Benyou Wang | Language Models | Best Researcher Award

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Mr. Benyou Wang | Language Models | Best Researcher Award

School of Data Science at The Chinese University of Hong Kong, Shenzhen, China

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Summary

Dr. Benyou Wang is an Assistant Professor jointly appointed in the School of Data Science and the School of Medicine at The Chinese University of Hong Kong, Shenzhen. He is also a Vice Director at the Center for Medical Artificial Intelligence and a Principal Investigator (PI) of multiple projects related to large language models (LLMs) and healthcare AI. Dr. Wang is a recipient of the prestigious Marie Skłodowska-Curie Fellowship and has spearheaded the development of "HuatuoGPT," the first large-scale medical LLM successfully deployed across 11 public hospitals in Shenzhen, impacting over half a million residents. His work bridges scientific innovation, real-world implementation, and industrial transformation, earning widespread media and institutional acclaim.

Educational Background

Dr. Wang earned his Ph.D. in Information Science and Technology from the University of Padua, Italy (2018–2022), funded by the EU's Marie Skłodowska-Curie Actions. He holds an M.Sc. in Computer Science from Tianjin University, China (2014–2017), where he specialized in pattern recognition and intelligent systems, and a B.Sc. in Software Engineering from Hubei University of Automotive Technology (2010–2014). He completed his secondary education at the prestigious Huanggang Middle School in Hubei Province.

Professional Experience

Dr. Wang began his career as an associate researcher at Tencent and later joined the University of Padua as a full-time Marie Curie Researcher. He has held visiting research appointments at institutions like the University of Montreal, University of Amsterdam, University of Copenhagen, and the Chinese Academy of Sciences. He interned at Huawei’s Noah’s Ark Lab and has delivered numerous invited talks worldwide. Since 2022, he has been teaching and leading research at CUHK-Shenzhen while supervising multiple Ph.D. and undergraduate students.

Research Interests

Dr. Wang’s research focuses on large language models (LLMs), their applications in vertical domains like healthcare and multilingual systems, quantum-inspired natural language processing, and information retrieval. He is deeply involved in the development of explainable AI, multimodal LLMs, and efficient LLM training. His work often explores the theoretical foundations of LLM alignment and evaluation and has recently expanded into medical reasoning and visual-language integration.

Author Metrics

Dr. Wang has authored over 40 peer-reviewed papers in top-tier venues such as ICLR, NeurIPS, ICML, NAACL, ACL, EMNLP, SIGIR, and AAAI. As of April 2024, his Google Scholar profile reports over 4,965 citations and an H-index of 37. He is the first or corresponding author on several high-impact studies and is actively engaged as a reviewer and Area Chair for major NLP and ML conferences.

Awards and Honors

Dr. Wang has received multiple Best Paper Awards, including at ICLR Financial AI 2025, NLPCC 2022, NAACL 2019, and SIGIR 2017. He was honored with the Huawei Spark Award (presented by Ren Zhengfei), and his HuatuoGPT project has been recognized in national strategic AI deployment plans. He also earned funding from Tencent’s Rhino-Bird Project, Huawei’s AI Top 100 Universities Program, and CCF-DiDi’s Gaia Scholar Initiative. His work has been featured in Nature, CCTV, Financial Times, and Global Times, among others.

Publication Top Notes

1. Learning from Peers in Reasoning Models

Authors: T. Luo, W. Du, J. Bi, S. Chung, Z. Tang, H. Yang, M. Zhang, B. Wang
Venue: arXiv preprint arXiv:2505.07787
Year: 2025
Summary:
This paper proposes a novel peer-learning framework where multiple large language models (LLMs) interact to enhance their reasoning abilities. By sharing intermediate reasoning steps and critiques, the models improve logical consistency and performance across various reasoning tasks.

2. Pushing the Limit of LLM Capacity for Text Classification

Authors: Y. Zhang, M. Wang, Q. Li, P. Tiwari, J. Qin
Venue: Companion Proceedings of the ACM on Web Conference 2025, pp. 1524–1528
Year: 2025
Summary:
The study investigates the potential of large language models for multi-class text classification without traditional fine-tuning. Using prompt engineering and strategic data augmentation, the authors demonstrate competitive or superior performance compared to classical approaches.

3. Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement

Authors: Z. Cheng, L. Zhou, F. Jiang, B. Wang, H. Li
Venue: Proceedings of the ACM on Web Conference 2025, pp. 2677–2688
Year: 2025
Summary:
This work moves beyond binary classification of AI-generated text and introduces a fine-grained detection system that recognizes multiple roles and the degree of AI involvement. The proposed model offers better insights into collaborative human-AI authored content.

4. UCL-Bench: A Chinese User-Centric Legal Benchmark for Large Language Models

Authors: R. Gan, D. Feng, C. Zhang, Z. Lin, H. Jia, H. Wang, Z. Cai, L. Cui, Q. Xie, ... B. Wang (et al.)
Venue: Findings of the Association for Computational Linguistics: NAACL 2025, pp. 7945–7988
Year: 2025
Summary:
This paper introduces UCL-Bench, a comprehensive legal benchmark in Chinese designed for evaluating LLMs in real-world legal advisory tasks. It emphasizes user intent, fairness, and practical utility, and serves as a tool for the responsible deployment of legal AI systems.

5. Huatuo-26M: A Large-scale Chinese Medical QA Dataset

Authors: X. Wang, J. Li, S. Chen, Y. Zhu, X. Wu, Z. Zhang, X. Xu, J. Chen, J. Fu, X. Wan, ... B. Wang (et al.)
Venue: Findings of the Association for Computational Linguistics: NAACL 2025, pp. 3828–3848
Year: 2025
Summary:
Huatuo-26M is a massive, high-quality Chinese medical question-answering dataset with 26 million entries. It supports the development of specialized medical LLMs like HuatuoGPT, which has been implemented in clinical settings and widely adopted across hospitals in Shenzhen.

Conclusion

Dr. Benyou Wang exemplifies the modern researcher's ideal, seamlessly combining technical depth, interdisciplinary application, global engagement, and measurable societal impact. His pioneering contributions—such as the development and real-world deployment of HuatuoGPT in healthcare, the creation of multilingual LLM benchmarks, and innovative work on fine-grained AI-generated content detection—underscore his leadership in advancing language model research. With a proven trajectory of sustained excellence, high-impact publications, and international recognition, Dr. Wang is not only a strong nominee but also a front-runner for the Best Researcher Award in Language Models. His continued expansion into global collaboration and theoretical grounding promises to shape the future landscape of natural language processing.

Changda Lei | Artificial intelligence | Best Academic Researcher Award

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Dr. Changda Lei | Artificial intelligence | Best Academic Researcher Award

Resident physician at First Affiliated Hospital of Soochow University, China

Professional Profile

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Summary

Dr. Changda Lei is a medical doctor and early-career researcher specializing in gastrointestinal tumors, digestive endoscopy, and artificial intelligence (AI) in clinical diagnostics. He is currently affiliated with the Department of Gastroenterology at the First Affiliated Hospital of Soochow University, China. His interdisciplinary expertise bridges medical imaging, clinical gastroenterology, and AI-driven diagnostic systems, with a particular focus on enhancing early cancer detection.

Educational Details

Dr. Lei earned his Doctor of Medicine degree with a residency in Gastroenterology from the First Affiliated Hospital of Soochow University, China. His doctoral thesis, titled “Artificial intelligence for early gastric cancer boundary recognition in NBI and NF-NBI endoscopic images,” was supervised by Prof. Rui Li and represents a significant contribution to AI applications in endoscopic image analysis for early cancer detection.

Professional Experience

During his residency and doctoral training at Soochow University, Dr. Lei gained clinical and research experience in gastroenterological procedures and endoscopic imaging. He has collaborated closely with multidisciplinary teams, including radiologists, computer scientists, and oncologists, to develop and validate AI-assisted diagnostic tools. His co-authored works involve both methodological development and clinical validation, marking him as a key contributor to translational medicine in the field of digestive oncology.

Research Interests

Dr. Lei’s research interests lie at the intersection of gastrointestinal tumor diagnostics, digestive endoscopy, and artificial intelligence. He focuses on improving early detection of gastric cancer, particularly through boundary recognition in NBI and NF-NBI endoscopic images. His broader research also explores multi-task learning, semantic segmentation, and clinical integration of AI tools for real-time diagnostic support in endoscopy suites.

