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

Amar Salehi | Reinforcement Learning | Best Researcher Award

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Dr. Amar Salehi | Reinforcement Learning | Best Researcher Award

Dr. Amar Salehi is a postdoctoral researcher at South China University of Technology 🇨🇳, specializing in microrobotics 🤖, AI 🧠, and biosystems engineering 🌱. With a Ph.D. in Mechanical Engineering of Biosystems 🎓 from the University of Tehran 🇮🇷, he developed intelligent and independent control systems for magnetic microrobots. His work integrates machine learning, deep learning, and bio-inspired design for environmental and biomedical applications 🌍🧬. Passionate about innovation, he has contributed to several peer-reviewed journals 📚, international conferences 🌐, and interdisciplinary projects. He also served as a teaching assistant and reviewer and held leadership roles in scientific societies 👨‍🏫. A top-ranked scholar in national entrance exams 🏆, Dr. Salehi actively collaborates across borders for research and development in cutting-edge AI and robotics 🔬.

Profile

Education 🎓

Dr. Salehi earned his Ph.D. in Mechanical Engineering of Biosystems 🎓 from the University of Tehran (2019–2024), focusing on intelligent magnetic microrobot control 🤖. He completed his M.S. at Isfahan University of Technology (2013–2015) 🧪, where he explored fluid heat transfer using CFD methods and mechanical behavior modeling with neural networks. His B.S. was from Razi University (2008–2012) in Biosystems Mechanical Engineering 🔧🌾. A consistent top performer, he ranked 2nd in the Ph.D. entrance exam and 90th in the M.S. exam among thousands 🏅. His academic record features exceptional GPAs and thesis scores 🌟. Dr. Salehi’s interdisciplinary education blends mechanical systems, AI, and biology, building a strong foundation for his current microrobotics and biosensor research 🔬📊.

Experience 👨‍🏫

Experience (150 words): Dr. Salehi is currently a Postdoctoral Fellow at the Shien-Ming Wu School of Intelligent Engineering, South China University of Technology 🇨🇳 (2024–present), working on intelligent agents and microrobotics 🤖. Previously, he was a teaching assistant at the University of Tehran, supporting physics and mechanical engineering courses 👨‍🏫. He also taught part-time at Azad University, Iran (2016–2019) 📘. As a research assistant at the AIAX Lab, he contributed to AI and advanced control systems. He led several interdisciplinary projects, including a joint Iran-Turkey research on microfluidic biochips 🧫. A reviewer for “The Innovation” journal, he is proficient in tools like COMSOL, SolidWorks, Python, and statistical analysis 📊🖥️. He also chaired a student startup “Green Daal Mechanics” and served in university and parliamentary scientific committees 🚀📈.

Awards & Recognitions 🏅

Awards and Honors (150 words): Dr. Salehi received the Best Oral Presentation Award 🥇 at IRAC 2024 for his work on deep learning and microrobots 🤖. Ranked 2nd in the national Ph.D. entrance exam and 90th in the M.S. exam, he also achieved excellent scores in his thesis evaluations (Ph.D.: 19.65/20, M.S.: 19.49/20) 🏆. His academic and research excellence has earned him recognition in national and international forums 📜. He has been an active member of the Scientific Association of Biosystems Engineering and the Interdisciplinary Scientific Student Association at the University of Tehran 🧠. He also served as Editor-in-Chief of the New Green Industry Journal 🌱. With strong leadership in university-industry interaction, he contributes to Iran’s agricultural, food, and energy research panels and policy discussions 🧑‍🔬📢.

Research Interests 🔬

.Research Focus (150 words): Dr. Salehi’s research lies at the intersection of microrobotics 🤖, artificial intelligence 🧠, and biosystems 🌱. His Ph.D. work focused on intelligent, model-free control of magnetic microrobots using deep reinforcement learning in real-world environments 🔍. He explores biosensor optimization using genetic algorithms 🧬, natural language interfaces for microrobot control 🗣️, and micro/nano-systems for biomedical and environmental applications 🌍. He integrates fuzzy logic, ANN, and reinforcement learning in his predictive modeling. Ongoing research includes yield prediction in intercropping systems 🌾 and AI-driven environmental cleanup technologies. Dr. Salehi’s goal is to create autonomous, intelligent microsystems that can navigate, sense, and interact with biological and physical environments, with potential applications in diagnostics, therapy, and sustainability 🧪♻️.

