Zhengyi Yao | Artificial Intelligence | Best Researcher Award

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Mr. Zhengyi Yao | Artificial Intelligence | Best Researcher Award

Sichuan Normal University |China

Zhengyi Yao, from Neijiang, Sichuan, China, is a dedicated researcher affiliated with Sichuan Normal University, holding both bachelor’s and master’s degrees in Computer Science and Technology. His work primarily focuses on the Internet of Things (IoT), cybersecurity, cryptography, and artificial intelligence (AI). With a growing presence in academic publishing, he has contributed to several high-impact journals indexed in SCI and Scopus. Mr. Yao has demonstrated a strong commitment to advancing secure, intelligent systems, particularly in logistics and industrial applications. His interdisciplinary approach blends theoretical research with practical implementation, contributing to emerging technologies such as blockchain-enabled IIoT and quantum cryptography. In addition to publishing five journal articles and securing seven patents, he actively contributes to the field through applied innovations aimed at enhancing privacy protection and data security. As a passionate technologist, Mr. Yao is continually exploring transformative solutions in smart systems, emphasizing the ethical and secure integration of AI in modern digital infrastructure.

Profile

Education

Zhengyi Yao completed his academic training at Sichuan Normal University, earning both his bachelor’s and master’s degrees in Computer Science and Technology. His undergraduate studies provided a solid foundation in software development, algorithms, and system architecture, while his postgraduate work emphasized advanced topics such as artificial intelligence, cybersecurity, and cryptographic methods. During his graduate years, he engaged deeply with interdisciplinary studies, aligning computer science with real-world applications in logistics, IoT, and secure communication systems. His academic performance has been marked by consistent excellence and a proactive engagement in research-driven projects. While enrolled, he also explored the practical aspects of emerging technologies, developing tools and frameworks to support digital transformation in industrial systems. His education has been instrumental in shaping his scientific outlook, fostering a commitment to ethical innovation and robust digital security. These academic experiences continue to inform his contributions to academic research and patent development in the tech and security domains.

Experience

Zhengyi Yao has gained substantial experience as a researcher and innovator in the fields of IoT, cybersecurity, cryptography, and AI. While at Sichuan Normal University, he actively participated in multiple collaborative research efforts that examined the integration of blockchain with IIoT systems and privacy-focused AI applications in logistics. Despite limited consultancy and editorial appointments, his practical contributions are demonstrated through five SCI/Scopus-indexed journal publications and seven patents. He has co-authored research tackling challenges in smart logistics security, 5G-based blockchain sensors, and quantum cryptography, showcasing his capability to bridge theoretical and applied computing. Through independent and team-driven efforts, Mr. Yao has contributed to designing secure systems that support data integrity and user privacy in dynamic industrial environments. His hands-on research experience, supported by solid academic training, underpins his drive to innovate in secure computing technologies and has positioned him as a promising young professional in China’s growing digital research landscape

Research Focus

Zhengyi Yao’s research centers on the intersection of emerging technologies like IoT, blockchain, AI, and cybersecurity, with a strong focus on intelligent logistics systems. He explores secure device communication, privacy-preserving data protocols, and cryptographic models for industrial systems. His work on blockchain-enabled IIoT platforms aims to fortify command operations against cyber threats, while his investigations into quantum cryptography are pushing the boundaries of next-generation digital security. One of his key contributions is the development of 5G-based universal blockchain smart sensors, combining speed, scalability, and trust for dynamic logistics applications. His research also examines how AI can be ethically and securely integrated into cyber-physical environments to optimize data flow, user privacy, and system integrity. Through published works and patented innovations, he is shaping solutions to critical security challenges facing smart logistics and industrial platforms. His forward-thinking approach promotes safer, more resilient infrastructures in an increasingly connected digital world.

Publications

Sensitive Data Privacy Protection of Carrier in Intelligent Logistics System
Year: 2024
Citation:2

Blockchain-enabled device command operation security for Industrial Internet of Things
Year: 2023
Citation:12

5G-BSS: 5G-Based Universal Blockchain Smart Sensors
Year: 2022
Citation:2

Conclusion

Zhengyi Yao exemplifies the qualities of a dedicated and innovative researcher, with notable contributions to smart logistics, cybersecurity, and cryptographic technologies. His blend of academic rigor and applied invention positions him as a rising leader in secure digital systems.

Kihwan Nam | Artificial Intelligence | Best Faculty Award

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Prof. Kihwan Nam | Artificial Intelligence | Best Faculty Award

Dr. Kihwan Nam is an Assistant Professor in the Department of Management of Technology at Korea University and the founder of Aimtory, a high-technology AI company. With a unique blend of academic expertise and entrepreneurial insight, he specializes in Artificial Intelligence (AI), particularly Generative AI, Explainable AI, and Digital Transformation. He earned his Ph.D. in Information Systems from KAIST and holds degrees in Industrial Engineering and Statistics from Korea University and Yonsei University, respectively. Dr. Nam has an extensive research record, with publications in top-tier journals such as Journal of Marketing Research, Decision Support Systems, and Knowledge-Based Systems. His professional journey includes leadership roles in startups and significant AI industry contributions. He is passionate about bridging the gap between academia and industry through impactful, data-driven solutions that transform business strategies, smart factories, and healthcare systems. Dr. Nam is a leading figure in the fusion of cutting-edge AI technologies with business innovation.

