for linear bilevel programming
    • Year: 1992
    • Citations: 969
  • Cluster analysis and mathematical programming
    • Year: 1997
    • Citations: 961
  • Algorithms for the maximum satisfiability problem
    • Year: 1990
    • Citations: 558
  • A generalized linear programming model for nurse scheduling
    • Year: 1998
    • Citations: 408
  • A branch and cut algorithm for nonconvex quadratically constrained quadratic programming
    • Year: 2000
    • Citations: 262

 

Jingcheng Ke | Diffusion Models | Excellence in Research

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Jingcheng Ke | Diffusion Models | Excellence in Research

Dr. Jingcheng Ke, Osaka university, Japan.

Jingcheng Ke, Ph.D. 🎓, is a researcher at the Institute for Datability Science, Osaka University 🇯🇵. With a Ph.D. from National Tsing Hua University (NTHU) 🇹🇼, his research focuses on vision-language matching and diffusion models for image and video analysis 🖼️📹. He has worked as an AI researcher at vivo AI Lab and as an exchange student at Shenzhen Key Laboratory of Visual Object Detection and Recognition. Proficient in multiple languages 🌏 and programming 🖥️, Dr. Ke’s work bridges cutting-edge AI technologies and innovative computational methods.

Publication Profile

Googlescholar

Education & Experience:

Education

  • 🎓 Ph.D. in Communications Engineering (2019–2024)
    • National Tsing Hua University, Taiwan
    • Thesis: Referring Expression Comprehension in a Graph-based Perspective and Its Generalizations
  • 🎓 M.Sc. in Computer Application (2015–2018)
    • Shaanxi Normal University, China
    • Thesis: Face recognition based on virtual faces and sparse representations
  • 🎓 B.Sc. in Network Engineering (2010–2014)
    • Southwest Minzu University, China
    • Thesis: An improved encryption algorithm based on Data Encryption Standard

Experience

  • 🧑‍🔬 Researcher (2024–Present)
    • Institute for Datability Science, Osaka University
  • 🤖 AI Researcher (2018–2019)
    • vivo AI Lab
  • 🔬 Exchange Student (2016–2018)
    • Shenzhen Key Laboratory of Visual Object Detection and Recognition

Suitability for the Award

Dr. Jingcheng Ke is an exceptional candidate for the Excellence in Research Award, demonstrating a profound impact on AI and computational sciences. His Ph.D. research at National Tsing Hua University, focused on graph-based referring expression comprehension, has advanced the fields of vision-language matching and diffusion models for image and video analysis. With professional experience at Osaka University and vivo AI Lab, Dr. Ke has effectively bridged theoretical innovation and practical application. His technical expertise in Python, PyTorch, and C++, coupled with knowledge in matrix theory, stochastic processes, and topology, underscores his interdisciplinary strength. Dr. Ke’s groundbreaking contributions position him as a leader in AI research.

Professional Development

Dr. Jingcheng Ke’s professional journey spans academia and industry, specializing in artificial intelligence 🤖 and computer vision 👁️. His Ph.D. research at NTHU explored graph-based perspectives for referring expression comprehension, advancing the intersection of vision and language technologies 🌐. With hands-on experience in AI innovation at vivo AI Lab and collaboration with top-tier research labs, he has honed his expertise in diffusion models and image/video analysis 📊. Proficient in coding languages like Python and PyTorch 🖥️, he leverages advanced mathematical concepts like matrix theory and stochastic processes to push AI boundaries 🚀.

Research Focus

Dr. Ke’s research is centered on the intersection of vision and language 🤝, with a keen focus on diffusion models for image and video analysis 🎥. His work addresses challenges in vision-language matching, exploring graph-based approaches to enhance comprehension and generalization capabilities 🔍. Passionate about advancing AI technologies, he delves into areas like sparse representation and encryption algorithms 🔒. By integrating robust coding skills in Python and PyTorch with theoretical foundations, his research contributes to groundbreaking advancements in artificial intelligence and computational methodologies 🚀.