Publications 

 

 

 

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

Mansoor Ali Darazi | Artificial Intelligence | Best Researcher Award

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Dr. Mansoor Ali Darazi | Artificial Intelligence | Best Researcher Award

Dr. Mansoor Ali Darazi is an accomplished English language educator and researcher with extensive experience in ELT, curriculum development, and student mentorship. Passionate about modern pedagogical techniques, he fosters an inclusive learning environment while actively contributing to academic research. His expertise in language teaching, academic writing, and leadership roles has earned him recognition in the field. Committed to continuous professional growth, he participates in conferences and research projects. His dynamic teaching approach and strong managerial skills enhance students’ academic success and institutional development.

Profile

Education 🎓

Dr. Darazi is pursuing a Ph.D. in English Linguistics at the University of Sindh (2023–2026). He holds a Ph.D. in Education (ELT) (2022) and an M.Phil. in Education (ELT) (2014) from Iqra University, Karachi. He completed his Bachelor of Arts at Shah Abdul Latif University, Khairpur (1997). His academic journey reflects his dedication to English language teaching, research, and linguistic studies.

Experience 👨‍🏫

Dr. Darazi is an Assistant Professor at Benazir Bhutto Shaheed University, Lyari (2022–present). He has served as a Lecturer (2015–2022), ELT Coordinator, and English Lecturer at various institutions, including Army Public School, Pakistan Marine Academy, and Bahria Foundation College. With over two decades in academia, he has contributed to curriculum development, language instruction, and educational leadership, shaping student success through innovative teaching methodologies.

Awards & Recognitions 🏅

Dr. Darazi has received recognition for his contributions to education and research. His accolades include academic excellence awards, research grants, and honors from national and international organizations. His active participation in TESOL, IELTA, and Linguistic Society of America highlights his commitment to advancing English language education and pedagogy.

Research Interests 🔬

Dr. Darazi’s research explores English language proficiency, ELT methodologies, academic motivation, and student engagement. His publications address linguistic pedagogy, transformational leadership in education, and the role of feedback in language learning. His work contributes to developing innovative teaching strategies that enhance students’ academic performance and career prospects.

Publications 

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 

 

Aakash Kumar | Path Planning | Best Researcher Award

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Dr. Aakash Kumar | Path Planning | Best Researcher Award

Dr. Aakash Kumar is a Postdoctoral Researcher at Zhongshan Institute of Changchun University of Science and Technology, China. Born in September 1987 in Pakistan, he specializes in Control Science and Engineering with expertise in AI, deep learning, and computer vision. Fluent in English, Chinese, Urdu, and Sindhi, he has worked extensively on spiking neural networks, UAV fault detection, and deep learning optimization. His research contributions span AI-driven robotics, autonomous vehicles, and computational neuroscience. Dr. Kumar has collaborated internationally, guiding Ph.D. and Master’s students, and publishing in renowned journals. He has also worked as a Machine Learning Engineer and Data Scientist. With a strong background in software development, statistical modeling, and GPU parallelization, he actively explores AI advancements. His interdisciplinary work bridges academia and industry, focusing on intelligent automation, efficient deep learning models, and AI applications in healthcare and engineering. 📊🤖🔬

Profile

Education 🎓

Dr. Aakash Kumar earned a Doctor of Engineering (2017–2022) and a Master’s (2014–2017) in Control Science and Engineering from the University of Science and Technology of China, specializing in Control Systems. Both degrees were fully funded by prestigious scholarships, including the Chinese Academy of Sciences-The World Academy of Sciences President’s Fellowship and the Chinese Government Scholarship. He also completed a Diploma in Chinese Language (2013–2014) from Anhui Normal University, achieving HSK-4 proficiency. His academic journey began with a B.S. in Electronic Engineering (2007–2011) from the University of Sindh, Pakistan. His education has been pivotal in shaping his expertise in AI-driven robotics, computational intelligence, and deep learning optimization. Through rigorous research and training, he has honed his skills in deep learning, reinforcement learning, and AI applications in control systems. His academic foundation supports his contributions to AI-powered automation, smart systems, and computational modeling. 🏅📡

