Micheal Arowolo | Machine Learning | Best Researcher Award

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Dr. Micheal Arowolo | Machine Learning | Best Researcher Award

Assistant Professor | Xavier University of Louisiana | United States

Dr. Micheal Olaolu Arowolo is an accomplished scholar, researcher, and educator in the field of computer science, with expertise in machine learning, health informatics, and bioinformatics. He currently serves as an Assistant Professor of Health Informatics at Xavier University of Louisiana, where he teaches master’s students in areas such as population health, statistics in health sciences, and healthcare quality. He earned his Ph.D. in Computer Science from Landmark University in Nigeria, building on a Master’s degree in Computer Science from Kwara State University and a Bachelor’s degree from Al-Hikmah University. He later advanced his academic career as a Post-doctoral Research Scholar at the University of Missouri’s Bond Life Sciences Center, where he contributed to the development of deep learning and machine learning models aimed at predicting relevant gene names in pathway figures for health practitioners. Dr. Arowolo’s teaching and research experience spans institutions in both the United States and Nigeria, where he has lectured and supervised students across a broad range of subjects, including artificial intelligence, data communication and networking, object-oriented programming, and computational theory. His research efforts have produced impactful publications in reputable journals indexed by Elsevier, IEEE, ISI, and Web of Science. He has also developed applied solutions for the United Nations Sustainable Development Goals, particularly SDG 11, by applying machine learning models to domains such as healthcare, telecommunications, and banking. His contributions to academic excellence helped Landmark University improve its global ranking significantly. An active member of the global research community, Dr. Arowolo belongs to several professional organizations, including IEEE, ACM, ISCB, and IAENG. He also serves as a reviewer and editorial board member for internationally recognized journals such as Heliyon, IEEE Access, and Journal of Big Data. His dedication to academic mentorship is reflected in his supervision of numerous graduate and undergraduate projects, guiding students to adopt innovative approaches to machine learning and computational methods. Recognized among the top 500 scholars in Nigeria by SciVal-Scopus, Dr. Arowolo has received certifications in SQL, Linux, Oracle, project management, and network administration. Through a blend of research, teaching, and leadership, he continues to contribute to knowledge creation, innovation, and the advancement of computational science and health informatics worldwide.

Profile:  Scopus | ORCID | Google Scholar

Featured Publications:

Arowolo, M. O., & co-authors. (n.d.). Enhancing cyber threat detection with an improved artificial neural network model. Data Science and Management.

Arowolo, M. O., & co-authors. (n.d.). Computational intelligence in big data analytics. In Book chapter.

Arowolo, M. O., & co-authors. (n.d.). A comprehensive evaluation of large language models in mining gene relations and pathway knowledge. Quantitative Biology.

Arowolo, M. O., & co-authors. (n.d.). Internet of things (IoT): Concepts, protocols, and applications. In Book chapter.

Arowolo, M. O., & co-authors. (n.d.). Adsorptive removal of synthetic food dyes using low-cost biochar: Efficiency prediction, kinetics and desorption index evaluation. Bioresource Technology Reports.

Arowolo, M. O., & co-authors. (n.d.). Gene name recognition in gene pathway figures using Siamese networks. In Conference proceedings.

Arowolo, M. O., & co-authors. (n.d.). Enhancing healthcare data security: An intrusion detection system for web applications with SVM and decision tree algorithms.

Dr. Zhiwei Zuo | Machine Learning | Best Researcher Award Lecturer

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Dr. Zhiwei Zuo | Machine Learning | Best Researcher Award

Lecturer | Hunan University | China

Dr. Zhiwei Zuo is a researcher specializing in machine learning, artificial intelligence, and machine unlearning. He earned his Ph.D. in Computer Science from Hunan University, China, under the supervision of Prof. Zhuo Tang, where his research explored machine unlearning, adversarial robustness, and efficient deep learning methods. He also gained international research experience as a visiting student at Nanyang Technological University, Singapore, under the mentorship of Prof. Anwitaman Datta, further expanding his expertise in trustworthy AI. Dr. Zuo is currently a lecturer at the Faculty of Artificial Intelligence in Education, Central China Normal University, where he continues to focus on designing algorithms that address data privacy, security, and robustness challenges in artificial intelligence systems. He has published in prestigious journals and conferences such as IEEE Transactions on Knowledge and Data Engineering, ICASSP, and Information Sciences. His work contributes to advancing trustworthy AI while ensuring ethical and responsible deployment of machine learning technologies.