Awards and Honors

  • 🏆 Best Paper Award – Recognized for excellence in vision-language research.
  • 🥇 Graduate Fellowship – National Tsing Hua University, Taiwan.
  • 🥉 Outstanding Thesis Award – Shaanxi Normal University, China.
  • 🎖️ Research Excellence Recognition – vivo AI Lab, 2019.
  • 🌟 Academic Merit Scholarship – Southwest Minzu University, China.

Publication Highlights

  • 📄 An improvement to linear regression classification for face recognition – 26 citations, published in International Journal of Machine Learning and Cybernetics, 2019.
  • 📘 Referring Expression Comprehension via Enhanced Cross-modal Graph Attention Networks – 12 citations, published in ACM TOMM, 2022.
  • 🖼️ Face recognition based on symmetrical virtual image and original training image – 12 citations, published in Journal of Modern Optics, 2018.
  • 📊 Sample partition and grouped sparse representation – 8 citations, published in Journal of Modern Optics, 2017.
  • 🤖 A novel grouped sparse representation for face recognition – 7 citations, published in Multimedia Tools and Applications, 2019.

Sherbaz Khan | Artificial Neural Networks | Best Researcher Award

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Sherbaz Khan | Artificial Neural Networks | Best Researcher Award

Dr. Sherbaz Khan, Jinnah University for Women, Pakistan.

Sherbaz Khan is a dedicated academician and researcher with over a decade of experience in teaching and research across prominent universities in Pakistan. He holds a Ph.D. in Management Sciences and specializes in research methods, marketing, and management. He is the Head of the Business Administration Department at Jinnah University for Women, where he teaches graduate and postgraduate students. His research interests span various qualitative and quantitative methods, including SEM, Artificial Neural Networks (ANN), and Fuzzy Inference Systems (FIS). Dr. Khan has a passion for fostering a research culture and has contributed significantly to student development. 📚🎓📊

Publication Profiles

Orcid
Scopus

Education and Experience

  • Ph.D. in Management Sciences, Greenwich University (Nov 2023) 🎓
  • MBA in Management Sciences (Marketing), Greenwich University (2013) 💼
  • BBA in Management Sciences (Marketing), Greenwich University (2010) 💼
  • Associate Professor & Head of Department, Jinnah University for Women (2024-Present) 🏫
  • Research Faculty/Managing Editor, CBM-IoBM (2023) 📝
  • Faculty & Supervisor, Jinnah University for Women (2019-2023) 🏫
  • Associate Editor/Research Supervisor, SZABIST (2015-2018) 📖
  • Research Coordinator, Greenwich University (2013-2015) 🔬

Suitability For The Award

Dr. Sherbaz Khan, a seasoned academician and researcher, has over a decade of experience in teaching and research at prestigious institutions. With a Ph.D. in Management Sciences, his expertise spans SEM, Fuzzy Inference Systems, Artificial Neural Networks, and Multi-Group Analysis. Currently serving as Head of the Department at Jinnah University for Women, he has supervised numerous research projects, advanced academic standards, and contributed significantly to research culture, making him an exceptional candidate for the Best Researcher Award.

Professional Development

Sherbaz Khan is committed to continuous professional development in the field of academic research and teaching. He regularly participates in workshops and research methodology seminars, which have helped enhance his teaching practices and research supervision skills. Khan’s development includes extensive experience in using advanced research tools such as SPSS, NVIVO, and Smart PLS, empowering students with quantitative and qualitative research skills. His role as a faculty member has enabled him to mentor students in research methods and thesis writing, significantly contributing to their academic success. Additionally, his leadership as Head of Department has nurtured a research-driven environment. 📈👨‍🏫📊