Experience 👨‍🏫

Dr. Aakash Kumar has been a Postdoctoral Researcher (2022–Present) at Zhongshan Institute of Changchun University of Science and Technology, China, where he develops AI-driven solutions for robotics and deep learning applications. Previously, he worked remotely as a Machine Learning Engineer (2021–2022) at COSIMA.AI Inc., USA, where he contributed to AI-based cancer detection, sign language translation, and smart vehicle monitoring. Earlier, he was a Data Scientist (2012–2013) at Japan Cooperation Agency, Pakistan, analyzing agriculture and livestock data. His academic career includes a Lecturer role (2011–2012) at The Pioneers College, Pakistan. He has led AI research initiatives, supervised Ph.D. and Master’s students, and optimized neural networks for industrial applications. With expertise in AI model compression, computer vision, and reinforcement learning, he has been instrumental in developing computational techniques for real-world automation, AI-powered robotics, and UAV fault detection. His work integrates deep learning, optimization, and AI-driven automation. 🏢🤖📈

Research Interests 🔬

Dr. Aakash Kumar’s research focuses on AI-driven robotics, deep learning optimization, and computational intelligence. He has developed Deep Spiking Q-Networks (DSQN) for mobile robot path planning, a CNN-LSTM-AM framework for UAV fault detection, and Deep Conditional Generative Models (DCGMDL) for supervised classification. His work integrates reinforcement learning, neural network pruning, and AI-driven automation to enhance machine learning efficiency. He specializes in deep learning model compression, AI-powered automation, and collaborative data analysis methods. His projects include endoscopy fault detection, smart vehicle monitoring, and neuropsychological condition prediction using AI. With extensive experience in R, Python, TensorFlow, and MATLAB, he develops AI models for healthcare, autonomous systems, and intelligent automation. His interdisciplinary research bridges academia and industry, advancing AI for real-world applications in robotics, deep learning optimization, and intelligent control systems. 🚀📡📊

Awards & Recognitions 🏅

Dr. Aakash Kumar has received numerous prestigious awards, including the Chinese Academy of Sciences-The World Academy of Sciences President’s Fellowship (2017–2022) and the Chinese Government Scholarship (2014–2017, 2013–2014). His AI research achievements earned recognition in top conferences, including IEEE Infoteh-Jahorina and Neurocomputing. He has been honored for his contributions to deep learning and AI-powered robotics, including Best Research Paper Awards at multiple international conferences. His work on efficient CNN optimization and deep spiking Q-networks has gained significant academic and industry recognition. As a speaker at AI conferences, he has presented on generative AI, photon-level ghost imaging, and autonomous vehicle advancements. He continues to receive accolades for his groundbreaking research in AI, robotics, and computational intelligence, solidifying his reputation as a leading expert in control systems and AI-driven automation. 🏅🔬📢

Publications 📚

Chunyu Liu | Cognitive Computing | Best Researcher Award

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Dr. Chunyu Liu | Cognitive Computing | Best Researcher Award

Chunyu Liu is a Lecturer at North China Electric Power University, specializing in machine learning, neural decoding, and visual attention. 📚 She earned her B.S. in Mathematics and Applied Mathematics from Henan Normal University, an M.S. in Applied Mathematics from Northwest A&F University, and a Ph.D. in Computer Application Technology from Beijing Normal University. 🎓 She completed postdoctoral training at Peking University. 🔬 Her research integrates AI methodologies with cognitive neuroscience, focusing on neural encoding, decoding, and attention mechanisms. 🧠 She has published over 10 research papers, including six SCI-indexed publications as the first author. 📝 Her work aims to bridge artificial intelligence with human cognitive function understanding, contributing significantly to computational neuroscience. 🌍 Liu has also been involved in several major research projects, furthering advancements in neural signal analysis and cognitive computing. 🚀

Profile

Education 🎓

Chunyu Liu holds a strong academic background in mathematics and computational sciences. She obtained her B.S. degree in Mathematics and Applied Mathematics from Henan Normal University. ➕ She pursued her M.S. in Applied Mathematics at Northwest A&F University, where she deepened her expertise in mathematical modeling. 🔢 Continuing her academic journey, she earned a Ph.D. in Computer Application Technology from Beijing Normal University. 🖥️ Her doctoral research explored advanced AI techniques applied to neural decoding and cognitive processing. 🧠 To further refine her skills, she completed postdoctoral training at Peking University, focusing on integrating artificial intelligence with neural mechanisms. 🔬 Her academic pathway reflects a multidisciplinary approach, merging mathematics, computer science, and cognitive neuroscience to address complex challenges in brain science and AI. 📊 Liu’s education laid the foundation for her contributions to machine learning, visual attention studies, and neural encoding research.

