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 ๐Ÿค– and neural computation ๐Ÿง . He completed his B.S. at Jiangsu University ๐Ÿ‡จ๐Ÿ‡ณ and M.S. in Energy and Power from Warwick University ๐Ÿ‡ฌ๐Ÿ‡ง. 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 ๐ŸŒ.

Profile

Education ๐ŸŽ“

๐ŸŽ“ B.S. in Engineering โ€“ Jiangsu University, China ๐Ÿ‡จ๐Ÿ‡ณ
๐ŸŽ“ M.S. in Energy and Power โ€“ University of Warwick, UK ๐Ÿ‡ฌ๐Ÿ‡ง (2018)
๐ŸŽ“ Ph.D. Candidate โ€“ Hohai University, majoring in Artificial Intelligence ๐Ÿค–
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. ๐Ÿ“˜๐Ÿง 

Experience ๐Ÿ‘จโ€๐Ÿซ

Dr. Wang is currently pursuing his Ph.D. at Hohai University, where he investigates dendritic learning algorithms and synaptic modeling. ๐Ÿงฌ 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.” ๐Ÿ“Š His work balances theoretical depth and applied research, particularly in neural computation, classification systems, and resource-efficient AI. ๐Ÿ”ฌ๐Ÿ’ก

Awards & Recognitions ๐Ÿ…

๐Ÿ… Patent Holder (CN202410790312.0, CN202410646306.8, CN201510661212.9)
๐Ÿ“„ Published in SCI-indexed journal Mathematics (MDPI)
๐ŸŒ Recognized on ORCID (0009-0002-6844-1446)
๐Ÿง  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. ๐Ÿš€๐Ÿ“˜

Research Interests ๐Ÿ”ฌ

Dr. Wangโ€™s research fuses deep learning ๐Ÿค– and dendritic modeling ๐Ÿง  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. ๐ŸŒฑ๐Ÿ“Š His vision is to bridge the gap between neuroscience and AI through interpretable, high-performance algorithms. ๐Ÿง ๐Ÿ’ก

Publications

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

    Mathematics
    2025-04-29 |ย Journal article
    DOI:ย 10.3390/math13091465
    CONTRIBUTORS:ย Chongyuan Wang;ย 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) ๐Ÿ‡ฎ๐Ÿ‡ท is a Petroleum Engineer and Data Scientist ๐Ÿ’ป๐Ÿ›ข๏ธ with expertise in reservoir engineering, petrophysics, and AI applications in the energy sector. Based in Tehran, Iran ๐Ÿ“, he holds a Masterโ€™s and Bachelorโ€™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 ๐Ÿ“š, won international awards ๐Ÿ†, and participated in key petroleum projects. He served in the military ๐Ÿช– and actively collaborates with academia and industry on AI-driven energy solutions.

Profile

Education ๐ŸŽ“

๐Ÿง‘โ€๐ŸŽ“ Master’s in Petroleum Engineering (Petroleum Exploration), Petroleum University of Technology, Abadan ๐Ÿ‡ฎ๐Ÿ‡ท (2019โ€“2022) | GPA: 18.82/20
๐ŸŽ“ Bachelorโ€™s in Petroleum Engineering, Islamic Azad University (Science & Research Branch), Tehran ๐Ÿ‡ฎ๐Ÿ‡ท (2015โ€“2019) | GPA: 19.14/20
๐Ÿ“š Courses covered include reservoir engineering, geomechanics, well-logging, and advanced data analytics.
๐Ÿ› ๏ธ Projects include COโ‚‚ storage modeling, permeability prediction via AI, and LWD-based mud loss forecasting.
๐Ÿ“Š Developed key industry collaborations with NISOC, RIPI, and OEID through thesis, internships, and military service projects.
๐Ÿ’ก Honed computational and simulation skills using MATLAB, Python, COMSOL, Petrel, and ECLIPSE.
๐Ÿ›๏ธ Academic mentors: Dr. Seyed Reza Shadizadeh, Dr. Bijan Biranvand, Dr. Majid Akbari.