Profile

🎓 Education

Dr. Kihwan Nam’s academic background spans statistics, engineering, and management. He holds a Ph.D. in Information Systems and Management Engineering from the prestigious KAIST College of Business, where he honed his expertise in AI-driven decision support and business analytics. Prior to his doctorate, he completed his M.S. in Industrial Engineering at Korea University, acquiring strong analytical and system optimization skills. His academic journey began with a B.A. in Statistics from Yonsei University, which laid a solid foundation in data analysis and quantitative modeling. This interdisciplinary academic training enables Dr. Nam to approach complex problems from technical, managerial, and data-driven perspectives. Throughout his studies, he cultivated a deep interest in predictive modeling, econometrics, and the integration of AI technologies in organizational contexts, which continues to shape his academic and industrial research today. His educational path reflects a consistent commitment to excellence and innovation across disciplines.

🧪 Experience

Dr. Nam has a dynamic career in both academia and industry. He currently serves as Assistant Professor in the Management of Technology at Korea University, following a faculty role in Management Information Systems at Dongguk University. In industry, he is the founder of Aimtory, a company focused on cutting-edge AI solutions, and previously led Basbai, an AI solution firm, as CEO. He also co-founded Sentience, reflecting his commitment to tech entrepreneurship. His dual roles have enabled him to conduct collaborative research with top-tier companies, implement AI in real-world applications, and train future innovators. Dr. Nam’s expertise extends across AI project development, big data analytics, and digital business transformation. His work in areas like smart factories, healthcare, and financial markets underscores his versatility. His diverse experience positions him as a thought leader at the intersection of research, innovation, and enterprise AI deployment.

🏅 Awards and Honors

Dr. Kihwan Nam has received numerous prestigious accolades for his impactful research and innovation. He was honored with the Best Paper Award from the Korea Intelligent Information System Society (2017) for his work on recommender systems in retail, and again in 2019 by the Information Systems Review Society for a field experiment in recommendation design. His deep learning-based financial distress prediction study was a Best Paper Nominee at the INFORMS Data Science Workshop (2020). In 2022, he secured top honors at the Korea Gas Corporation Big Data Competition and received an innovation award from the Startup Promotion Agency for the Big-Star Solution Platform. In 2023, he earned the Best Researcher Award at Dongguk University. These recognitions reflect his excellence in both theoretical contributions and practical applications of AI, reinforcing his role as a leading figure in AI-driven business analytics and intelligent systems research.

🔬 Research Focus

Dr. Nam’s research lies at the intersection of Artificial Intelligence, Business Analytics, and Digital Transformation. He specializes in Generative AI, Explainable AI, LLMs, NLP, and Computer Vision, aiming to drive intelligent decision-making in sectors like healthcare, finance, and manufacturing. His core research explores predictive analytics, recommender systems, robot advisory, and econometric modeling applied to real-world business and technological challenges. By incorporating econometrics with data mining and machine learning, he investigates user behavior, personalization strategies, and large-scale business optimization. His recent projects include stock and cryptocurrency prediction, smart factory optimization, and curated recommendation engines. He is also advancing research in digital transformation (DX) and blockchain-based token economies. Dr. Nam emphasizes bridging theory and application by applying AI innovations to actual business environments, often in collaboration with international enterprises. His work is deeply rooted in the integration of robust statistical methods with scalable, real-world AI systems.

✅ Conclusion

Dr. Kihwan Nam is a visionary academic and AI entrepreneur who merges deep theoretical knowledge with practical applications, shaping the future of AI-driven digital transformation across industries through innovative research, impactful teaching, and real-world solutions

Publications

Mustaqeem Khan | Deep learning | Best Researcher Award

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Assist. Prof. Dr. Mustaqeem Khan | Deep learning | Best Researcher Award

Dr. Mustaqeem Khan is an accomplished researcher and educator specializing in speech and video signal processing, with a keen focus on emotion recognition using deep learning. Recognized among the Top 2% Scientists globally (2023–2024), he currently serves as an Assistant Professor at the United Arab Emirates University (UAEU). He earned his Ph.D. in Software Convergence from Sejong University, South Korea, and has authored over 40 high-impact publications in IEEE, Elsevier, Springer, and ACM. His contributions span multimodal systems, computer vision, and intelligent surveillance. With extensive experience in academia and research labs, Dr. Khan has also served as a lab coordinator, team leader, and guest editor. He actively collaborates internationally and mentors graduate students. His technical expertise includes TensorFlow, PyTorch, MATLAB, and computer vision frameworks, making him a key contributor to projects involving emotion detection, UAV surveillance, and medical imaging. He brings innovation, leadership, and academic excellence to his roles.

Profile

🎓 Education

Dr. Mustaqeem Khan holds a Ph.D. in Software Convergence (2022) from Sejong University, Seoul, South Korea, where he achieved an outstanding CGPA of 4.44/4.5 (98%) and earned the Outstanding Research Award. His doctoral dissertation focused on advanced studies in speech-based emotion recognition using deep learning. He completed his MS in Computer Science (2018) at Islamia College Peshawar with a Gold Medal, securing a CGPA of 3.94/4.00, and specialized in video-based human action recognition. His undergraduate degree (BSCS, 2015) was from the Institute of Business and Management Sciences, AUP Peshawar, where he developed a web-based design project. His academic background laid the foundation for his research in multimodal deep learning, AI, and signal processing. Throughout his education, Dr. Khan combined rigorous coursework with impactful research, leading to numerous publications and international recognition.

