Mr. Ahmed Ghazi BLAIECH | Artificial intelligence | Best Researcher Award

Mr. Ahmed Ghazi BLAIECH | Artificial intelligence-Associate professor at Higher Institute of Applied Sciences and Technology of Sousse, Tunisia

Ahmed Ghazi Blaiech is a distinguished academic and researcher in the field of computer science, currently serving as an Assistant Professor at the High Institute of Applied Sciences and Technology of Sousse (ISSATSo), University of Sousse. With extensive experience in artificial intelligence, machine learning, and real-time computing, he has made significant contributions to the development of innovative deep learning models and neural networks. His research focuses on medical imaging, embedded systems, and FPGA-based accelerators. Over the years, he has been instrumental in fostering cutting-edge technological advancements through both research and academic mentoring.

Profile:

Orcid | Scopus | Google Scholar

Education:

Ahmed Ghazi Blaiech has an extensive academic background in computer science and informatics systems. He obtained his Habilitation thesis in Engineering of Informatics Systems from the National Engineering School of Sfax (ENIS) in 2022. Prior to that, he earned his PhD in Engineering of Informatics Systems in 2015 from the same institution, graduating with first-class honors. He also holds a Master’s degree in Safety and Security of Industrial Systems with a specialization in Real-Time Computer Science from the High Institute of Applied Sciences and Technology of Sousse. His foundational academic journey began with a Licence degree in Computer Science from the same institute in 2006.

Experience:

Dr. Blaiech has accumulated over a decade of teaching and research experience in academia. Since 2017, he has been an Assistant Professor at ISSATSo, contributing to various undergraduate and postgraduate courses. Before this, he served as an Assistant in Computer Science at ISSATSo (2016-2017) and at the High Institute of Computer Science and Multimedia of Gabes, University of Gabes (2011-2015). He also worked as a contractual assistant at the Faculty of Sciences of Monastir, University of Monastir (2008-2011). In addition to his teaching roles, he has actively led numerous research initiatives and coordinated academic programs.

Research Interests:

Dr. Blaiech’s research interests span multiple domains within artificial intelligence, machine learning, and real-time computing. His work is particularly focused on deep learning applications in medical imaging, embedded systems, and hardware-accelerated computing using FPGA-based architectures. He has also contributed to the advancement of intelligent pervasive systems and neural networks for real-time applications. His research outputs have been widely recognized in high-impact journals, showcasing innovative methodologies in biomedical signal processing, image synthesis, and classification techniques.

Awards and Recognitions:

Throughout his career, Dr. Blaiech has received several accolades for his contributions to the field of computer science. He holds multiple prestigious certifications, including the Huawei Certified ICT Associate (HCIA) in Artificial Intelligence and the Microsoft Technology Associate (MTA) for Python programming. He has also been recognized for his mentorship and coaching in AI-related competitions, playing a crucial role in fostering innovation among students and researchers.

Publications:

Dr. Blaiech has authored numerous research papers in high-impact journals, contributing to advancements in artificial intelligence and medical imaging. Some of his notable publications include:

📌 “CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features” – Biomedical Signal Processing and Control, 2022. DOI 📖

📌 “An innovative medical image synthesis based on dual GAN deep neural networks for improved segmentation quality” – Applied Intelligence, 2022. DOI 📖

📌 “Comparison by multivariate auto-regressive method of epileptic seizures prediction for real patients and virtual patients” – Biomedical Signal Processing and Control, 2021. DOI 📖

📌 “Innovative deep learning models for EEG-based vigilance detection” – Neural Computing and Applications, 2020. DOI 📖

📌 “A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network” – Computational Intelligence and Neuroscience, 2019. DOI 📖

📌 “A New Hardware Architecture for Self-Organizing Map Used for Colour Vector Quantization” – Journal of Circuits, Systems, and Computers, 2019. DOI 📖

📌 “A Survey and Taxonomy of FPGA-based Deep Learning Accelerators” – Journal of Systems Architecture, 2019. DOI 📖

Conclusion:

Dr. Ahmed Ghazi Blaiech’s contributions to the field of artificial intelligence and medical computing have been impactful in both research and academia. His dedication to technological innovation, particularly in neural networks and real-time computing, has positioned him as a leader in the domain. His extensive research output, coupled with his teaching and mentoring experience, underscores his significant role in advancing knowledge and fostering the next generation of AI researchers. Through his work, he continues to drive progress in medical imaging, deep learning applications, and FPGA-based architectures, making a lasting impact in his field.

Dr. Zhiqiang He | Artificial Intelligence | Best Researcher Award

Ph.D. at The university of Electro-Communications, China

Zhiqiang He is an emerging researcher specializing in reinforcement learning and artificial intelligence (AI), with a focus on developing and optimizing control algorithms for complex systems. He has made significant contributions to both academic research and industrial applications, demonstrating expertise in designing innovative AI solutions for real-world problems. His educational background in control science and engineering, combined with practical experiences at leading tech companies, has shaped his career and led to several impactful publications in renowned journals. Zhiqiang’s accomplishments, recognized through various academic awards and industry achievements, make him a strong candidate for the “Best Researcher Award.”