Experience ๐Ÿ‘จโ€๐Ÿซ


๐Ÿ”ฌ Computer Aided Process Engineering (CAPE) โ€“ Petroleum Reservoir Engineer (Nov 2024โ€“Present)
๐Ÿ›ข๏ธ Petro Vision Pasargad โ€“ Reservoir Engineer & Lab Operator (Sep 2023โ€“May 2024)
๐Ÿง  Research Institute of Petroleum Industry (RIPI) โ€“ Petroleum Engineer, Data Scientist (Mar 2023โ€“Apr 2024)
๐Ÿญ National Iranian South Oil Company (NISOC) โ€“ Petroleum Engineer, Petrophysicist (Mar 2021โ€“Nov 2024)
๐Ÿงช Internships: NIOC – Exploration Management, Oil & Energy Industries Development (OEID)
๐Ÿ“Š Key contributions include EOR analysis, SCAL/RCAL lab testing, permeability modeling, machine learning pipelines, and field data analysis.
๐Ÿงพ Delivered reports, simulations, and AI models supporting production optimization and reservoir characterization.

Awards & Recognitions ๐Ÿ…

๐Ÿฅ‰ 3rd Prize Winner โ€“ EAGE Laurie Dake Challenge 2022 (Madrid, Spain) ๐ŸŒ
๐ŸŽ–๏ธ Recognized for thesis excellence in AI-driven mud loss prediction with NISOC collaboration
๐Ÿ“Œ Acknowledged during military service project with RIPI for developing ANN-based well log models
๐Ÿ… Published in high-impact journals such as Energy, Geoenergy Science and Engineering, and JRMGE
โœ๏ธ Co-author of multiple peer-reviewed papers and under-review articles across petroleum engineering disciplines
๐Ÿ”ฌ Worked alongside top researchers including Dr. Ostadhassan, Dr. Gao, and Dr. Hemmati-Sarapardeh
๐Ÿ› ๏ธ Actively participated in multidisciplinary teams combining AI, geomechanics, and petrophysics
๐Ÿ“ข Regular presenter and contributor at petroleum conferences and AI-in-energy seminars.

Research Interests ๐Ÿ”ฌ

๐Ÿ“Œ AI & ML applications in petroleum engineering ๐Ÿง ๐Ÿ›ข๏ธ โ€“ including ANN, genetic algorithms, and deep learning
๐Ÿ“Š Mud loss zone prediction, formation permeability modeling, COโ‚‚ storage feasibility using ML
๐Ÿงช Experimental rock mechanics: nanoindentation, geomechanical upscaling, SCAL/RCAL testing
๐Ÿ“ˆ Petrophysical property estimation in carbonate and unconventional reservoirs
๐ŸŒ Reservoir simulation, LWD analysis, and smart data integration using Python, Petrel, COMSOL
๐Ÿ“– Notable studies include: elastic modulus upscaling, kerogen behavior under pyrolysis, RQI/FZI modeling
๐Ÿ”ฌ Interdisciplinary projects bridging data science with geoscience and reservoir engineering
๐Ÿค Collaboration with academic and industry leaders to develop practical, AI-driven solutions for energy challenges.

Publicationsย 

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

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

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

    Energy
    2025-04 |ย Journal article
    DOI:ย 10.1016/j.energy.2025.136040
    CONTRIBUTORS:ย 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 |ย Journal article
    DOI:ย 10.1007/s13202-025-01952-6
    CONTRIBUTORS:ย Farshad Sadeghpour;ย Kamran Jahangiri;ย 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 |ย Journal article
    DOI:ย 10.1016/j.jrmge.2023.12.027
    CONTRIBUTORS:ย Ghasem Aghli;ย Babak Aminshahidy;ย Hem Bahadur Motra;ย Ardavan Darkhal;ย Farshad Sadeghpour;ย Mehdi Ostadhassan

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

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

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 Elsevierโ€™s Worldโ€™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. ๐ŸŒ๐Ÿ“–

Profile

Education ๐ŸŽ“

  • Ph.D. in Control Science & Engineering (2014-2017) โ€“ University of Science & Technology of China (USTC), China ๐Ÿ‡จ๐Ÿ‡ณ
    • Specialization: Pattern Recognition & Intelligent Systems
  • MS (Computer Science) (2009-2010) โ€“ Iqra University, Islamabad, Pakistan ๐Ÿ‡ต๐Ÿ‡ฐ
    • CGPA: 3.64 | Specialization: Image Processing
  • MCS (Master of Computer Science) (2004-2006) โ€“ COMSATS Institute of Information Technology, Pakistan
    • CGPA: 3.24 | 80% Marks
  • BCS (Bachelor in Computer Science) (1999-2003) โ€“ Punjab University, Lahore, Pakistan
    • CGPA: 3.28 | 64.25% Marks
  • Higher Secondary (Pre-Engineering) โ€“ Islamabad College for Boys
  • Matriculation (Science) โ€“ Islamabad College for Boys
    Dr. Razaโ€™s academic journey is marked by top-tier universities and a strong focus on AI, pattern recognition, and cybersecurity. ๐ŸŽ“๐Ÿ“š