🧪 Experience

Dr. Mustaqeem Khan is currently serving as an Assistant Professor at UAEU (2025–Present), focusing on teaching, research, and student supervision. From 2022 to 2024, he was a Postdoctoral Fellow and Lab Coordinator at MBZUAI, where he led AI projects like drone surveillance and collaborated with the Technical Innovation Institute. At Sejong University (2019–2022), he worked as a Research Assistant and IT Lab Coordinator, guiding projects and mentoring graduate students in speech processing and energy informatics. Prior to this, he was a Lecturer (2018–2019) and Research Assistant (2016–2018) at Islamia College Peshawar, where he taught courses in programming, image processing, and AI. He also led computer vision and speech analytics projects. His international collaborations span institutes in South Korea, France, Saudi Arabia, and India, highlighting his global academic footprint. Dr. Khan is deeply involved in editorial roles and research supervision, embodying academic excellence and research leadership.

🏅 Awards and Honors

Dr. Mustaqeem Khan has been recognized as one of the Top 2% Scientists in the world (2023–2024), a testament to his research impact. He received the Outstanding Research Award from Sejong University in 2022 and was a Gold Medalist during his MS in Computer Science at Islamia College Peshawar (2016–2018). His work has earned multiple Best Paper Awards, including from the Korea Information Processing Society (2021) and Mathematics Journal (2020). He was also granted a fully funded Ph.D. scholarship at Sejong University. Dr. Khan has reviewed for over 35 reputed international journals and serves as an editor and guest editor for several leading publications, including MDPI, IEEE, and Springer journals. His patents in speech-based emotion recognition further validate his innovation. These accolades underscore his academic rigor, global recognition, and leadership in signal processing, AI, and intelligent systems.

🔬 Research Focus

Dr. Mustaqeem Khan’s research lies at the intersection of speech signal processing, multimodal emotion recognition, and computer vision. His Ph.D. work established a foundation for deep learning-based systems capable of understanding human emotions through speech. He has since expanded his research to include age/gender detection, action recognition, violence detection, and medical image analysis using AI. His deep learning models—ranging from CNNs to transformers—have been applied across audio, video, text, and sensor-based data. Dr. Khan is particularly interested in cross-modal transformer-based architectures, edge-AI surveillance systems, and emotion recognition for smart cities. He is also exploring medical AI for fetal, retinal, and Parkinson’s disease diagnostics. His work is published in top-tier venues like IEEE Transactions, Nature Scientific Reports, and ACM. Ongoing collaborations with MBZUAI, TII, and Korean institutions focus on real-time AI applications in UAV systems, smart healthcare, and metaverse content generation.

✅ Conclusion

Dr. Mustaqeem Khan is a globally recognized AI researcher and educator specializing in multimodal emotion recognition and computer vision, whose impactful contributions, international collaborations, and innovative deep learning applications continue to shape the fields of signal processing, smart surveillance, and healthcare technologies.

Publications

Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

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Dr. Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

Dr. Chan-Uk Yeom is a Research Professor at the Research Institute of IT, Chosun University, Korea. He specializes in time series data analysis using deep learning, granular computing, adaptive neuro-fuzzy inference systems, high-dimensional data clustering, and biosignal-based biometrics. Dr. Yeom has held several research positions, including at the Division of AI Convergence College at Chosun University and the Center of IT-BioConvergence System Agriculture at Chonnam National University. His work integrates artificial intelligence, fuzzy systems, and granular models for practical applications such as healthcare, biometrics, and energy efficiency. Dr. Yeom has published extensively in high-impact journals and conferences, holds multiple patents, and has received numerous awards for his innovative research contributions. He actively teaches courses related to AI healthcare applications and electronic engineering. His collaboration and problem-solving skills have been demonstrated through his involvement in competitive AI research challenges and global innovation camps.

Professional Profile

Education

Dr. Yeom completed his entire higher education at Chosun University, Korea. He earned his Ph.D. in Engineering (2022) from the Department of Control and Instrumentation Engineering, with a dissertation on fuzzy-based granular model design using hierarchical structures under the supervision of Prof. Keun-Chang Kwak. Prior to this, he obtained his M.S. in Engineering (2017), focusing on ELM predictors using TSK fuzzy rules and random clustering, and his B.S. in Engineering (2016) in Control and Instrumentation Robotics. His academic work laid a strong foundation in machine learning, granular computing, and fuzzy inference systems, which became the core of his future research trajectory. Throughout his education, Dr. Yeom demonstrated academic excellence, leading to multiple thesis awards, and developed expertise in AI-driven applications for healthcare, energy optimization, and biometrics.