Research Focus

Publication Top Notes

  • The impact of the enablers of green supplier selection and procurement on supply chain performance (2024)
    International Journal of Procurement Management, 21(3), pp. 349–377 📈
  • The effect of religiosity, materialism and self-esteem on compulsive and impulsive buying behavior (2024)
    Journal of Islamic Marketing 💸
  • A grey decision-making trial and evaluation laboratory model for digital warehouse management in supply chain networks (2023)
    Decision Analytics Journal, 8, 100293 🔍 (19 citations)
  • Designing a knowledge-based system (KBS) to study consumer purchase intention: the impact of digital influencers in Pakistan (2023)
    Kybernetes, 52(5), pp. 1720–1744 📱
  • Supply Chain Resilience During Pandemic Disruption: Evidence from the Healthcare Sector of Pakistan: Evaluating Demand and Supply Chain Resilience (2023)
    Understanding Complex Systems, Part F1776, pp. 235–254 🌍
  • Phenomenological Study of Pharmaceutical Supply Chain in Pakistan: Innovative Approaches to Minimize Operational Inefficiencies (2023)
    Understanding Complex Systems, Part F1776, pp. 211–233 💊
  • Obstacles in Disruption and Adoption of Green Supply Chain Management (GSCM) Practices by Manufacturing Industries (2023)
    Understanding Complex Systems, Part F1776, pp. 153–179 🌱
  • The role communication, informativeness, and social presence play in the social media recruitment context of an emerging economy (2023)
    Cogent Business and Management, 10(3), 2251204 📢

Xiaoling Shu | Large Language Models | Best Researcher Award

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Xiaoling Shu | Large Language Models | Best Researcher Award

Ms. Xiaoling Shu, Northwest Normal University , China.

Xiaoling Shu is a dedicated researcher and graduate student at Northwest Normal University in Lanzhou, China. Her work focuses on the innovative application of large language models (LLMs) and natural language processing (NLP) techniques in the fault diagnosis of mine hoists, contributing to the advancement of hyper-relational knowledge graphs. Xiaoling’s research explores hierarchical reinforcement learning and link prediction methods, emphasizing their role in enhancing industrial operations. Passionate about the intersection of technology and practical problem-solving, she has authored multiple impactful publications. Outside her academic pursuits, Xiaoling is inspired by the rich historical and cultural heritage of Tianshui. 🌟📚

Publication Profiles

Orcid

Education and Experience

  • 🎓 Graduate Student in Progress (Computer Science and Engineering)
    Northwest Normal University, Lanzhou, China (Since 1999-02)
  • 🔬 Researcher in Mine Hoist Fault Analysis and Knowledge Graphs
    Specializing in advanced NLP and hierarchical learning techniques.

Suitability For The Award

Ms. Xiaoling Shu, a graduate student at Northwest Normal University, specializes in applying large language models and natural language processing for fault diagnosis in mine hoists. Her innovative research, including hyper-relational knowledge graphs and reinforcement learning, contributes significantly to advancements in fault prediction and analysis. Ms. Shu’s impactful work positions her as a deserving candidate for the Best Researcher Award.

Professional Development

Xiaoling Shu is continuously advancing her expertise in cutting-edge computational techniques, leveraging the power of large language models and NLP. Her work integrates artificial intelligence with industrial fault diagnostics, focusing on predictive algorithms and hyper-relational knowledge graphs. With an eye on technological evolution, she engages in workshops, seminars, and collaborations aimed at fostering innovation in hierarchical reinforcement learning. Xiaoling’s dedication to problem-solving has earned her a place among emerging experts in AI-driven industrial applications. Beyond her academic endeavors, she actively participates in cross-disciplinary exchanges to promote innovative thinking in fault diagnosis systems. 🚀🖥️

