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.

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

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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 📚

Pritpal Singh | Ambiguous set theory | Best Researcher Award

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Dr. Pritpal Singh | Ambiguous set theory | Best Researcher Award

Pritpal Singh is an Assistant Professor at the Department of Data Science and Analytics, Central University of Rajasthan, India. He earned his Ph.D. in Computer Science and Engineering from Tezpur (Central) University in 2015 and has held various academic and research positions in India, Taiwan, and Poland. His expertise includes soft computing, optimization algorithms, time series forecasting, image analysis, and machine learning. He has published extensively in high-impact journals like IEEE Transactions, Elsevier, and Springer. His research focuses on advanced computational techniques, including quantum-based optimization and fMRI data analysis. Dr. Singh has received prestigious research fellowships, including a Postdoctoral Fellowship from Taiwan’s Ministry of Science and Technology and an International Visiting Research Fellowship from Poland’s Foundation for Polish Science. His work significantly contributes to artificial intelligence, data science, and computational modeling, making him a key figure in these fields. 🚀📊📚

Profile

Education 🎓

Dr. Pritpal Singh obtained his Ph.D. in Computer Science and Engineering from Tezpur (Central) University, Assam, India, in 2015, specializing in soft computing applications for time series forecasting. He completed his Master in Computer Applications (MCA) from Dibrugarh University, Assam, in 2008, following a B.Sc. in Physics, Chemistry, and Mathematics from the same university in 2005. His academic journey began with Higher Secondary (2002) from the Assam Higher Secondary Education Council and HSLC (1999) from the Secondary Education Board of Assam. His doctoral dissertation focused on improving fuzzy time series forecasting models through hybridization with neural networks and optimization techniques like particle swarm optimization. His strong foundation in computational sciences, mathematics, and engineering has shaped his research in AI-driven predictive modeling, optimization, and data analytics. 🎓📚🔬

Experience 👨‍🏫

Dr. Singh has extensive academic and research experience. He is currently an Assistant Professor at the Central University of Rajasthan (since June 2022). Previously, he was an Assistant Professor at CHARUSAT University, Gujarat (2015-2019), and a Lecturer at Thapar University, Punjab (2013-2015). His research experience includes serving as an Adjunct Professor (Research) at Jagiellonian University, Poland (2020-2022) and a Postdoctoral Research Fellow at National Taipei University of Technology, Taiwan (2019-2020). Throughout his career, he has mentored students, led research projects, and contributed significantly to data science, artificial intelligence, and computational modeling. His global exposure has enriched his expertise in optimization, machine learning, and interdisciplinary AI applications. 🌍📊

Research Interests 🔬

Dr. Singh’s research revolves around ambiguous set theory, optimization algorithms, time series forecasting, image analysis, and machine learning. He specializes in hybrid computational techniques, particularly quantum-based optimization and soft computing applications. His work extends to fMRI data analysis, mathematical modeling, and simulation. His research has been published in leading journals such as IEEE Transactions on Systems, Elsevier’s Information Sciences, and Artificial Intelligence in Medicine. His focus on interdisciplinary AI applications, particularly in healthcare and data science, has positioned him as a key contributor to advancing machine learning methodologies. 🧠📊🤖Awards & Recognitions 🏅

Dr. Singh has received multiple prestigious fellowships and recognitions. In 2019, he was awarded a Postdoctoral Research Fellowship by the Ministry of Science and Technology, Taiwan. In 2020, he received the International Visiting Research Fellowship from the Foundation for Polish Science, Poland. His contributions to artificial intelligence, optimization, and data science have been recognized globally through research grants, invited talks, and publications in top-tier journals. His work in soft computing and AI-driven predictive modeling continues to impact both academic and industrial research. 🏅🎖️📜

Publications 📚

  • Scopus 1-2023: P. Singh, An investigation of ambiguous sets and their application to
    decision-making from partial order to lattice ambiguous sets. Decision Analytics
    Journal (Elsevier), 08, 100286, 2023.
  • Scopus 2-2023: P. Singh, A general model of ambiguous sets to a single-valued ambiguous numberswith aggregation operators. Decision Analytics Journal (Elsevier), 08,
    100260, 2023.
  • Scopus 3-2023: P. Singh, Ambiguous set theory: A new approach to deal with unconsciousness and ambiguousness of human perception. Journal of Neutrosophic and
    Fuzzy Systems (American Scientific Publishing Group), 05(01), 52–58, 2023.
  • Scopus 4-2022: P. Singh, Marcin W ˛atorek, Anna Ceglarek, Magdalena F ˛afrowicz, and
    Paweł O´swi˛ecimka, Analysis of fMRI Time Series: Neutrosophic-Entropy Based
    Clustering Algorithm. Journal of Advances in Information Technology, 13(3), 224–
    229, 2022.