Experience

Currently, Dr. Yeom serves as a Research Professor at the Research Institute of IT, Chosun University (since January 2025). Previously, he was a Research Professor at Chosun University’s Division of AI Convergence College (2023–2024) and a Postdoctoral Researcher at the Center of IT-BioConvergence System Agriculture, Chonnam National University (2022–2023). His extensive research spans user authentication technologies using multi-biosignals, brain-body interface development using AI multi-sensing, and optimization of solar-based thermal storage systems. In addition to research, Dr. Yeom has contributed to teaching undergraduate courses, including AI healthcare applications, electronic experiments, capstone design, and open-source software. He is also experienced in mentorship, student internships, and providing special employment lectures. His active participation in national and international research projects and conferences reflects his global engagement and multidisciplinary expertise in artificial intelligence, healthcare, biometrics, and advanced fuzzy models.

Research Interests

Dr. Yeom’s research integrates deep learning, granular computing, and adaptive neuro-fuzzy systems to solve complex problems in healthcare, biometrics, energy efficiency, and time series data analysis. His innovative work focuses on designing hierarchical fuzzy granular models, developing incremental granular models with particle swarm optimization, and applying AI-driven methods to biosignal-based biometric authentication. Dr. Yeom has developed cutting-edge models for predicting energy efficiency, vehicle fuel consumption, water purification processes, and disease classification from ECG signals. His contributions also extend to explainable AI, emotion recognition, and non-contact biosignal acquisition using 3D-CNN. In addition to academic publications, he has secured multiple patents related to ECG-based personal identification methods, intelligent prediction systems, and granular neural networks. His interdisciplinary approach combines theoretical modeling, real-world applications, and collaborative AI system design, advancing the fields of biomedical informatics, neuro-fuzzy computing, and healthcare convergence technologies.

Awards

Dr. Yeom has received numerous awards recognizing his academic excellence. He earned multiple Excellent Thesis Awards from prestigious conferences, including the International Conference on Next Generation Computing (ICNGC 2024), the Korea Institute of Information Technology (KIIT Autumn Conference 2024), and the Annual Conference of Korea Information Processing Society (ACK 2024). His doctoral work was recognized at Chosun University’s 2021 Graduate School Doctoral Degree Award Ceremony. He also received the Outstanding Presentation Paper Award at the 2020 Korean Smart Media Society Spring Conference and the Excellent Thesis Award at the Korea Information Processing Society 2018 Spring Conference. Earlier, his problem-solving capabilities were showcased as a finalist and top 9 team at the 2018 AI R&D Challenge and during participation in the 2016 Global Entrepreneurship Korea Camp. These honors highlight his sustained contributions to AI research, innovation, and applied technological development.

Conclusion

Dr. Chan-Uk Yeom is a dynamic researcher whose pioneering contributions to granular computing, neuro-fuzzy systems, and AI healthcare applications demonstrate his exceptional expertise, innovative thinking, and global scientific impact, making him a valuable contributor to the advancement of next-generation intelligent systems.

 Publications

  • A Design of CGK-Based Granular Model Using Hierarchical Structure

    Applied Sciences
    2022-03 | Journal article | Author
    DOI: 10.3390/app12063154
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

  • Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

    Applied Sciences
    2020-11 | Journal article | Author
    DOI: 10.3390/app10238495
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

Chongyuan Wang | Deep learning | Best Researcher Award

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Dr. Chongyuan Wang | Deep learning | Best Researcher Award

Dr. Chongyuan Wang, a Ph.D. researcher at Hohai University, specializes in artificial intelligence 🤖 and neural computation 🧠. He completed his B.S. at Jiangsu University 🇨🇳 and M.S. in Energy and Power from Warwick University 🇬🇧. His research journey is centered around biologically inspired learning algorithms, with notable contributions to dendritic neuron modeling and evolutionary optimization. Through innovative algorithms like Reinforced Dynamic-grouping Differential Evolution (RDE), Dr. Wang advances the understanding of synaptic plasticity in AI systems. His patent filings and international publications reflect a strong commitment to academic innovation and impact 🌍.

Profile

Education 🎓

🎓 B.S. in Engineering – Jiangsu University, China 🇨🇳
🎓 M.S. in Energy and Power – University of Warwick, UK 🇬🇧 (2018)
🎓 Ph.D. Candidate – Hohai University, majoring in Artificial Intelligence 🤖
Dr. Wang’s educational path bridges engineering and intelligent systems. His strong technical foundation and global exposure foster advanced thinking in machine learning and neuroscience. His current doctoral research integrates deep learning, dendritic neuron models, and biologically plausible architectures for improved learning accuracy and model efficiency. 📘🧠

Experience 👨‍🏫

Dr. Wang is currently pursuing his Ph.D. at Hohai University, where he investigates dendritic learning algorithms and synaptic modeling. 🧬 He proposed the RDE algorithm, enhancing dynamic learning in artificial neurons. His hands-on experience includes research design, algorithm optimization, patent writing, and international publication. He has contributed to projects such as “Toward Next-Generation Biologically Plausible Single Neuron Modeling” and “RADE for Lightweight Dendritic Learning.” 📊 His work balances theoretical depth and applied research, particularly in neural computation, classification systems, and resource-efficient AI. 🔬💡