Research Focus

Xiaoling Shu’s research is centered on applying advanced computational models to optimize fault diagnosis systems for mine hoists. Her focus includes utilizing large language models to construct hyper-relational knowledge graphs, enabling precise and efficient fault analysis. She explores hierarchical reinforcement learning techniques to enhance decision-making in industrial operations and develops methodologies like HyperKGLinker for effective link prediction. Her work aligns with the broader goal of integrating AI with practical applications, addressing complex challenges in mining industries. Xiaoling’s innovative approach contributes to smarter, safer, and more reliable industrial systems. 🤖⚙️

Awards and Honors

  • 🏅 Best Research Contribution Award for advancements in NLP-based fault diagnostics.
  • 🏆 Innovation in AI Award for hyper-relational knowledge graph applications.
  • 🎖️ Outstanding Researcher for publications on hierarchical reinforcement learning.
  • 📜 Certificate of Excellence for contributions to link prediction methods.
  • 🌟 Technology Pioneer Award for integrating LLMs in industrial applications.

Publication Top Notes

  • 📘 “Utilizing Large Language Models for Hyper Knowledge Graph Construction in Mine Hoist Fault Analysis” – 2024, cited by 0,  ✍️
  • 📕 “Research on Fault Diagnosis of Mine Hoists Based on Hierarchical Reinforcement Learning” – 2024, cited by 0. 

Abdul-Majeed Al-Izeri | Data Science | Best Scholar Award

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Abdul-Majeed Al-Izeri | Data Science | Best Scholar Award

Dr. Abdul-Majeed Al-Izeri , Clermont Auvergne University, France.

Publication profile

Googlescholar

Education and Experience

  • 2020-2021: University degree in Data Science, University Clermont Auvergne, France. 🎓
  • 2013-2016: PhD in Mathematics (Mathematical analysis of PDEs), University Clermont Auvergne, France. 📜
  • 2011-2012: Master 2 in Mathematical Modelling (PDEs, calculation, epidemiology), University of Bordeaux, France. 💻
  • 2010-2011: Master 1 in Mathematics (Modelling, calculation, environment), University of Bordeaux, France. 📐
  • 2002-2006: BSc in Mathematics, University of Thamar, Yemen. 📘
  • October 2021-Present: Assistant Professor, Applied Mathematics, Clermont Auvergne University, France. 👩‍🏫
  • January 2018-July 2021: Postdoctoral Researcher in Epidemiology and PDEs, Clermont Auvergne University, France. 🔬
  • 2017: Postdoctoral Project in PDEs Dynamics, Clermont Auvergne University, France. 🧮
  • 2013-2016: Thesis Project in Mathematical Analysis of Population Dynamics, Blaise Pascal University, France. 🔍
  • 2012: Research Internship, Epidemic Model Study, University of Bordeaux, France. 💡
  • 2011: Project in Mathematical Modelling for Fishing Resources, University of Bordeaux, France. 🐟

Suitability For The Award

Dr. Abdul-Majeed Al-Izeri is indeed a highly suitable candidate for the Best Scholar Award based on his extensive academic qualifications, professional experience, and notable contributions to the field of Applied Mathematics and Data Science. His academic background, including a PhD in Mathematics with a specialization in Partial Differential Equations (PDEs), as well as a strong postdoctoral research profile, makes him a valuable asset in both academia and research communities.

Professional Development 

Dr. Al-Izeri has gained comprehensive skills in programming languages like Fortran, Matlab, Python, and R, along with proficiency in parallel computation using MPI. His expertise extends to using Latex and other office software for academic writing and presentations. He has been involved in several international research projects focused on applying mathematical theories to solve real-world problems in epidemiology and population dynamics. Dr. Al-Izeri’s ongoing commitment to improving his mathematical expertise and expanding his knowledge in data science and computational methods keeps him at the forefront of his field. 📊💻🔍

Research Focus 

Awards and Honors

  • 2021: Assistant Professor Appointment, Clermont Auvergne University, France. 🎓
  • 2016: PhD Completion, Mathematical Analysis of PDEs, University Clermont Auvergne. 🏆
  • 2012: Research Internship Excellence Award, University of Bordeaux. 🌟
  • 2011: Best Project in Mathematical Modelling for Resource Management, University of Bordeaux. 🏅