Author Metrics

Although early in his academic career, Dr. Lei has co-authored multiple peer-reviewed articles in reputable international journals. His publications in Annals of Medicine, Scandinavian Journal of Gastroenterology, and Expert Systems with Applications demonstrate a growing influence in both clinical and AI research communities. His ORCID ID is 0000-0001-7908-7011, and his citation metrics are expected to rise with his growing publication footprint in multidisciplinary fields.

Awards and Honors

While formal awards are not explicitly listed, Dr. Lei’s contributions to high-impact publications and participation in cutting-edge research—such as the application of task-specific prompting in AI models for endoscopy—indicate peer recognition and significant academic promise. His collaborative work with senior scientists like Prof. Rui Li and publication in top-tier journals positions him as a rising expert in the medical AI and gastroenterology research community.

Publication Top Notes

1. Artificial Intelligence-Assisted Diagnosis of Early Gastric Cancer: Present Practice and Future Prospects

  • Authors: Changda Lei, Wenqiang Sun, Kun Wang, Ruirong Weng, Xiuji Kan, Rui Li

  • Journal: Annals of Medicine

  • Volume/Issue: Volume 57, Issue 1

  • Article ID: 2461679

  • Publication Date: 2025

  • DOI: 10.1080/07853890.2025.2461679

  • Summary:
    This article reviews current applications and future directions for artificial intelligence (AI) in the diagnosis of early gastric cancer (EGC). It highlights advances in endoscopic imaging, especially NBI (Narrow Band Imaging) and AI-based pattern recognition, and discusses clinical integration, challenges, and prospects for real-time implementation.

2. Neonatal Lupus Erythematosus: An Acquired Autoimmune Disease to Be Taken Seriously

  • Authors: Wenqiang Sun, Changchang Fu, Xinyun Jin, Changda Lei, Xueping Zhu

  • Journal: Annals of Medicine

  • Publication Date: December 31, 2025

  • DOI: 10.1080/07853890.2025.2476049

  • Summary:
    This clinical review focuses on neonatal lupus erythematosus (NLE), a rare but significant autoimmune condition affecting newborns. The article emphasizes early diagnosis, maternal screening, and therapeutic strategies, highlighting the need for interdisciplinary vigilance and patient-specific care.

Conclusion

Dr. Changda Lei is an exceptionally promising early-career academic who has already made meaningful contributions to the convergence of AI and gastrointestinal oncology. His research is not only innovative and clinically relevant but also indicative of leadership in next-generation diagnostic solutions.

Ahmad Hassanat | Machine Learning | Best Researcher Award

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Prof. Ahmad Hassanat | Machine Learning | Best Researcher Award

Professor at Mutah University, Jordan

Professional Profile

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Summary

Prof. Ahmad B. A. Hassanat is a Full Professor of Computer Science at Mutah University, Jordan, and a senior IEEE member. He is globally recognized for his extensive contributions to artificial intelligence, machine learning, biometrics, and image processing. With over two decades of academic and research experience, he has authored numerous impactful papers and books and is widely known for pioneering innovative techniques like the "Hassanat Distance" metric and deep learning-based biometric systems. He is also active in international collaborations, editorial work, and AI-driven healthcare research.

Educational Details

Prof. Hassanat earned his Ph.D. in Computer Science from the University of Buckingham, UK,, with a focus on automatic lip-reading. He holds an M.Sc. in Computer Science from Al al-Bayt University, Jordan, where he specialized in fast string matching algorithms. He completed his B.Sc. in Computer Science at Mutah University, Jordan. His academic foundation reflects a strong blend of theoretical depth and applied research skills in computing and AI.

Professional Experience

Prof. Hassanat has served in multiple academic roles across Jordan and Saudi Arabia, including as a Full Professor at Mutah University and the University of Tabuk. He was Head of the IT Department at Mutah University and a visiting researcher at the Sarajevo School of Science and Technology. Earlier in his career, he worked for the Jordanian Armed Forces as a programmer and systems analyst, where he developed over a dozen mission-critical ICT systems. He is also a founder or co-founder of academic programs, conferences, and novel biometric solutions.

Research Interests

His research spans machine learning, artificial intelligence, image processing, biometrics, pattern recognition, and evolutionary algorithms. He is known for practical innovations such as deep learning for veiled-face recognition, genetic algorithm optimization, voice-based Parkinson’s detection, and machine learning models for epidemiology, security, and finance. He also created the widely referenced Hassanat Distance, improving classifier performance in imbalanced data scenarios.