Experience 👨‍🏫

Dr. Chunyu Liu is currently a Lecturer at North China Electric Power University, where she teaches and conducts research in cognitive computing and machine learning. 🎓 She has led and collaborated on multiple projects related to neural encoding and decoding, investigating how the brain processes object recognition, emotions, and attention. 🧠 Prior to her current role, she completed postdoctoral research at Peking University, where she worked on advanced AI-driven models for neural signal analysis. 🔍 Over the years, Liu has gained extensive experience in analyzing multimodal neural signals, including magnetoencephalography (MEG) and functional MRI (fMRI). 📡 She has also served as a reviewer for esteemed scientific journals and collaborated with interdisciplinary research teams on AI and brain science projects. 🔬 Her expertise extends to both academia and industry, where she has contributed to the development of novel computational models for decoding brain activity. 🚀

Research Interests 🔬

Dr. Chunyu Liu’s research integrates artificial intelligence and brain science to understand cognitive functions through neural decoding. 🧠 She employs multi-modal neural signals such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to analyze brain activity. 📡 Her work explores neural encoding and decoding, focusing on object recognition, emotion processing, and multiple-object attention. 🎯 She develops AI-based models to extract human brain features and gain insights into cognitive mechanisms. 🤖 By integrating psychological experimental paradigms with AI, Liu aims to advance computational neuroscience. 🏆 Her research also inspires the development of new AI theories and algorithms based on principles of brain function. 📊 She has led major projects in cognitive computing, contributing significantly to both theoretical advancements and practical applications in neural signal processing. 🚀 Through her work, she bridges the gap between human cognition and artificial intelligence, driving innovations in brain-computer interface research. 🏅

 

Awards & Recognitions 🏅

Dr. Chunyu Liu has received recognition for her outstanding contributions to cognitive computing and AI-driven neuroscience research. 🏅 She has been nominated for the prestigious International Cognitive Scientist Award for her pioneering work in neural decoding and visual attention mechanisms. 🎖️ Liu’s research publications have been featured in high-impact journals, earning her accolades from the scientific community. 📜 Her first-author papers in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Science China Life Sciences, and IEEE Journal of Biomedical and Health Informatics have been widely cited. 📝 She has also been honored with research grants and funding for AI-driven cognitive studies. 🔬 Her innovative work in decoding brain signals has been recognized in international AI and neuroscience conferences. 🌍 Liu’s academic excellence and contributions continue to shape the field of computational neuroscience and machine learning applications in cognitive science. 🚀

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 📚

Guoliang Wang | Control Science and Engineering | Best Researcher Award

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Prof. Guoliang Wang | Control Science and Engineering | Best Researcher Award

 Guoliang Wang is a Professor at the Department of Automation, School of Information and Control Engineering, Liaoning Petrochemical University. 📚 With extensive expertise in control theory and automation, he has made significant contributions to Markov jump systems, stochastic system theory, and big data-driven fault detection. 🚀 He has published 86 journal articles indexed in SCI and Scopus, authored books, and holds 9 patents. 🏅 As a postdoctoral researcher at Nanjing University of Science and Technology (2011-2016), he furthered his research in control engineering. 🎓 His professional memberships include the Chinese Association of Automation and the Chinese Mathematical Society. 🏆 He has received the Liaoning Province Natural Science Academic Achievement Second Prize for his contributions. His innovative work in optimization, reinforcement learning, and system modeling continues to impact academia and industry. 🌍

Profile

Education 🎓

Guoliang Wang earned his Ph.D. in Control Theory and Control Engineering from Northeastern University (2007-2010). 🎓 Prior to that, he completed his Master’s degree in Operations Research and Control Theory at the School of Science, Northeastern University (2004-2007). 📖 His academic foundation is built on advanced mathematical modeling, stochastic systems, and automation, equipping him with expertise in complex system analysis. 🏗️ His research has consistently focused on optimizing control mechanisms and enhancing stability in dynamic environments. As a Postdoctoral Researcher at Nanjing University of Science and Technology (2011-2016), he deepened his understanding of control theory, reinforcement learning, and system dynamics. 🏅 His education has been pivotal in developing innovative methodologies for automation, fault detection, and big data-driven decision-making.