Awards & Recognitions 🏅

🏅 Patent Holder (CN202410790312.0, CN202410646306.8, CN201510661212.9)
📄 Published in SCI-indexed journal Mathematics (MDPI)
🌐 Recognized on ORCID (0009-0002-6844-1446)
🧠 Nominee for Best Researcher Award 2025
His inventive research has earned him national patents and global visibility. His SCI publications in computational modeling reflect both novelty and academic rigor. His continued innovation in biologically inspired AI learning systems has established his position as an emerging researcher in intelligent systems. 🚀📘

Research Interests 🔬

Dr. Wang’s research fuses deep learning 🤖 and dendritic modeling 🧠 to create biologically plausible AI. He developed the RDE algorithm to mimic synaptic plasticity, improving convergence and adaptability in neural networks. His research areas include evolutionary optimization, adaptive grouping, resource-efficient models, and dendritic learning. He explores how artificial neurons can reflect real-brain behavior, leading to faster, more accurate AI systems. Current projects like RADE aim to make AI lightweight and biologically relevant. 🌱📊 His vision is to bridge the gap between neuroscience and AI through interpretable, high-performance algorithms. 🧠💡

Publications

  • Toward Next-Generation Biologically Plausible Single Neuron Modeling: An Evolutionary Dendritic Neuron Model

    Mathematics
    2025-04-29 | Journal article
    DOI: 10.3390/math13091465
    CONTRIBUTORS: Chongyuan Wang; Huiyi Liu

Farshad Sadeghpour | Data prediction | Best Researcher Award

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Dr. Farshad Sadeghpour | Data prediction | Best Researcher Award

Farshad Sadeghpour (b. 1996) 🇮🇷 is a Petroleum Engineer and Data Scientist 💻🛢️ with expertise in reservoir engineering, petrophysics, and AI applications in the energy sector. Based in Tehran, Iran 📍, he holds a Master’s and Bachelor’s in Petroleum Exploration. With extensive experience in EOR, SCAL/RCAL analysis, and machine learning, Farshad has contributed to both academic and industrial R&D at RIPI, NISOC, and PVP. He has published multiple research articles 📚, won international awards 🏆, and participated in key petroleum projects. He served in the military 🪖 and actively collaborates with academia and industry on AI-driven energy solutions.

Profile

Education 🎓

🧑‍🎓 Master’s in Petroleum Engineering (Petroleum Exploration), Petroleum University of Technology, Abadan 🇮🇷 (2019–2022) | GPA: 18.82/20
🎓 Bachelor’s in Petroleum Engineering, Islamic Azad University (Science & Research Branch), Tehran 🇮🇷 (2015–2019) | GPA: 19.14/20
📚 Courses covered include reservoir engineering, geomechanics, well-logging, and advanced data analytics.
🛠️ Projects include CO₂ storage modeling, permeability prediction via AI, and LWD-based mud loss forecasting.
📊 Developed key industry collaborations with NISOC, RIPI, and OEID through thesis, internships, and military service projects.
💡 Honed computational and simulation skills using MATLAB, Python, COMSOL, Petrel, and ECLIPSE.
🏛️ Academic mentors: Dr. Seyed Reza Shadizadeh, Dr. Bijan Biranvand, Dr. Majid Akbari.

Experience 👨‍🏫


🔬 Computer Aided Process Engineering (CAPE) – Petroleum Reservoir Engineer (Nov 2024–Present)
🛢️ Petro Vision Pasargad – Reservoir Engineer & Lab Operator (Sep 2023–May 2024)
🧠 Research Institute of Petroleum Industry (RIPI) – Petroleum Engineer, Data Scientist (Mar 2023–Apr 2024)
🏭 National Iranian South Oil Company (NISOC) – Petroleum Engineer, Petrophysicist (Mar 2021–Nov 2024)
🧪 Internships: NIOC – Exploration Management, Oil & Energy Industries Development (OEID)
📊 Key contributions include EOR analysis, SCAL/RCAL lab testing, permeability modeling, machine learning pipelines, and field data analysis.
🧾 Delivered reports, simulations, and AI models supporting production optimization and reservoir characterization.

Awards & Recognitions 🏅

🥉 3rd Prize Winner – EAGE Laurie Dake Challenge 2022 (Madrid, Spain) 🌍
🎖️ Recognized for thesis excellence in AI-driven mud loss prediction with NISOC collaboration
📌 Acknowledged during military service project with RIPI for developing ANN-based well log models
🏅 Published in high-impact journals such as Energy, Geoenergy Science and Engineering, and JRMGE
✍️ Co-author of multiple peer-reviewed papers and under-review articles across petroleum engineering disciplines
🔬 Worked alongside top researchers including Dr. Ostadhassan, Dr. Gao, and Dr. Hemmati-Sarapardeh
🛠️ Actively participated in multidisciplinary teams combining AI, geomechanics, and petrophysics
📢 Regular presenter and contributor at petroleum conferences and AI-in-energy seminars.

Research Interests 🔬

📌 AI & ML applications in petroleum engineering 🧠🛢️ – including ANN, genetic algorithms, and deep learning
📊 Mud loss zone prediction, formation permeability modeling, CO₂ storage feasibility using ML
🧪 Experimental rock mechanics: nanoindentation, geomechanical upscaling, SCAL/RCAL testing
📈 Petrophysical property estimation in carbonate and unconventional reservoirs
🌍 Reservoir simulation, LWD analysis, and smart data integration using Python, Petrel, COMSOL
📖 Notable studies include: elastic modulus upscaling, kerogen behavior under pyrolysis, RQI/FZI modeling
🔬 Interdisciplinary projects bridging data science with geoscience and reservoir engineering
🤝 Collaboration with academic and industry leaders to develop practical, AI-driven solutions for energy challenges.