Author Metrics

Prof. Hassanat has published over 100 journal articles and conference papers, with an H-index of 33, i10-index of 56, and more than 4,000 citations. His work is featured in top journals such as IEEE Access, PLOS ONE, Sustainability, Applied Sciences, and Computers. His algorithmic contributions and models are highly cited in the fields of AI, healthcare informatics, and big data analytics.

Awards and Honors

Prof. Hassanat has been named among the world’s top 2% scientists by Stanford–Elsevier in 2021, 2022, and 2023. He has received the Best Scientist award at Mutah University for 2023 and 2024, and multiple competitive research grants from Jordan and Saudi Arabia. He was the recipient of Mutah University’s Distinguished Researcher Award (2018, 2019), and granted IEEE Senior Membership for his research excellence. His innovations, including terrorist identification from hand gestures and COVID-19 forecasting tools, have received global media attention.

Publication Top Notes

1. Deep learning computer vision system for estimating sheep age using teeth images

  • Authors: AB Hassanat, MA Al-Sarayreh, AS Tarawneh, MA Abbadi, et al.

  • Journal: Connection Science

  • Volume/Issue: 37 (1)

  • Article ID: 2506456

  • Year: 2025

  • Summary:
    This study presents a deep learning-based computer vision system designed to estimate the age of sheep by analyzing images of their teeth. The model likely leverages convolutional neural networks (CNNs) or similar architectures to accurately assess age-related dental features, offering a non-invasive and automated method for livestock age estimation that can assist farmers and veterinarians.

  • Citations: Not provided

  • Access: Details not provided

2. ICT: Iterative Clustering with Training: Preliminary Results

  • Authors: AB Hassanat, AS Tarawneh, AS Alhasanat, M Alghamdi, K Almohammadi, et al.

  • Conference: 2025 International Conference on New Trends in Computing Sciences (ICTCS)

  • Year: 2025

  • Summary:
    This paper introduces a novel method named Iterative Clustering with Training (ICT), presumably a machine learning or data clustering approach. Preliminary results demonstrate its effectiveness in improving clustering accuracy or training efficiency for datasets common in computing science. The approach likely combines clustering with supervised training iterations for better performance.

3. Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing

  • Authors: E Al-Khlifeh, AS Tarawneh, K Almohammadi, M Alrashidi, R Hassanat, et al.

  • Journal: Parasites & Vectors

  • Volume/Issue: 18 (1)

  • Article Number: 33

  • Year: 2025

  • Summary:
    This research applies decision tree-based machine learning techniques to mine laboratory data for efficient and accurate diagnosis of amebiasis. The study demonstrates how data mining on clinical data combined with decision trees can improve testing accuracy and streamline diagnostic procedures in parasitology.

4. Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach

  • Authors: AS Tarawneh, AK Al Omari, EM Al-Khlifeh, FS Tarawneh, M Alghamdi, et al.

  • Book/Series: Advances and Applications in Bioinformatics and Chemistry

  • Pages: 159-178

  • Year: 2024

  • Summary:
    This paper proposes a non-invasive method for cancer detection by combining blood test results with predictive modeling approaches, likely using machine learning algorithms. The approach aims to provide an early, cost-effective screening tool for cancer by analyzing biomarkers and patterns in blood test data.

5. Extended spectrum beta-lactamase bacteria and multidrug resistance in Jordan are predicted using a new machine-learning system

  • Authors: EM Al-Khlifeh, IS Alkhazi, MA Alrowaily, M Alghamdi, M Alrashidi, et al.

  • Journal: Infection and Drug Resistance

  • Pages: 3225-3240

  • Year: 2024

  • Summary:
    This study develops and applies a machine learning system to predict the occurrence of extended spectrum beta-lactamase (ESBL) producing bacteria and multidrug resistance patterns in Jordan. The predictive model aids in understanding and managing antibiotic resistance, supporting healthcare decision-making and antimicrobial stewardship.

Conclusion

Prof. Ahmad Hassanat embodies the qualities of a world-class researcher—his work is innovative, deeply applied, and globally relevant. From introducing original metrics and models in AI to developing life-saving diagnostic systems and biometric security applications, his impact is both academic and practical.