Experience 👨‍🏫

Since March 2010, Guoliang Wang has been a Professor at Liaoning Petrochemical University, specializing in automation and control engineering. 🏫 He served as Associate Dean of the Department of Automation (2013-2014), leading academic and research initiatives. 🌍 His postdoctoral research at Nanjing University of Science and Technology (2011-2016) explored stochastic system applications and control theory advancements. 🔬 Over the years, he has led multiple research projects, consulted on industrial automation solutions, and contributed to major technological advancements. 💡 His work has resulted in 86 peer-reviewed journal publications, 9 patents, and significant contributions to adaptive dynamic programming. 🚀 As a member of various professional associations, he actively collaborates with international researchers and institutions. His expertise spans Markov jump systems, stochastic modeling, fault detection, and AI-driven automation strategies

Research Interests 🔬

Guoliang Wang’s research spans modeling and control of Markov jump systems, stochastic system applications, and AI-driven automation. 🤖 His work in fault detection, diagnosis, and big data-driven prediction has led to practical advancements in system optimization. 📊 He has proposed novel stabilizing controllers, developed reinforcement learning-based optimization models, and improved system performance through convex optimization techniques. 🔍 His expertise in stochastic control extends to image processing, predictive analytics, and adaptive dynamic programming. 📡 His research contributions have significantly enhanced system stability and reduced computational complexity in industrial automation. 💡 Through collaborations with global researchers, he continues to push the boundaries of automation, AI, and smart control systems. 🚀 His work integrates theoretical insights with real-world applications, ensuring a lasting impact on engineering and technology. 🌍

Guoliang Wang has been recognized for his outstanding contributions to automation and control engineering. 🏆 He received the prestigious Liaoning Province Natural Science Academic Achievement Second Prize for his groundbreaking research. 🏅 His achievements include being a member of elite research committees such as the Youth Committee of the Chinese Association of Automation and the Adaptive Dynamic Programming and Reinforcement Learning Technical Committee. 🎖️ He has been an invited speaker at international conferences and has received commendations for his work in optimizing stochastic system control. 📜 His research impact is further reflected in his editorial board membership at the Journal of Liaoning Petrochemical University. ✍️ With numerous patents, consultancy projects, and high-impact research, he continues to receive nominations and accolades in automation, AI, and control system optimization

Publications 📚

  • Sampled-Data Stochastic Stabilization of Markovian Jump Systems via an Optimizing Mode-Separation Method

    IEEE Transactions on Cybernetics
    2025 | Journal article
    DOI: 10.1109/TCYB.2025.3534268
    CONTRIBUTORS: Guoliang Wang; Yaqiang Lyu; Guangxing Guo

  • Stabilization of Stochastic Markovian Jump Systems via a Network-Based Controller

    IEEE Transactions on Control of Network Systems
    2024-03 | Journal article
    DOI: 10.1109/TCNS.2024.3355044
    CONTRIBUTORS: Guoliang Wang; Siyong Song; Zhiqiang Li

  • Almost Sure Stabilization of Continuous-Time Semi-Markov Jump Systems via an Earliest Deadline First Scheduling Controller

    IEEE Transactions on Systems, Man, and Cybernetics: Systems
    2024-01 | Journal article
    DOI: 10.1109/TSMC.2023.3318610
    CONTRIBUTORS: Guoliang Wang; Yunshuai Ren; Zhiqiang Li

  • Stability and stabilisation of Markovian jump systems under fast switching: an averaging approach

    Journal of Control and Decision
    2023-10-02 | Journal article
    DOI: 10.1080/23307706.2022.2127427
    CONTRIBUTORS: Guoliang Wang; Yande Zhang; Yunshuai Ren

Ahmad Kamandi | Optimization | Best Researcher Award

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Dr. Ahmad Kamandi | Optimization | Best Researcher Award