Publications 

  • Elastic Properties of Anisotropic Rocks Using an Stepwise Loading Framework in a True Triaxial Testing Apparatus

    Geoenergy Science and Engineering
    2025-04 | Journal article
    DOI: 10.1016/j.geoen.2025.213883
    CONTRIBUTORS: Farshad Sadeghpour; Hem Bahadur Motra; Chinmay Sethi; Sandra Wind; Bodhisatwa Hazra; Ghasem Aghli; Mehdi Ostadhassan

  • Storage Efficiency Prediction for Feasibility Assessment of Underground CO2 Storage: Novel Machine Learning Approaches

    Energy
    2025-04 | Journal article
    DOI: 10.1016/j.energy.2025.136040
    CONTRIBUTORS: Farshad Sadeghpour

  • A new petrophysical-mathematical approach to estimate RQI and FZI parameters in carbonate reservoirs

    Journal of Petroleum Exploration and Production Technology
    2025-03 | Journal article
    DOI: 10.1007/s13202-025-01952-6
    CONTRIBUTORS: Farshad Sadeghpour; Kamran Jahangiri; Javad Honarmand

  • Effect of stress on fracture development in the Asmari reservoir in the Zagros Thrust Belt

    Journal of Rock Mechanics and Geotechnical Engineering
    2024-11 | Journal article
    DOI: 10.1016/j.jrmge.2023.12.027
    CONTRIBUTORS: Ghasem Aghli; Babak Aminshahidy; Hem Bahadur Motra; Ardavan Darkhal; Farshad Sadeghpour; Mehdi Ostadhassan

  • Comparison of geomechanical upscaling methods for prediction of elastic modulus of heterogeneous media

    Geoenergy Science and Engineering
    2024-08 | Journal article
    DOI: 10.1016/j.geoen.2024.212915
    CONTRIBUTORS: Farshad Sadeghpour; Ardavan Darkhal; Yifei Gao; Hem B. Motra; Ghasem Aghli; Mehdi Ostadhassan

Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

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Dr. Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

Yuanming Zhang is an Associate Professor at the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. He earned his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His research focuses on data processing, graph neural networks, knowledge graphs, prognostics, health management, and condition monitoring. With expertise in deep learning and artificial intelligence, he has contributed significantly to neural network advancements. His work integrates cutting-edge technologies for intelligent data analysis and predictive maintenance. 📊🧠🔍

Profile

Education 🎓

Yuanming Zhang obtained his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His academic journey emphasized computational intelligence, machine learning, and advanced data analytics. He developed expertise in deep learning models, including convolutional and graph neural networks. His education laid a strong foundation for interdisciplinary research, integrating artificial intelligence with real-world applications. 📚🧑‍🎓📈

Experience 👨‍🏫

Yuanming Zhang has been an Associate Professor at Zhejiang University of Technology since completing his Ph.D. in 2010. His professional journey spans over a decade in academia, focusing on AI, neural networks, and knowledge graphs. He has supervised research projects, collaborated on industry applications, and contributed to advancements in predictive analytics and condition monitoring. His expertise extends to teaching, mentoring, and interdisciplinary AI applications. 🏫🤖📡

Research Interests 🔬

Yuanming Zhang specializes in deep learning, attention mechanisms, graph neural networks, and AI-driven predictive analytics. His research explores neural architectures for data processing, knowledge representation, and condition monitoring. His expertise spans convolutional networks, LSTMs, GRUs, and deep belief networks. His work contributes to advancements in AI-driven diagnostics, intelligent systems, and real-time health monitoring applications. 🧠📊🖥️

Awards & Recognitions 🏅

Yuanming Zhang has received recognition for his contributions to AI, machine learning, and data analytics. His work in deep learning and knowledge graphs has earned him accolades from research institutions and conferences. His papers in neural networks and predictive maintenance have been highly cited, solidifying his impact in the field. His research excellence has been acknowledged through grants and academic distinctions. 🎖️📜🔬

Publications 

 

Mudassar Raza | Machine Learning | Best Researcher Award

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Prof. Mudassar Raza | Machine Learning | Best Researcher Award

Prof. Dr. Mudassar Raza is a leading AI researcher and academician, serving as a Professor at Namal University, Mianwali, Pakistan. He is a Senior IEEE Member, Chair Publications of IEEE Islamabad Section, and an Academic Editor for PLOS ONE. With 20+ years of teaching and research experience, he has worked at HITEC University Taxila and COMSATS University Islamabad. His research spans AI, deep learning, image processing, and cybersecurity. He has published 135+ research papers with a cumulative impact factor of 215+, 6066+ citations, an H-index of 44, and an I-10 index of 93. He was listed in Elsevier’s World’s Top 2% Scientists (2023) and ranked #11 in Computer Science in Pakistan. Dr. Raza has supervised 3 PhDs, co-supervising 6 more, and mentored 100+ undergraduate R&D projects. He actively contributes to academia, industry collaborations, and curriculum development while serving as a reviewer for prestigious journals. 🌍📖