His dedication to research excellence, mentorship, and cross-disciplinary innovation makes him highly deserving of the Best Researcher Award in Machine Learning.

Shakila Rahman | Machine Learning | Best Researcher Award

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Ms. Shakila Rahman | Machine Learning | Best Researcher Award

Lecturer at American International University, Bangladesh

Author Profile

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Summary

Shakila Rahman is a dedicated academician currently serving as a Lecturer in the Department of Computer Science at the Faculty of Science and Technology, American International University-Bangladesh (AIUB). She holds a strong academic background in Artificial Intelligence and Computer Engineering, with her research focusing on emerging areas such as UAV networking, wireless sensor networks, optimization algorithms, and machine learning. Shakila is actively involved in mentoring students, guiding projects, and publishing impactful research in reputed platforms.

Educational Details

Shakila Rahman earned her M.Sc. in AI & Computer Engineering from the University of Ulsan, South Korea, in 2023 with an impressive CGPA of 4.00 out of 4.50. She completed her B.Sc. in Computer Science and Engineering from International Islamic University Chittagong (IIUC), Bangladesh, in 2019, securing a CGPA of 3.743 out of 4.00. Prior to her university education, she completed her Higher Secondary Certificate (HSC) from Cox’s Bazar Govt. College and Secondary School Certificate (SSC) from Cox’s Bazar Govt. Girls’ High School.

Professional Experience

Shakila is currently employed as a Lecturer in the Department of Computer Science and Engineering at AIUB, Dhaka, Bangladesh, where she has been working since January 2023. She previously served as a Graduate Research Assistant at the University of Ulsan, South Korea, from September 2020 to December 2022 under Professor Seokhoon Yoon. Additionally, she worked as an Undergraduate Teaching Assistant at IIUC in 2019. She has participated in technical boot camps and workshops and actively contributes to academic supervision, having guided several student projects and a machine learning-based thesis group.

Research Interests

Her research interests span a wide range of cutting-edge topics including UAV Networking, Wireless Sensor Networks, Network Systems, Optimization Algorithms, Machine Learning, Deep Learning, Image Processing, and AR/VR Applications in Artificial Intelligence. These multidisciplinary areas reflect her focus on building intelligent and adaptive systems for real-world applications.

Author Metrics

Shakila Rahman actively maintains a presence on prominent academic platforms. Her ResearchGate profile can be found at https://www.researchgate.net/profile/Shakila-Rahman-3, and her ORCID ID is 0000-0001-6375-4174. She is also available on LinkedIn at Shakila Rahman. Her published works and citation records are regularly updated on these platforms.

Awards and Honors

During her master's studies, Shakila was awarded the prestigious Brain Korea 21 (BK21) Scholarship and a fully funded AF1 scholarship at the University of Ulsan, valued at approximately USD 21,000. She also received funding from Korean Government-supported National Research Foundation (NRF) projects to support her graduate research publications. These accolades recognize her academic excellence and research contributions in the field of computer science and engineering.

Publication Top Noted

1. Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets

Authors: N. Navin, F.A. Farid, R.Z. Rakin, S.S. Tanzim, M. Rahman, S. Rahman, J. Uddin, ...
Journal: Journal of Imaging, Vol. 11, Issue 5, Article 134
Year: 2025
Citation: 1 (as of now)
Summary:
This study introduces a YOLOv11-based deep learning model designed to recognize both Bangla and English sign language alphabets in real-time. The model was trained on a custom bilingual sign dataset and achieved high accuracy and low latency. The contribution is notable in promoting inclusivity for hearing-impaired communities in multilingual regions like Bangladesh.

2. Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11

Authors: R.Z. Rakin, M. Rahman, K.F. Borsa, F.A. Farid, S. Rahman, J. Uddin, H.A. Karim
Journal: Future Internet, Vol. 17, Issue 5, Article 187
Year: 2025
Summary:
This paper proposes an AI model using YOLOv11 to identify infrastructure faults (e.g., road cracks, bridge damage) through image data. Designed with smart city integration in mind, the model is tested in urban environments and demonstrates high efficiency.