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

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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.

Vikas Palekar | Machine Leaning | Best Researcher Award

Published on by Cognitive Scientist Awards

Mr. Vikas Palekar | Machine Leaning | Best Researcher Award

 

Profile

Education

He is currently pursuing a Ph.D. in Computer Science and Engineering at Vellore Institute of Technology, Bhopal, Madhya Pradesh, since December 2018. His research focuses on developing an Adaptive Optimized Residual Convolutional Image Annotation Model with a Bionic Feature Selection Strategy. He holds a Master of Engineering (M.E.) in Information Technology from Prof. Ram Meghe College of Engineering Technology and Research, Badnera (SGBAU Amravati), which he completed in December 2012 with an impressive 88.00%, securing the first merit position in the university for the summer 2012 examination. Prior to that, he earned a Bachelor of Engineering (B.E.) in Computer Science and Engineering from Shri Guru Gobind Singhji Institute of Engineering Technology and Research, Nanded (SRTMNU, Nanded), in June 2007, achieving a commendable 74.40%.

Work experience

He is currently working as an Assistant Professor in the Department of Computer Engineering at Bajaj Institute of Technology, Wardha, since July 31, 2023. In addition to his teaching responsibilities, he serves as the Academic Coordinator of the department and has worked as a Senior Supervisor for the DBATY Winter-23 Exam at Government College of Engineering, Yavatmal.

Previously, he worked as an Assistant Professor (UGC Approved, RTMNU, Nagpur) in the Department of Computer Science and Engineering at Datta Meghe Institute of Engineering, Technology & Research, Wardha, from June 14, 2011, to June 30, 2023. During this tenure, he held the position of Head of the Department from April 21, 2016, to June 30, 2023. He taught various subjects, including Distributed Operating Systems, TCP/IP, System Programming, Data Warehousing and Mining, Artificial Intelligence, and Computer Architecture and Organization. Additionally, he contributed to university examinations as the Chief Supervisor in the Winter-2015 Examination and a committee member for the Summer-2013, Summer-2015, and Summer-2018 Examinations. He also played a key role in institutional development as a member of the Admission Committee, NBA & NAAC core committees at the department level, and as the convener of the National Level Technical Symposium “POCKET 16” organized by the CSE Department on March 16, 2016.

Earlier in his career, he served as an Assistant Professor in the Department of Computer Engineering at Bapurao Deshmukh College of Engineering, Wardha, from November 26, 2008, to April 30, 2011. He taught subjects such as Unix and Shell Programming, Object-Oriented Programming, and Operating Systems while also serving as a Department Exam Committee Member.

Achievement

He was the first university topper (merit) in M.Tech (Information Technology) and received the Best Paper Award at the 2021 International Conference on Computational Performance Evaluation (ComPE), organized by the Department of Biomedical Engineering, North Eastern Hill University (NEHU), Shillong, Meghalaya, India, from December 1st to 3rd, 2023. He has actively participated in various conferences, including presenting the paper “Label Dependency Classifier using Multi-Feature Graph Convolution Networks for Automatic Image Annotation” at ComPE 2021 in Shillong, India. He also presented his research on “Visual-Based Page Segmentation for Deep Web Data Extraction” at the International Conference on Soft Computing for Problem Solving (SocProS 2011) held from December 20-22, 2011. Additionally, he contributed to the Computer Science & Engineering Department at Sardar Vallabhbhai National Institute of Technology, Surat, by presenting “A Critical Analysis of Learning Approaches for Image Annotation Based on Semantic Correlation” from December 13-15, 2022. His work on “A Survey on Assisting Document Annotation” was featured at the 19th International Conference on Hybrid Intelligent Systems (HIS) at VIT Bhopal University, India, from December 10-12, 2022. Furthermore, he co-authored a study titled “Review on Improving Lifetime of Network Using Energy and Density Control Cluster Algorithm,” which was presented at the 2018 IEEE International Students’ Conference on Electrical, Electronics, and Computer Science (SCEECS) in Bhopal, India.