Publoication Top Notes

  1. On the solutions for a nonlinear boundary value problem modeling a proliferating cell population with inherited cycle length – AM Al-Izeri, K Latrach, Nonlinear Analysis: Theory, Methods & Applications 143, 1-18, Cited by 6, 2016 📘🧬
  2. Well-posedness of a nonlinear model of proliferating cell populations with inherited cycle length – ALI Abdul-Majeed, K Latrach, Acta Mathematica Scientia 36 (5), 1225-1244, Cited by 5, 2016 📊🧫
  3. Nonlinear semigroup approach to transport equations with delayed neutrons – ALI Abdul-Majeed, K Latrach, Acta Mathematica Scientia 38 (6), 1637-1654, Cited by 3, 2018 🔬⏳
  4. A nonlinear age-structured model of population dynamics with inherited properties – AM Al-Izeri, K Latrach, Mediterranean Journal of Mathematics 13, 1571-1587, Cited by 3, 2016 🌱🔢
  5. On the asymptotic spectrum of a transport operator with elastic and inelastic collision operators – AM Al-Izeri, K Latrach, Acta Mathematica Scientia 40, 805-823, Cited by 2, 2020 🔍🔄
  6. A note on fixed point theory for multivalued mappings – AM Al-Izeri, K Latrach, Fixed Point Theory 24 (1, 2023), 233-240, Cited by 1, 2023 📐📍

 

Mr. Yeonsoo Kim | AI Network Awards | Best Researcher Award

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Mr. Yeonsoo Kim | AI Network Awards | Best Researcher Award

Mr. Yeonsoo Kim, Surromind, South Korea

Yeonsoo Kim is a South Korean researcher specializing in AI and robotics development, with a particular focus on data analytics and autonomous systems. Currently, Yeonsoo is a researcher at Surromind in Seoul, where they contribute to advanced robotics and AI projects. With prior experience as a researcher at HnT in Suwon and an internship at the Korea Railroad Research Institute in Uiwang, Yeonsoo has developed a robust skill set in data-driven innovation. Yeonsoo holds degrees in engineering from Kyonggi University and has earned professional certifications in big data analysis and advanced data analytics. Recognized for their contributions to the AI field, Yeonsoo was awarded the ICONI 2023 Outstanding Paper award. Proficient in Python and ROS 2, they are committed to advancing automation and machine learning in applied technology. Yeonsoo’s background reflects a blend of technical expertise and innovation, making them a promising figure in the realm of AI and robotics.

Professional Profile:

Google Scholar

Summary of Suitability for the Award:

Yeonsoo Kim is a promising candidate for the Best Researcher Award due to her strong background in AI and robotics, relevant industry experience, and award-winning contributions to data analytics and advanced technology. Her work in AI and robotics, supported by her roles at prominent research institutions in South Korea, demonstrates her capability to advance these rapidly evolving fields.

🎓Education :

Yeonsoo Kim pursued their academic journey in engineering at Kyonggi University, South Korea. Beginning their studies in 2017, Yeonsoo completed a bachelor’s degree with a focus on engineering in early 2023 and is currently engaged in postgraduate studies, expected to finish in August 2024. Throughout their education, Yeonsoo specialized in areas integral to modern AI and robotics, such as big data analysis and autonomous systems, equipping them with the theoretical and practical skills necessary for advanced technological development. Their academic experience includes coursework and projects that emphasize AI applications, data analytics, and robotics programming, providing a comprehensive foundation for their research work. With an emphasis on interdisciplinary learning, Yeonsoo’s education has shaped their approach to real-world challenges in robotics and machine learning, further enhanced by certifications in advanced data analytics.

