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 📚

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

Mahmoud Alimoradi | Machine Learning | Best Researcher Award

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Mr. Mahmoud Alimoradi | Machine Learning | Best Researcher Award

Lahijan Azad ,Iran

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

Profile

Education

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

Research Interests

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

Work Experience

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

Publications