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. 🚀📡📊
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 📚
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- Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach
H Bilal, B Yin, A Kumar, M Ali, J Zhang, J Yao
Soft Computing 27 (7), 4029-4039
- Real-time lane detection and tracking for advanced driver assistance systems
H Bilal, B Yin, J Khan, L Wang, J Zhang, A Kumar
2019 Chinese control conference (CCC), 6772-6777
- Reduction of multiplications in convolutional neural networks
M Ali, B Yin, A Kunar, AM Sheikh, H Bilal
2020 39th Chinese control conference (CCC), 7406-7411
- Advanced efficient strategy for detection of dark objects based on spiking network with multi-box detection
M Ali, B Yin, H Bilal, A Kumar, AM Shaikh, A Rohra
Multimedia Tools and Applications 83 (12), 36307-36327
- Adaptive preview control with deck motion compensation for autonomous carrier landing of an aircraft
AK Bhatia, J Jiang, A Kumar, SAA Shah, A Rohra, Z ZiYang
International Journal of Adaptive Control and Signal Processing 35 (5), 769-785
- Using feature entropy to guide filter pruning for efficient convolutional networks
Y Li, L Wang, S Peng, A Kumar, B Yin
Artificial Neural Networks and Machine Learning–ICANN 2019: Deep Learning …
- CorrNet: pearson correlation based pruning for efficient convolutional neural networks
A Kumar, B Yin, AM Shaikh, M Ali, W Wei
International Journal of Machine Learning and Cybernetics 13 (12), 3773-3783
- Building on prior lightweight CNN model combined with LSTM-AM framework to guide fault detection in fixed-wing UAVs
A Kumar, S Wang, AM Shaikh, H Bilal, B Lu, S Song
International Journal of Machine Learning and Cybernetics 15 (9), 4175-4191