🏢Professional Experience :

Yeonsoo Kim has amassed valuable professional experience in AI, robotics, and data analytics. Currently, they work as a researcher at Surromind in Seoul, where they contribute to AI and robotics projects, integrating machine learning techniques to drive innovations in automation. Previously, Yeonsoo was a researcher at HnT in Suwon from 2022 to early 2024, focusing on the practical application of data analytics in industrial settings. Before that, they interned at the Korea Railroad Research Institute in Uiwang (2021-2022), where they gained hands-on experience in real-time data processing and control systems in transportation. Each role has enhanced their expertise in data-driven research and reinforced their commitment to developing AI-driven technologies. Yeonsoo’s work experience has honed their ability to integrate AI methodologies into various industrial applications, making significant contributions to the fields of robotics and big data.

🏅Awards and Honors :

Throughout their career, Yeonsoo Kim has been recognized for their achievements in AI and data analytics. They hold several notable certifications and awards, including the Engineer Big Data Analysis certification, which signifies their advanced skill in managing and interpreting complex datasets. In recognition of their expertise in data science, Yeonsoo earned the Advanced Data Analytics Semi-Professional (ADsP) certificate, a credential that underscores their proficiency in advanced analytics and statistical applications. Yeonsoo’s research contributions have also been acknowledged with the ICONI 2023 Outstanding Paper award, reflecting their ability to produce impactful, high-quality research in AI. These awards and certifications highlight Yeonsoo’s dedication to continuous learning and excellence in data analytics and artificial intelligence, positioning them as a forward-thinking researcher committed to pushing the boundaries of robotics and AI technology.

🔬Research Focus:

Yeonsoo Kim’s research is centered on the development of AI and robotics, with a focus on integrating big data analytics into autonomous systems. Their work encompasses both software and hardware aspects of robotics, with applications ranging from industrial automation to transportation technology. Yeonsoo’s current role at Surromind involves utilizing AI algorithms to enhance robotic functions, leveraging data analytics to optimize decision-making in autonomous systems. Their previous research at HnT and the Korea Railroad Research Institute focused on real-time data processing and implementing machine learning models for system control, showcasing their versatility across different sectors. With proficiency in Python and ROS 2, Yeonsoo develops scalable, data-driven solutions for complex robotic applications, aiming to make autonomous systems more efficient and adaptable. Their work is driven by a commitment to advancing AI as a transformative tool in robotics, with a special emphasis on data-informed system intelligence.

Publication Top notes:

Title: “Autoencoder-Based Cargo Recommendation System with Latent Factor Model”

Citations: 1

Title: “Deep Learning-Based Freight Recommendation System for Freight Brokerage Platform”
Title: “Real-Time Detection of Printing Defects with YOLOv5 Models”
Title: “Development of a Human-Following Transport Robot for Collaboration with Railway Workers”
Title: “Identifying Process Abnormalities through Real-Time Defect Detection”

 

 

Dr. Thomas Kotoulas | Artificial Intelligence Award | Best Researcher Award

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Dr. Thomas Kotoulas | Artificial Intelligence Award | Best Researcher Award

Dr. Thomas Kotoulas, Aristotle University of Thessaloniki, Greece, Greece

Dr. Thomas Kotoulas is a renowned physicist specializing in Newtonian dynamics and celestial mechanics. He has built a distinguished career in the study of dynamical systems, particularly the behavior of small bodies in the outer Solar System. He is currently a researcher at the University of Thessaloniki, where he earned his B.Sc. in Physics (1995) and Ph.D. in Physics (2003). Over the years, Kotoulas has become a key figure in the field of celestial mechanics, with numerous publications and contributions to the study of periodic orbits, stability, and resonance dynamics. His expertise extends to inverse problems in Newtonian dynamics and its applications in astronomy. Dr. Kotoulas has been awarded for his excellence as an external reviewer and continues to significantly contribute to the advancement of his research areas.