3. A Hybrid CNN Framework DLI-Net for Acne Detection with XAI

Authors: S. Sharmin, F.A. Farid, M. Jihad, S. Rahman, J. Uddin, R.K. Rafi, R. Hossan, ...
Journal: Journal of Imaging, Vol. 11, Issue 4, Article 115
Year: 2025
Summary:
This paper presents DLI-Net, a hybrid CNN framework for classifying and explaining acne severity. It incorporates Explainable AI (XAI) techniques to enhance trust and transparency in medical AI systems.

4. A Deep Q-Learning Based UAV Detouring Algorithm in a Constrained Wireless Sensor Network Environment

Authors: S. Rahman, S. Akter, S. Yoon
Journal: Electronics, Vol. 14, Issue 1, Article 1
Year: 2024
Citation: 2 (as of now)
Summary:
This study explores a reinforcement learning-based approach using Deep Q-Learning for UAV navigation in constrained wireless sensor networks. The algorithm optimizes path planning in real-time, even in environments with signal interference or node failures.

5. A Deep Learning Model for YOLOv9-based Human Abnormal Activity Detection: Violence and Non-Violence Classification

Authors: S. Salehin, S. Rahman, M. Nur, A. Asif, M. Bin Harun, J. Uddin
Journal: Iranian Journal of Electrical & Electronic Engineering, Vol. 20, Issue 4
Year: 2024
Citation: 2 (as of now)
Summary:
This paper proposes a YOLOv9-based model to detect abnormal human activity, particularly violent behavior, in real-time video surveillance. The system is trained on public datasets and achieves high detection accuracy.

Conclusion

Ms. Shakila Rahman is a promising and emerging researcher, with an impressive blend of academic excellence, funded research, and contributions to cutting-edge domains like machine learning and UAV networks. Her commitment to mentoring students and publishing research makes her a very strong candidate for the Best Researcher Award, particularly among early-career researchers or those in developing countries.

Gan Xu – Artificial Intelligence – Best Researcher Award

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Gan Xu – Artificial Intelligence – Best Researcher Award

Mr. Gan Xu distinguished academic and researcher in the field Artificial Intelligence.

🌐 Professional Profile

Educations📚📚📚

He is currently pursuing a Ph.D. in Finance at the Capital University of Economics and Business in Beijing, China, since September 2021. Prior to this, he completed his Master’s in Finance from Beijing Union University, Beijing, China, graduating in June 2021. His academic journey began with a Bachelor’s degree in Biotechnology, which he obtained from Guilin Medical University, Guilin, Guangxi, China, in June 2010.

Research Experience

He participated in the Project of the National Social Science Foundation of China, focusing on the “Research on Level Measurement, Spatial and Temporal Divergence, and Improvement Path of Rural Financial Services for Rural Revitalization” (19BJY158), where he was mainly responsible for the research design of some sub-topics and participated in enterprise research. Additionally, he contributed to the Key Topic of the China Mobile Communication Federation on the “Research on the Application of Blockchain Technology in Finance” (CMCA2018ZD01), taking charge of the research design of certain sub-topics and writing research reports. Furthermore, he was involved in the research project on “Financial Support for Deepening Financial Services for Private and Micro and Small Enterprises” as part of the Comprehensive Reform Pilot City Project in Jincheng City, Shanxi Province, where he was responsible for independently participating in application writing.

Social Experience

He has co-authored several significant publications, including “Financial Density of Village Banks and Income Growth of Rural Residents” with Yang, G.Z., Xu, G., Zhang, Y., and others, published in Economic Issues in 2021. Additionally, he contributed to “Knowledge Mapping Analysis of Seven Decades of Rural Finance Research in China” with Zhang, F., Xu, G., Zhang, X.Y., and Cheng, X., which appeared in Rural Finance Research in 2020. He also co-authored “A Review of Blockchain Applications in the Financial Sector” with Zhang, F. and Cheng, X., published in Technology for Development in 2019.