Ahmad Kamandi is an accomplished researcher in applied mathematics, specializing in optimization algorithms and numerical analysis. He is currently affiliated with the Department of Mathematics at the University of Science and Technology of Mazandaran, Iran. His work focuses on developing novel algorithms for solving nonlinear equations, variational inequalities, and optimization problems. With numerous publications in high-impact journals, Kamandi has significantly contributed to computational mathematics and machine learning applications. His research interests include trust-region methods, projection-based algorithms, and support vector machines. Over the years, he has collaborated with leading researchers to advance mathematical optimization techniques. Kamandi’s expertise extends to signal processing, image retrieval, and deep learning applications. His academic excellence is reflected in his outstanding rankings and awards during his undergraduate and graduate studies. He actively contributes to mathematical research and continues to push the boundaries of computational problem-solving. 📚🔢

Profile

Education 🎓

Ahmad Kamandi holds a Ph.D. in Applied Mathematics from Razi University, Kermanshah, Iran (2011-2015), where he developed modified trust-region methods for optimization under the supervision of Prof. Keyvan Amini. He earned his M.Sc. in Applied Mathematics from Sharif University of Technology, Tehran (2008-2011), focusing on Lagrangian methods for degenerate nonlinear programming, guided by Prof. Nezam Mahadadi Amiri. His academic journey began at Razi University, where he completed a B.Sc. in Applied Mathematics (2004-2008). His exceptional academic performance placed him at the top of his class in both undergraduate and Ph.D. programs. His expertise spans optimization algorithms, variational inequalities, and numerical methods, forming the foundation for his extensive research contributions. Kamandi’s education has equipped him with the skills necessary to develop innovative solutions for complex mathematical and computational problems. 📖

Research Interests 🔬

Ahmad Kamandi’s research centers on optimization algorithms, numerical methods, and computational mathematics. His expertise includes trust-region methods, inertial projection-based algorithms, and variational inequalities, with applications in signal processing, image retrieval, and machine learning. His work extends to support vector machines, hyper-parameter tuning, and monotone equation solving, impacting fields like AI and engineering. His notable contributions include developing novel inertial proximal algorithms and hybrid conjugate gradient methods. His recent studies focus on binary classification, hierarchical variational inequalities, and the intersection of optimization and deep learning. With numerous publications in leading journals, Kamandi continuously refines mathematical models for real-world applications. His research has practical implications in data science, financial modeling, and computational engineering, bridging the gap between theoretical mathematics and applied problem-solving. His contributions drive innovation in solving large-scale mathematical problems efficiently. 📊

Awards & Recognitions 🏅

Ahmad Kamandi has received multiple academic accolades, highlighting his excellence in mathematics. In 2012, he secured First Place in the Ph.D. Program at Razi University with an outstanding GPA of 19.26/20. In 2010, he was ranked Second in the M.Sc. Program at Sharif University of Technology with a GPA of 18.92/20. His academic brilliance was evident early on when he ranked 73rd out of 10,763 candidates in Iran’s Nationwide Master’s Examination (2008). Additionally, he achieved First Place in the B.Sc. Program at Razi University with a GPA of 16.85/20. These awards underscore his dedication to mathematical research and his ability to excel in rigorous academic environments. His outstanding performance has positioned him as a leading expert in applied mathematics, contributing to the advancement of optimization and computational methods. 🏅🎖️

Publications 📚

  • A novel projection-based method for monotone equations with Aitken Δ2 acceleration and its application to sparse signal restoration

    Applied Numerical Mathematics
    2025-07 | Journal article
    DOI: 10.1016/j.apnum.2025.02.013
    CONTRIBUTORS: Ahmad Kamandi

  • Relaxed-inertial derivative-free algorithm for systems of nonlinear pseudo-monotone equations

    Computational and Applied Mathematics
    2024-06 | Journal article
    DOI: 10.1007/s40314-024-02673-y
    CONTRIBUTORS: Abdulkarim Hassan Ibrahim; Sanja Rapajić; Ahmad Kamandi; Poom Kumam; Zoltan Papp

  • A NOVEL ALGORITHM FOR APPROXIMATING COMMON SOLUTION OF A SYSTEM OF MONOTONE INCLUSION PROBLEMS AND COMMON FIXED POINT PROBLEM

    Journal of Industrial and Management Optimization
    2023 | Journal article
    DOI: 10.3934/jimo.2021210

    EID:

    2-s2.0-85140006988

    Part of ISSN: 1553166X 15475816
    CONTRIBUTORS: Eslamian, M.; Kamandi, A.