Professional Profile:

Google Scholar

Scopus

Summary of Suitability for Award:

Dr. Thomas Kotoulas is a strong contender for the Best Researcher Awards. His in-depth expertise, consistent scholarly output, contributions to high-impact research, leadership in projects, and acknowledgment from prestigious journals position him as a leading figure in the field of celestial mechanics. Given his outstanding research achievements and influential role in advancing scientific knowledge, Dr. Kotoulas is undoubtedly deserving of recognition as a top researcher in his field.

🎓Education: 

Dr. Kotoulas completed his B.Sc. in Physics at the Department of Physics at Aristotle University of Thessaloniki (A.U.Th.). He further pursued his postgraduate studies, culminating in a Ph.D. in Physics from the same department in 2003. His doctoral research focused on the dynamical evolution of small bodies in resonant areas within the outer Solar System, for which he received an excellent evaluation. His Ph.D. work was supervised by Professor John D. Hadjidemetriou. In addition to his academic qualifications, Dr. Kotoulas was awarded a fellowship from the National Foundation of Fellowships (Ι.Κ.Υ.) during his doctoral studies, where he specialized in dynamical systems and celestial mechanics. His academic journey was marked by excellence, shaping his future contributions to the scientific community in the fields of celestial mechanics and dynamics.

🏢Work Experience:

Dr. Kotoulas has accumulated extensive experience in the field of celestial mechanics and dynamical systems. He has worked on several significant research projects, including the “Dynamics of the restricted three-body problem and applications in Celestial Mechanics,” which was funded by the Greek Ministry of Education and the European Community. As a post-doctoral researcher, he contributed to the study of retrograde periodic orbits in the restricted three-body problem, focusing on applications in asteroids and the Kuiper Belt. Over the years, he has also served as a reviewer for several esteemed journals, such as “Celestial Mechanics and Dynamical Astronomy,” “Astrophysics and Space Science,” and “Research in Astronomy and Astrophysics.” His academic career is marked by his deep involvement in the application of inverse problems in Newtonian dynamics, which he continues to explore and develop through his research.

🏅Awards:

Dr. Thomas Kotoulas has received several prestigious awards and honors throughout his career. Notably, he was recognized as one of the best external reviewers for the journal “Research in Astronomy and Astrophysics” in 2022, receiving the Outstanding Reviewer Award for his valuable contributions. He also received a letter of recognition from Dr. Fabio Santos, the Publishing Editor of “Astrophysics and Space Science,” for his outstanding work as a reviewer during 2021 and 2022. Furthermore, Dr. Kotoulas was included in the Mathematical Reviews database, where he has written reviews for numerous papers on celestial mechanics. His work has been consistently acknowledged by the scientific community, affirming his expertise in dynamical systems and celestial mechanics. These honors highlight his significant contributions to the field, particularly in the areas of celestial mechanics, dynamics, and inverse problems.

🔬Research Focus:

Dr. Kotoulas’ primary research focus lies in the field of Newtonian dynamics and celestial mechanics, with an emphasis on the restricted three-body problem, orbital stability, and resonance dynamics. His research explores the dynamical evolution of small bodies, particularly in the outer Solar System, and how these bodies behave under the influence of resonances with larger celestial bodies. He specializes in the computation of families of periodic orbits, spectral analysis, and stability/instability in resonance regions. Additionally, Dr. Kotoulas works on inverse problems in Newtonian dynamics, applying them to astronomy and galactic dynamics. His work involves finding generalized force fields from families of orbits, as well as applying these techniques to improve our understanding of the structure and stability of orbital systems. Through his research, Dr. Kotoulas has significantly contributed to advancing theoretical models that describe the motion of celestial bodies and their dynamical interactions.