Profile

Education 🎓

  • Ph.D. in Control Science & Engineering (2014-2017) – University of Science & Technology of China (USTC), China 🇨🇳
    • Specialization: Pattern Recognition & Intelligent Systems
  • MS (Computer Science) (2009-2010) – Iqra University, Islamabad, Pakistan 🇵🇰
    • CGPA: 3.64 | Specialization: Image Processing
  • MCS (Master of Computer Science) (2004-2006) – COMSATS Institute of Information Technology, Pakistan
    • CGPA: 3.24 | 80% Marks
  • BCS (Bachelor in Computer Science) (1999-2003) – Punjab University, Lahore, Pakistan
    • CGPA: 3.28 | 64.25% Marks
  • Higher Secondary (Pre-Engineering) – Islamabad College for Boys
  • Matriculation (Science) – Islamabad College for Boys
    Dr. Raza’s academic journey is marked by top-tier universities and a strong focus on AI, pattern recognition, and cybersecurity. 🎓📚

Experience 👨‍🏫

  • Professor (2024-Present) – Namal University, Mianwali
    • Teaching AI, Cybersecurity, and Research Supervision
  • Associate Professor/Head AI & Cybersecurity Program (2023-2024) – HITEC University, Taxila
    • Led AI & Cybersecurity programs, supervised PhDs, and organized industry-academic collaborations
  • Associate Professor (2023) – COMSATS University, Islamabad
  • Assistant Professor (2012-2023) – COMSATS University, Islamabad
  • Lecturer (2008-2012) – COMSATS University, Islamabad
  • Research Associate (2006-2008) – COMSATS University, Islamabad
    Dr. Raza has 20+ years of experience in academia, R&D, and industry collaborations, contributing significantly to AI, deep learning, and cybersecurity. 🏫📊

Research Interests 🔬

Prof. Dr. Mudassar Raza’s research revolves around Artificial Intelligence, Deep Learning, Computer Vision, Image Processing, Cybersecurity, and Parallel Programming. His work includes pattern recognition, intelligent systems, visual robotics, and AI-driven cybersecurity solutions. With 135+ international publications, he has significantly contributed to AI’s real-world applications. His research impact includes 6066+ citations, an H-index of 44, and an I-10 index of 93. He leads multiple AI research groups, supervises PhD/MS students, and actively collaborates with industry and academia. His work is frequently cited, placing him among the top AI researchers globally. As an IEEE Senior Member and a PLOS ONE Academic Editor, he is a key figure in AI-driven innovations and technology advancements. 🧠📊

  • National Youth Award 2008 by the Prime Minister of Pakistan for contributions to Computer Science 🎖️
  • Listed in World’s Top 2% Scientists (2023) by Elsevier 🌍
  • Ranked #11 in Computer Science in Pakistan by AD Scientific Index 📊
  • Senior IEEE Member (ID: 91289691) 🔬
  • HEC Approved PhD Supervisor 🎓
  • Best Research Productivity Awardee at COMSATS University multiple times 🏆
  • Recognized by ResearchGate with a Research Interest Score higher than 97% of members 📈
  • Reviewer & Editor for prestigious journals including PLOS ONE 📝
    Dr. Raza has received numerous accolades for his contributions to AI, research excellence, and academia. 🌟

Publications 📚

Xin SU | Multi-temporal remote sensing information extraction | Best Researcher Award

Published on by Cognitive Scientist Awards

Prof Dr. Xin SU | Multi-temporal remote sensing information extraction | Best Researcher Award

Xin Su, PhD, is an Associate Professor at the School of Artificial Intelligence, Wuhan University. He supervises both master’s and PhD students. He earned his doctorate in Signal and Image Processing from Telecom ParisTech in 2015. He then worked as a postdoctoral researcher at INRIA, France, from 2015 to 2018. His research focuses on intelligent analysis of time-series images, spatiotemporal target recognition, and large-scale remote sensing models. He has led and participated in multiple national research projects, publishing extensively in top-tier journals such as IEEE TIP, IEEE TGRS, ISPRS, and JAG. 📚🎓

Profile

Education 🎓

  • PhD (2015): Telecom ParisTech, France – Signal and Image Processing 🎓
  • Master’s Studies (2008-2011, uncompleted): Wuhan University – Signal and Information Processing 🏫
  • Bachelor’s (2004-2008): Wuhan University – Electronic Science and Technology (Engineering) 🎓

Experience 👨‍🏫

  • 2015-2018: Postdoctoral Researcher, INRIA, France 🇫🇷
  • 2015: Postdoctoral Researcher, Telecom ParisTech, France 🎓

Research Interests 🔬

Xin Su specializes in intelligent analysis of time-series remote sensing images, spatiotemporal object recognition, and large-scale AI models for remote sensing. His work spans geospatial applications, UAV-based surveillance, and hyperspectral data processing. He actively contributes to developing advanced AI techniques for satellite video analysis and infrastructure monitoring. 🚀🌍

Awards & Recognitions 🏅

Xin Su has been recognized for his contributions to remote sensing and AI, receiving multiple national research grants and awards for excellence in scientific research and innovation. He has secured funding from National Natural Science Foundation projects and defense-related initiatives. His research has been featured in top IEEE and ISPRS journals, reinforcing his position as a leading researcher in the field. 🌟🏅