 

Publication

Dingming Wu | Computer Science | Best Researcher Award

Published on by Cognitive Scientist Awards

Dr. Dingming Wu | Computer Science | Best Researcher Award

 

Profile

  • scopus

Education

He holds a Ph.D. in Computer Science and Technology from Harbin Institute of Technology, where he studied under the supervision of Professor Xiaolong Wang from March 2018 to December 2022. Prior to that, he earned a Master’s degree in Probability Theory and Mathematical Statistics from Shandong University of Science and Technology in collaboration with the University of Chinese Academy of Sciences, completing his studies under the guidance of Professor Tiande Guo between September 2014 and July 2017. His academic journey began with a Bachelor’s degree in Information and Computational Science from Shandong University of Science and Technology, which he completed between September 2006 and July 2010.

Work experience

He is currently a Postdoctoral Fellow at the University of Electronic Science and Technology of China, Chengdu, a position he has held since December 2022 and will continue until December 2024. His research focuses on EEG signal processing and algorithm feature extraction, specifically addressing the challenges posed by the complexity and individual variations of EEG signals. Given the limitations of traditional classification methods, his work aims to enhance recognition accuracy through advanced deep learning models, improving the decoding of intricate EEG signals and optimizing control accuracy. Additionally, he integrates artificial intelligence technologies to predict user intentions and provide proactive responses, ultimately enhancing the interactive experience. His system is designed for long-term stability and adaptability, leveraging self-learning mechanisms based on user feedback.

Previously, he worked as a Data Analyst at Qingdao Sanlujiu International Trade Co., Ltd., Shanghai, from September 2010 to July 2014. In this role, he was responsible for conducting statistical analysis of trade flow data.

Publication

  • [1] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Jointly modeling transfer learning of
    industrial chain information and deep learning for stock prediction[J]. Expert Systems with
    Applications, 2022, 191(7):116257.
    [2] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu.A hybrid framework based on extreme
    learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock
    prediction[J]. Expert Systems with Applications, 2022, 207(24):118006.
    [3] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Construction of stock portfolio based on
    k-means clustering of continuous trend features[J]. Knowledge-Based Systems, 2022,
    252(18):109358.
    [4] Dingming Wu, Xiaolong Wang∗, Jingyong Su, Buzhou Tang, and Shaocong Wu. A labeling
    method for financial time series prediction based on trends[J]. Entropy, 2020, 22(10):1162.
    [5] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. A hybrid method based on extreme
    learning machine and wavelet transform denoising for stock prediction[J]. Entropy, 2021,
    23(4):440.
    Papers to be published:
    [6] Wavelet transform in conjunction with temporal convolutional networks for time series
    prediction. Journal: PATTERN RECOGNITION; Status: under review; Position: Sole
    Author.
    [7] A Multidimensional Adaptive Transformer Network for Fatigue Detection. Journal: Cognitive
    Neurodynamics; Status: accept; Position: First Author.
    [8] A Multi-branch Feature Fusion Deep Learning Model for EEG-Based Cross-Subject Motor
    Imagery Classification. Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL
    INTELLIGENCE; Status: under review; Position: First Author.
    [9] A Coupling of Common-Private Topological Patterns Learning Approach for Mitigating Interindividual Variability in EEG-based Emotion Recognition. Journal: Biomedical Signal
    Processing and Control; Status: Revise; Position: First Corresponding Author.
    [10] A Function-Structure Adaptive Decoupled Learning Framework for Multi-Cognitive Tasks
    EEG Decoding. Journal: IEEE Transactions on Neural Networks and Learning Systems;
    Status: under review; Position: Co-First Author.
    [11] Decoding Topology-Implicit EEG Representations Under Manifold-Euclidean Hybrid Space.
    Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI);
    Status: under review; Position: Second Corresponding Author.
    [12] Style Transfer Mapping for EEG-Based Neuropsychiatric Diseases Recognition. Journal:
    EXPERT SYSTEMS WITH APPLICATIONS; Status: under review; Position: Second
    Corresponding Author.
    [13] An Adaptive Ascending Learning Strategy Based on Graph Optional Interaction for EEG
    Decoding. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: Second Corresponding Author.
    [14] A Transfer Optimization Methodology of Graph Representation Incorporating CommonPrivate Feature Decomposition for EEG Emotion Recognition. Computer conference:
    International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review;
    Position: Second Corresponding Author.
    [15] An Interpretable Neural Network Incorporating Rule-Based Constraints for EEG Emotion
    Recognition. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: First Author.