Publication Top Notes: 

  • “Planar Periodic Orbits in Exterior Resonances with Neptune”
    • Citations: 44
  • “Comparative Study of the 2:3 and 3:4 Resonant Motion with Neptune: An Application of Symplectic Mappings and Low Frequency Analysis”
    • Citations: 43
  • “On the Stability of the Neptune Trojans”
    • Citations: 34
  • “Symmetric and Nonsymmetric Periodic Orbits in the Exterior Mean Motion Resonances with Neptune”
    • Citations: 32
  • “On the 2/1 Resonant Planetary Dynamics–Periodic Orbits and Dynamical Stability”
    • Citations: 31

 

 

 

 

Mr. Tohid Sharifi | Machine Learning Award | Best Researcher Award

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Mr. Tohid Sharifi | Machine Learning Award | Best Researcher Award

Mr. Tohid Sharifi, Niroo Research Institute, Iran

Mr. Tohid Sharifi is a proficient electrical engineer with an M.Sc. in Electrical Machines and Power Electronics from Amirkabir University of Technology and a B.Sc. in Electrical Power Engineering from Urmia University. His research encompasses notable projects such as a hybrid estimation model for real-time temperature monitoring in electric motors, published in Case Studies in Thermal Engineering, and he is actively working on heat transfer investigations for advanced motor designs. With industrial experience as a CFD Specialist and Cooling System Design Engineer, he has contributed to thermal analysis for a 100kW flywheel energy storage system and optimized heat transfer for a 200kW water-cooled motor using artificial neural networks. His research interests include power electronics, electrical machines, electric vehicles, and metaheuristics, and he holds a patent for a hybrid excited flux switching permanent magnet motor for electric vehicle applications.

Professional Profile:

Orcid
Google Scholar

Suitability for the Best Researcher Award:

Mr. Tohid Sharifi’s extensive research and industrial contributions make him an ideal candidate for the Best Researcher Award. His focus on heat transfer and cooling systems for electric motors, coupled with his work in metaheuristic optimization for motor efficiency, reflects his forward-thinking approach to solving key challenges in power electronics and energy systems. His innovative contributions to electric vehicle motor design and the optimization of thermal systems using advanced algorithms showcase his potential for significant future impact in the field.

🎓 Education:

Mr. Tohid Sharifi holds an M.Sc. in Electrical Machines and Power Electronics from Amirkabir University of Technology (Tehran Polytechnic) and a B.Sc. in Electrical Power Engineering from Urmia University.

🛠️ Academic Projects:

His research includes significant projects such as a hybrid estimation model for real-time temperature monitoring in electric motors, published in Case Studies in Thermal Engineering. He has also worked on heat transfer investigations for advanced motor designs, with papers under revision in prominent journals.

🏭 Industrial Experience:

In the industrial sector, Mr. Sharifi has contributed as a CFD Specialist and Cooling System Design Engineer for electric motors. He played a crucial role in thermal analysis for a 100kW flywheel energy storage system at Niroo Research Institute and optimized heat transfer for a 200kW water-cooled motor using artificial neural networks.

🔍 Research Focus:

His research interests lie in power electronics, electrical machines, electric vehicles, metaheuristics, and heat transfer. He is also an inventor, with a patented hybrid excited flux switching permanent magnet motor for electric vehicle applications.

Publication Top Notes:

  • “An asymmetrical cascaded single-phase quasi Z-source multilevel inverter with reduced number of switches and lower THD”
    • Citations: 9
    • Published: 2020
  • “Optimal design of a synchronous reluctance motor using biogeography-based optimization”
    • Citations: 5
    • Published: 2021
  • “Optimal Design of a Permanent Magnet Synchronous Motor Using the Cultural Algorithm”
    • Citations: 4
    • Published: 2021
  • “Analytical Modeling and Electrical Equivalent Circuit Extraction for a Flux Switching PM Motor for EVs”
    • Citations: 3
    • Published: 2022
  • “Torque Ripple Minimization for a Switch Reluctance Motor Using the Ant Lion Optimization Algorithm”
    • Citations: 2
    • Published: 2022