Experience ๐Ÿ‘จโ€๐Ÿซ

  • Professor (2024-Present) โ€“ Namal University, Mianwali
    • Teaching AI, Cybersecurity, and Research Supervision
  • Associate Professor/Head AI & Cybersecurity Program (2023-2024) โ€“ HITEC University, Taxila
    • Led AI & Cybersecurity programs, supervised PhDs, and organized industry-academic collaborations
  • Associate Professor (2023) โ€“ COMSATS University, Islamabad
  • Assistant Professor (2012-2023) โ€“ COMSATS University, Islamabad
  • Lecturer (2008-2012) โ€“ COMSATS University, Islamabad
  • Research Associate (2006-2008) โ€“ 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. ๐Ÿซ๐Ÿ“Š

Research Interests ๐Ÿ”ฌ

Prof. Dr. Mudassar Razaโ€™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 AIโ€™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. ๐Ÿง ๐Ÿ“Š

  • National Youth Award 2008 by the Prime Minister of Pakistan for contributions to Computer Science ๐ŸŽ–๏ธ
  • Listed in Worldโ€™s Top 2% Scientists (2023) by Elsevier ๐ŸŒ
  • Ranked #11 in Computer Science in Pakistan by AD Scientific Index ๐Ÿ“Š
  • Senior IEEE Member (ID: 91289691) ๐Ÿ”ฌ
  • HEC Approved PhD Supervisor ๐ŸŽ“
  • Best Research Productivity Awardee at COMSATS University multiple times ๐Ÿ†
  • Recognized by ResearchGate with a Research Interest Score higher than 97% of members ๐Ÿ“ˆ
  • Reviewer & Editor for prestigious journals including PLOS ONE ๐Ÿ“
    Dr. Raza has received numerous accolades for his contributions to AI, research excellence, and academia. ๐ŸŒŸ

Publications ๐Ÿ“š

Mahmoud Alimoradi | Machine Learning | Best Researcher Award

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Mr. Mahmoud Alimoradi | Machine Learning | Best Researcher Award

Lahijan Azad ,Iran

He understands the growing need for Machine Learning and has a keen interest in the field, which he considers a blessing. Recognizing the importance of managing large and complex computations to control various aspects of the human environment has led him into this vast world. He is particularly fascinated by machine learning, especially reinforcement learning, supervised learning, semi-supervised learning, outliers, and basic data challenges. Furthermore, optimization, an area of artificial intelligence that requires fundamental studies and a change in approach, is another of his key research interests.

Profile

Education

He holds a Masterโ€™s degree in Artificial Intelligence Engineering from the University of Shafagh, completed in 2020. His thesis was titled “Trees Social Relations Optimization Algorithm: A New Swarm-Based Metaheuristic Technique to Solve Continuous and Discrete Optimization Problems.” He also earned a Bachelorโ€™s degree in Software Engineering from Azad Lahijan University, which he attended from 2007 to 2011.

Research Interests

Theory: Reinforcement Learning (high-dimensional problems, regularized algorithms, model
learning,
representation learning and deep RL, learning from demonstration, inverse optimal control, deep
Reinforcement Learning); Machine Learning (statistical learning theory, nonparametric
algorithms, time series. processes, manifold learning, online learning); Large-scale Optimization;
Evolutionary Computation, Metaheuristic Algorithm, Deep Learning, Healthcare Machine
learning, Big Data, Data Problems (Imbalanced), Signal Analysis
Applications: Automated control, space affairs, robotic control, medicine and health, asymmetric
data, data science, scheduling, proposing systems, self-enhancing systems

Work Experience

He is a freelance programmer with expertise in various operating systems, including Microsoft Windows and Linux (Arch, Ubuntu, Fedora). He is proficient in software tools such as Microsoft Office, Anaconda, Jupyter, PyCharm, Visual Studio, Tableau, RapidMiner, MATLAB, and Visual Studio. His programming skills include Matlab, Python, C++, Scala, Java, and Julia, with a focus on data mining, data science, computer vision, and machine learning. He is experienced with Python libraries like Pandas, Numpy, Matplotlib, Seaborn, PyCV, TensorFlow, Time Series Analysis, Spark, Hadoop, and Cassandra. Additionally, he is skilled in using Github, Docker, and MySQL. His expertise spans machine learning, deep learning, imbalanced data, missing data, semi-supervised learning, healthcare machine learning, algorithm design, and metaheuristic algorithms. He is fluent in English and Persian.

Publications