Honors

  • Received Beijing Outstanding Graduates in 2020
  • Outstanding graduate of Beijing Union University in 2020
  • First Prize of Excellent Paper in the First Annual Meeting of the Financial Technology Professional Committee of the China Society for Technology Economics, 2019
  • Second Prize of Excellent Paper in the 13th China Rural Finance Development Forum, 2019
  • Second Prize of Excellent Paper of the 9th Annual Conference of China Regional Finance and Xiongnu Financial Technology Forum, 2019

📝🔬Publications📝🔬

Micheal Olaolu Arowolo – Artificial intelligence – Excellence in Innovation

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Micheal Olaolu Arowolo – Artificial intelligence – Excellence in Innovation

Dr. Micheal Olaolu Arowolo  distinguished academic and researcher in the field Artificial Intelligence. He holds several academic and professional memberships. In March 2021, he became a member of the Institute of Electrical and Electronics Engineers (IEEE), with membership number 96234988. He joined the Asia Pacific Institute of Science and Engineering (APISE) in September 2019, holding membership number M20190918110. In May 2019, he became a member of both the International Society for Computational Biology (ISCB) and the Nigerian Bioinformatics and Genomics Network (NBGN), with membership number NBGNI380. He also joined the Society of Digital Information and Wireless Communications (SDIWC) in March 2017 and the European Alliance for Innovation (EAI) in February 2017. Additionally, he has been a member of the International Association of Engineers (IAENG) since September 2015, with membership number 158851. His professional certifications include being an Oracle Database SQL Certified Expert from Oracle University, achieved in March 2014. Moreover, he is indexed on Scopus (57214819505), ORCID (0000-0002-9418-5346), and Web of Science Researcher (ABD-4157-202), all obtained in 2019.

 

🌐 Professional Profile

Educations📚📚📚

He attended several academic institutions, beginning with ECWA L.G.E.A Primary School ‘B’ in Ilorin, Kwara State, where he obtained his First School Leaving Certificate (FSLC) from 1991 to 1998. He then moved on to Modelak Science College in Ilorin, completing his Senior School Certificate Examination (SSCE) between 1998 and 2004. For his undergraduate studies, he attended Al-Hikmah University in Ilorin, Kwara State, earning a Bachelor of Science (B.Sc.) degree in Computer Science with Second Class Honors (Lower Division) from 2008 to 2012. Continuing his education, he obtained a Master of Science (M.Sc.) degree in Computer Science from Kwara State University in Malete, Kwara State, between 2014 and 2017. Finally, he completed his academic journey at Landmark University in Omu-aran, Kwara State, where he earned a Doctor of Philosophy (Ph.D.) in Computer Science from 2018 to 2021.

Work Experience:

He has held various academic and professional positions throughout his career. Since 2022, he has been serving as a Research Scholar, Instructor, and Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Missouri, Columbia, specifically at the Christopher S. Bond Life Sciences Center. In 2021, he was a Lecturer II in the Department of Computer Science at Landmark University, Omu-Aran, Kwara State, Nigeria, and prior to that, from 2020 to 2021, he worked there as an Assistant Lecturer. From 2018 to 2020, he was a Graduate Lecturer in the Department of Computer Science at the Institute of Professional Studies, Kwara State University, Malete. In 2019, he served as an Ad-Hoc Staff for the Independent National Electoral Commission (INEC) in Nigeria, working as an Oke-Ode Ad-Hoc Registration Area Technician for the Kwara State Election. His earlier roles include being an IT Consultant at Dalayak IT Consults from 2016 to 2017, a Computer Analyst at Baylings Enterprises from 2013 to 2015, and a Computer Analyst for the Ogun-Oshun River Basin Development Authority during his National Youth Service Corps (NYSC) from November 2012 to October 2013.

Academic and Administrative Positions Held

He has served in various academic and administrative roles, including being the Academic Level Adviser for Computer Science 400L students and the Examination Officer for the Computer Science department at Landmark University from 2021 to 2022. Additionally, he was a member of the University Ranking Committee at Landmark University in 2022. He contributed to the university community by being a member of the Landmark University Sustainable Development Goal 9 group focused on industry, innovation, and infrastructure. He also served on the Local Organizing Committee (LOC) for the 2nd Nigerian Bioinformatics and Genomics Network (#NBGN21) Conference in 2021. Furthermore, he acted as the Social Director of the Al-Hikmah University Alumni Association and was an instructor for H3ABioNet’s Introduction to Bioinformatics course (IBT_2021).

His personal qualities include good logical skills, a strong personality, excellent communication abilities, keen observation, quick learning, multitasking, and proficiency in computing. Throughout his career, he has supervised over 40 undergraduate students (B.Sc.) on their projects, theses, and dissertations.

📝🔬Publications📝🔬