Professional Profile

Scopus

Education

Dr. Zhiwei Zuo pursued his academic journey across several prestigious institutions. He completed his Ph.D. in Computer Science at Hunan University focusing on machine learning, adversarial robustness, and machine unlearning, under the supervision of Prof. Zhuo Tang. During his doctoral studies, he broadened his international exposure as a visiting student at Nanyang Technological University, Singapore where he collaborated with Prof. Anwitaman Datta at the School of Computer Science and Engineering, working on machine unlearning algorithms and data privacy in AI systems. Prior to his doctoral research, he earned his Bachelor’s degree in Computer Science from Central China Normal University  which laid the foundation for his interest in artificial intelligence and secure computing. Building on these academic milestones, he now serves as a Lecturer at the Faculty of Artificial Intelligence in Education, Central China Normal University where he integrates his strong educational background with active research and teaching.

Experience

Dr. Zuo’s professional and research experience spans academia and international collaboration in computer science. Currently, he is a Lecturer at the Faculty of Artificial Intelligence in Education, Central China Normal University, where he engages in teaching and research on artificial intelligence and its applications in education and security. His doctoral research at Hunan University provided him with extensive experience in algorithm development, adversarial machine learning, and machine unlearning frameworks. As a visiting student at Nanyang Technological University, Singapore, he collaborated with Prof. Anwitaman Datta on advancing fine-grained approaches to machine unlearning, combining theoretical insights with practical applications. Dr. Zuo has also contributed to multiple interdisciplinary projects, focusing on robust classifiers, text adversarial attacks, and efficient algorithms for high-performance computing. His teaching and mentorship roles further reflect his dedication to cultivating the next generation of AI researchers. His career demonstrates a blend of innovative research, teaching excellence, and international collaboration.

Research Focus

Dr. Zuo’s research focuses on machine unlearning, privacy-preserving artificial intelligence, adversarial robustness, and trustworthy machine learning systems. His work seeks to address one of the emerging challenges in AI—how to efficiently remove specific data or knowledge from trained models without retraining them entirely. He has developed fine-grained parameter perturbation methods and incremental learning frameworks to advance machine unlearning. His research also explores adversarial robustness, designing models that can withstand adversarial text and image attacks, and developing generative classifiers resistant to transfer attacks. Additionally, he has contributed to efficient high-performance algorithms for Bayesian text classification in distributed environments. His interdisciplinary approach combines theory, algorithm design, and practical implementation to ensure machine learning models remain reliable, secure, and ethically aligned. Currently, his research bridges AI and education, focusing on the safe deployment of machine learning systems in sensitive domains, while addressing privacy, fairness, and accountability in artificial intelligence.

Awards and Honors

Dr. Zuo has received recognition for his academic excellence, innovative research, and contributions to the field of artificial intelligence. His publications in top-tier venues such as IEEE Transactions on Knowledge and Data Engineering, ICASSP, and Information Sciences have been well received in the research community. As a doctoral student, he earned research scholarships and support for his outstanding performance and contributions at Hunan University. His visiting research tenure at Nanyang Technological University was also supported by competitive funding, reflecting the significance of his work in machine unlearning. Additionally, his contributions to adversarial robustness and parallel algorithms have been acknowledged through conference presentations and collaborative projects. Dr. Zuo has participated in international conferences, where his work received positive recognition for originality and practical relevance. His career highlights include balancing strong theoretical research with applied solutions in secure AI systems, establishing him as a promising researcher in trustworthy and privacy-preserving AI.

Publication Top Notes 

A distributed skewed stream processing system based on scoring high-frequency key perception

Year: 2025

Conclusion

Zhiwei Zuo’s impressive research experience, innovative research, and interdisciplinary collaboration make them a strong candidate for the Best Researcher Award. With further development of their publication record, global impact, and research translation, Zuo could solidify their position as a leading researcher in machine learning.