Assoc. Prof. Dr. Seyed Abolfazl Shahzadeh Fazeli | Computational Intelligence | Associte Professsor at yazd University, Iran
Dr. Seyed Abolfazl Shahzadeh Fazeli is an Associate Professor at the Parallel Processing Lab, Department of Computer Science, Yazd University, Iran. With expertise in Computational Intelligence, Numerical Analysis, Machine Learning, and Parallel Algorithms, he has significantly contributed to cutting-edge research in high-performance computing and bioinformatics. Dr. Fazeli has supervised multiple Ph.D. and M.Sc. students and has collaborated on numerous international research projects. His work spans data mining, heuristic and metaheuristic algorithms, fuzzy systems, and numerical linear algebra applications. He has published extensively in high-impact journals and presented at renowned conferences. Recognized for his research excellence, he has received multiple awards and grants. His interdisciplinary approach has led to advancements in AI-driven chemical simulations, optimization techniques, and biomedical data analysis. With a passion for innovation, he continues to bridge the gap between theory and real-world computational applications.
Professional Profile :
Scopus
Summary of Suitability for Award:
Dr. Seyed Abolfazl Shahzadeh Fazeli is an exceptional researcher in Computational Intelligence, Numerical Analysis, Parallel Algorithms, and Machine Learning. As an Associate Professor at Yazd University, he has demonstrated excellence in both theoretical advancements and practical applications of AI-driven solutions in bioinformatics, chemistry, and large-scale data processing. With a strong publication record in high-impact journals, numerous Best Paper Awards, and international research collaborations, his contributions have significantly advanced the field of computational science. His expertise in fuzzy systems, heuristic algorithms, and AI-based modeling has led to innovative solutions in complex problem-solving, making his work highly influential and impactful. Dr. Fazeli’s groundbreaking research, strong academic contributions, and interdisciplinary innovations make him an outstanding candidate for the “Best Researcher Award”. His ability to bridge AI with scientific applications has not only pushed the boundaries of computational research but also fostered technological advancements with real-world impact. His commitment to research excellence, mentorship, and academic leadership makes him highly deserving of this prestigious recognition.
🎓Education:
Dr. Fazeli completed his Ph.D. in Computer Science at Yazd University, Iran, specializing in Parallel Algorithms and Numerical Analysis. His doctoral research focused on developing efficient high-performance computing models for large-scale data processing and computational intelligence applications. Before this, he earned his M.Sc. in Computer Science from Yazd University, where he worked on heuristic and metaheuristic algorithms, optimizing their performance in AI-driven problem-solving. His B.Sc. in Computer Science, also from Yazd University, provided him with a strong foundation in machine learning, fuzzy systems, and computational mathematics. Throughout his academic journey, he actively engaged in research, contributing to AI-based bioinformatics, numerical linear algebra, and data mining. His extensive background in both theoretical and applied computer science has made him a key contributor to advancements in computational modeling, high-speed processing techniques, and AI-powered chemical informatics.
🏢Work Experience:
Dr. Fazeli is an Associate Professor at Yazd University’s Parallel Processing Lab, where he has been leading research in computational intelligence and parallel computing for over a decade. He has taught advanced courses on machine learning, numerical optimization, parallel algorithms, and data mining, mentoring both undergraduate and postgraduate students. In addition to academia, he has worked as a visiting researcher at international institutions, collaborating on AI-driven bioinformatics and computational chemistry projects. His experience also includes consulting roles in industry, where he applied machine learning techniques to solve complex optimization and data analysis challenges. He has contributed as a reviewer for top-tier scientific journals and actively participates in international conferences as a keynote speaker. His extensive experience in AI, big data, and computational modeling allows him to contribute to both theoretical advancements and practical implementations in various interdisciplinary fields.
🏅Awards:
Dr. Fazeli has been recognized multiple times as an Outstanding Researcher at Yazd University for his contributions to computational intelligence and parallel processing. He has received national and international awards for his pioneering research in machine learning, heuristic algorithms, and AI-driven chemical simulations. Several of his publications in high-impact journals have been awarded Best Paper Awards at international conferences. He has also secured prestigious research grants for projects in bioinformatics, numerical analysis, and computational chemistry, further demonstrating his impact in these fields. In addition to research, he has been honored with Excellence in Teaching Awards, recognizing his dedication to mentoring and academic leadership. His interdisciplinary collaborations and groundbreaking contributions to AI-driven scientific research have earned him a strong reputation in the global scientific community.
🔬Research Focus:
Dr. Fazeli’s research is centered on Computational Intelligence, Parallel Algorithms, and Numerical Analysis, with applications in bioinformatics, AI-driven chemistry, and data mining. His work in high-performance computing enhances the efficiency of large-scale scientific computations and optimization problems. He has made significant contributions to heuristic and metaheuristic algorithms, improving their performance in solving real-world computational challenges. His expertise in fuzzy systems and machine learning enables him to develop intelligent models for biomedical data analysis, chemical simulations, and AI-based decision-making. He is also actively involved in numerical linear algebra applications, focusing on efficient data representation and computation. His interdisciplinary approach allows him to integrate computational methods with chemistry, healthcare, and engineering, making significant strides in AI-driven scientific discoveries.
Publication Top Notes:
“Improved Salp Swarm Optimization Algorithm Based on a Robust Search Strategy and a Novel Local Search Algorithm for Feature Selection Problems”
“Improving the Performance of the FCM Algorithm in Clustering Using the DBSCAN Algorithm”