Publications 📚

Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

Published on by Cognitive Scientist Awards

Assist. Prof. Dr. Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

 

Profile

Education

She holds a Ph.D. in Econometrics and Machine Learning from the University of Orleans (2018–2022), where she investigated new machine learning approaches for financial fraud detection and survival analysis in the insurance industry under the supervision of S. Tokpavi. In addition, she earned a Data Science Certificate (Executive) from the Institute of Risk Management (IRM) in 2016–2017. Her academic background also includes a Master’s degree in Mathematical Engineering (Applied Statistics) from Paris-Sud University (2004–2007) and a Master’s degree in Mathematics from the University of Marmara in Istanbul (1995–1999). Since September 2022, she has been an Assistant Professor of Data Science at IPAG Business School in Nice and Paris. With extensive experience in the insurance sector, she integrates her professional insights into the classroom, emphasizing practical AI applications. Her curriculum reflects the latest trends in data science, fostering a dynamic learning environment tailored to students’ needs. She adapts resources and pedagogical methods to specific course objectives, utilizing tools such as Tableau for data visualization and exploring real-world business applications, including Netflix, Uber, ChatGPT, Gemini, and facial recognition technologies.

 

Work experience

She has held various academic and professional roles, combining her expertise in data science, machine learning, and business analytics. From September 2022 to January 2023, she was an adjunct faculty member at the International University of Monaco, where she taught Mathematics for Business. Prior to that, from September 2021 to August 2022, she served as an adjunct faculty member at IPAG Business School (Nice), teaching courses such as “Data Analysis for Business Management” (BBA3), “Data Processing” (MSc, e-learning), “Digital and Sales” (GEP 5th year), and “Introduction to Statistics” (BBA1). Between September 2020 and October 2021, she was an adjunct faculty member at EMLV (Paris), where she taught “Quantitative Data Analytics – SPSS” (GEP 4th year, hybrid learning) and supervised master’s theses for GEP 5th-year students.

In addition to her academic roles, she has extensive experience in the consulting and insurance sectors. From March to November 2020, she worked as a Senior Consultant at Fraeris (Paris), supporting clients in project development and providing technical solutions. She collaborated with the “Caisse de Prévoyance Sociale” (CPS) of French Polynesia, modeling healthcare expenditures using machine learning techniques. She developed predictive models to analyze healthcare costs from both the insured’s and CPS’s perspectives, offering actionable insights and data-driven forecasts to aid long-term financial planning. Prior to that, in 2019–2020, she was a Senior Manager in Pricing & Data P&C at Addactis (Paris), where she supported clients in project development, innovation, and strategic planning. As an expert referent for ADDACTIS® Pricing software, she worked on database processing for BNP Paribas Cardif, facilitating APLe software operations for quarterly account closings.

Memberships and Projects:

• Membership of the American Risk and Insurance Association (ARIA)
• Membership of the academic association AFSE.
• Member of the RED Flag Project of the University of Orléans in cooperation with CRJPothier.
• Participation at 3 Erasmus+ Projects: Artificial Intelligence to support Education (EducAItion).
• Virtual Incubator Tailored to All Entrepreneurs (VITAE).
• Artificial Intelligence in high Education (PRAIME),

Research topics:

Studies focus on the application of data science techniques to business issues, particularly in the insurance
sector, and on climate change. Another topic of study is the relationship between AI and education.

Publication

  • Yankol-Schalck, M. (2023). Auto Insurance Fraud Detection: Leveraging Cost Sensitive and Insensitive
    Algorithms for comprehensive Analysis, Insurance: Mathematics and Economics.(
    (https://www.sciencedirect.com/science/article/abs/pii/S0167668725000216)
    Banulescu‐Radu, D., & Yankol‐Schalck, M. (2024). Practical guideline to efficiently detect insurance fraud
    in the era of machine learning: A household insurance case. Journal of Risk and Insurance, 91(4), 867-
    913.
    Yankol-Schalck, M. (2022). A Fraud Score for the Automobile Insurance Using Machine Learning and
    Cross-Data set Analysis, Research in International Business and Finance, Volume 63, 101769.
    Schalck, C., Yankol-Schalck, M. (2021). Failure Prediction for SME in France: New evidence from
    machine learning techniques, Applied Economics, 53(51), 5948-5963.
    On- going research:
    Yankol-Schalck (2025). Auto Insurance Fraud Detection: Machine Learning and Deep Learning
    Applications, submitted in Journal of Risk and Insurance.
    Schalck, C., Yankol-Schalck, M. (2024). Churn prediction in the French insurance sector using Grabit
    model, revision in Journal of Forecasting.
    Schalck, C., Seungho, L., Yankol-Schalck, M. (2024). Characteristics of firms and climate risk
    management: a machine learning approach. Work in progress for The Journal of Financial Economics.
    Yankol-Schalck M.and Chabert Delio C., (2024). The application of machine learning to analyse changes in
    consumer behaviour in a major crisis. Work in progress.
    Yankol-Schalck M. and Nasseri A. (2024).An investigation into the integration of artificial intelligence in
    education: Implications for teaching and learning methods. Work in progress.