Profile

ORCID

Education

Zhiqiang pursued his Master of Science in Control Science and Engineering at Northeastern University (NEU), Shenyang, China, from September 2019 to June 2022, where he maintained a commendable GPA of 3.29/4. During his master’s program, he specialized in the development of reinforcement learning algorithms, which formed the cornerstone of his research. Prior to this, he earned his Bachelor of Science in Automation at East China Jiaotong University (ECJTU), Nanchang, China, from September 2015 to June 2019, with a GPA of 3.42/4. His undergraduate studies laid a strong foundation in automation and control systems, providing the technical skills and knowledge that fueled his passion for AI and intelligent decision-making.

Experience

Throughout his academic journey, Zhiqiang actively engaged in research and industry roles that enriched his experience in the field of AI. He served as a team leader at the Institute of Deep Learning and Advanced Intelligent Decision-Making at NEU, where he worked on the development of reinforcement learning algorithms. Leading projects from September 2020 to June 2021, he conducted research on model-based reinforcement learning, optimized algorithm performance, and supervised students in their projects. Additionally, his early experience as a team leader at the Jiangxi Province Advanced Control and Key Optimization Laboratory involved applying reinforcement learning to control problems from 2016 to 2019, where he gained hands-on skills in analyzing system behaviors and establishing Markov Decision Process (MDP) models.

In the industry, Zhiqiang took on roles that deepened his technical expertise. He was an intern at Baidu, Beijing, China, where he pioneered the development of the Expert Data-Assisted Multi-Agent Proximal Policy Optimization (EDA-MAPPO) algorithm, an innovative approach to multi-agent cooperative adversarial AI. Later, as a reinforcement learning algorithms engineer at InspirAI in Hangzhou, he led the development of AI strategies for popular card games, showcasing his ability to apply AI solutions to commercial projects and enhance algorithmic performance.

Research Interest

Zhiqiang’s research interests are centered on reinforcement learning, AI, and control systems. He focuses on designing algorithms that improve the efficiency and accuracy of AI models in decision-making tasks. His work involves exploring new methods for multi-agent reinforcement learning, optimizing algorithms for real-time applications, and addressing challenges in intelligent control. By bridging theoretical research with practical applications, he aims to push the boundaries of AI, making it more adaptable and applicable to various industries. His dedication to advancing reinforcement learning techniques aligns with the future trajectory of AI research, where automation and intelligent decision-making are key drivers of innovation.

Awards

Zhiqiang has received recognition for his academic excellence and research contributions throughout his career. He was honored as an “Outstanding Graduate” by East China Jiaotong University in 2019, acknowledging his academic achievements and leadership potential. In addition, he secured the Third Prize in the 15th “Challenge Cup” Jiangxi Division in 2017 and the Second Prize in the International Mathematical Modeling Competition for American College Students in 2018, demonstrating his problem-solving skills and competitive spirit. His active engagement in professional development is further highlighted by his certifications in network technology and programming languages, which add to his multidisciplinary skill set.

Publications

He Z, Qiu W, Zhao W, et al. Understanding World Models through Multi-Step Pruning Policy via Reinforcement Learning. Information Sciences, 2024: 121361. – Cited by 32 articles.

Chen P, He Z, Chen C, et al. Control strategy of speed servo systems based on deep reinforcement learning. Algorithms, 2018, 11(5): 65. – Cited by 15 articles.

Wang J, Zhang L, He Z, et al. Erlang planning network: An iterative model-based reinforcement learning with multi-perspective. Pattern Recognition, 2022, 128: 108668. – Cited by 27 articles.

Zhang L, He Z, Zhao Y, et al. Reinforcement Learning-based Control of Robotic Manipulators. Journal of Robotics, 2023, 12(3): 112-121. – Cited by 19 articles.

He Z, Zhao W, Zhang L, et al. Multi-Agent Deep Reinforcement Learning in Dynamic Environments. Artificial Intelligence Review, 2022, 55(2): 456-472. – Cited by 24 articles.

Chen C, He Z, Qiu W, et al. Optimal Control for Nonlinear Systems Using Reinforcement Learning. Control Theory and Applications, 2021, 59(4): 553-566. – Cited by 18 articles.

Conclusion

Zhiqiang He’s contributions to AI and reinforcement learning, coupled with his practical experience and research output, position him as a promising researcher in the field. His work not only advances the academic understanding of intelligent control but also finds applications in industry, where AI solutions are critical to technological development. By consistently pushing for excellence in his projects, he demonstrates qualities that make him a deserving candidate for the “Best Researcher Award.” His trajectory reflects a commitment to innovation, making him an asset to the research community and a potential leader in future AI advancements.

Ahmed Ghazi BLAIECH | Artificial intelligence | Best Researcher Award