Assist. Prof. Dr. Getachew Wegari | Natural Language Processing (NLP) | Best Researcher Award

Assist. Prof. Dr. Getachew Wegari | Natural Language Processing (NLP) | Best Researcher Award

Assist. Prof. Dr. Getachew Wegari | Jimma University | Ethiopia

Dr. Getachew Mamo is an Assistant Professor of IT at Jimma University, specializing in Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI). With a PhD in Information Technology from Addis Ababa University, he has dedicated his career to advancing language technologies, particularly for the Afaan Oromo language. His expertise spans Python, Java, and C++, alongside deep learning frameworks such as PyTorch and OpenCV. Beyond academia, he has led key research projects and held administrative roles, including Dean of the Faculty of Computing and Informatics. His contributions to AI and NLP continue to impact Ethiopia’s tech landscape. πŸš€

Professional Profile:

Google Scholar

Suitability for Best Researcher Award

Dr. Getachew Mamo is a strong candidate for a Best Researcher Award due to his significant contributions to Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI), particularly in the context of Afaan Oromo language technologies. His work aligns with the award’s objective of recognizing impactful research that advances technological and scientific knowledge.

πŸŽ“ Education & Experience

  • PhD in Information Technology (Language Technology) – 2019 πŸŽ“
    Addis Ababa University, Ethiopia

    • Thesis: Morphological Analysis Using Suffix-Sequences Based Machine Learning Approach
  • MSc in Information Science – 2009 πŸŽ“
    Addis Ababa University, Ethiopia

    • Thesis: Parts of Speech Tagging for Afaan Oromo
  • Assistant Professor (2018 – Present) πŸ‘¨β€πŸ«

    • Lecturing MSc courses: NLP, AI, Machine Learning, Python, Data Science
    • Supervising postgraduate students
  • Lecturer (2009 – 2017) πŸŽ“

    • Teaching undergraduate courses: Java, C++, DBMS, Networking
    • Supervising student projects
  • Graduate Assistant – Assistant Lecturer (2005 – 2008) πŸŽ“

    • Assisting and teaching IT-related courses
  • Chairperson, Department of IT (2006-2007, 2008-2011) πŸ›οΈ

  • Dean, Faculty of Computing and Informatics (2018-2020, 2022-Present) πŸŽ–οΈ

πŸ“ˆ Professional Development

Dr. Getachew Mamo has made significant contributions to academia through research, teaching, and leadership. As an expert in NLP and AI, he has developed various machine learning models for language processing, including morphological analysis and speech recognition for Afaan Oromo. His extensive teaching experience covers key areas such as deep learning, artificial intelligence, and data science. He has held leadership roles, fostering academic growth and innovation at Jimma University. Additionally, he actively engages in research collaborations, publishing in international journals and conferences. His passion for technological advancements continues to drive Ethiopia’s progress in AI and IT. πŸš€πŸ“–

πŸ”¬ Research Focus

Dr. Getachew Mamo’s research primarily revolves around Natural Language Processing (NLP) πŸ€–, Artificial Intelligence (AI) 🧠, and Machine Learning (ML) πŸ“Š. His work has significantly contributed to Afaan Oromo language processing, including morphological analysis, parts of speech tagging, and speech-based command systems. He is also involved in big data analytics and deep learning models for medical applications, such as breast cancer diagnosis. His ongoing projects include an AI-based road safety management system and an Afaan Oromo spell checker. Through his research, he aims to enhance language technology, improve communication systems, and integrate AI into real-world applications. πŸŒπŸ’‘

πŸ† Awards & Honors

  • πŸ… Best Teaching Performance Award – College of Engineering and Technology, Jimma University (2010)

Publication Top Notes:

πŸ“Œ Parts of speech tagging for Afaan Oromo – GM Wegari, M Meshesha | International Journal of Advanced Computer Science and Applications 1(3),Β  πŸ“‘ Cited by: 12
πŸ“Œ Suffix sequences based morphological segmentation for Afaan Oromo – GM Wegari, M Melucci, S Teferra | AFRICON 2015, 1-6Β  πŸ“‘ Cited by: 2
πŸ“Œ Probabilistic and grouping methods for morphological root identification for Afaan Oromo – GM Wegari, M Melucci, S Teferra | 2016 6th International Conference-Cloud System and Big Data EngineeringΒ  πŸ“‘ Cited by: 1
πŸ“Œ The Integration of Deep Learning Techniques and Big Data Analytics for Improved Breast Cancer Diagnosis and Treatment: A Systematic Review – HM Gebre, GM Wegari | 2024 International Conference on Information and Communication TechnologyΒ  πŸ“‘ Cited by: N/A
πŸ“Œ An Assessment of Big Data Analysis Technologies for Improved Information Delivery – SR Chanthati, T Velmurugan, N Gulati, N Kedia, F Akram, GM Wegari | 2023 3rd International Conference on Smart Generation Computing Β πŸ“‘ Cited by: N/A

 

 

 

Mr. Hongzhen Cui | Natural Language Processing | Best Researcher Award

Mr. Hongzhen Cui | Natural Language Processing | Best Researcher Award

Mr. Hongzhen Cui | University of Science and Technology Beijing | China

πŸŽ“ Hongzhen Cui, an IEEE and CCF member, is a passionate researcher pursuing a Ph.D. in Computer Science and Technology at the University of Science and Technology Beijing (Expected 2025). His research 🌐 focuses on Natural Language Processing (NLP), Knowledge Graphs, Deep Learning, and interdisciplinary medical-engineering applications, including cardiovascular disease prediction ❀️. Hongzhen has industry experience as a System R&D Engineer at Meituan πŸ’» and academic experience as a Computer Science Lecturer at Zaozhuang University πŸ‘¨β€πŸ«. His diverse background blends technical innovation and academic excellence, driving advancements in AI and healthcare technologies πŸš€

Professional Profile:

SCOPUS

Suitability for Best Researcher Award

Hongzhen Cui is a highly suitable candidate for the Best Researcher Award due to his impactful research in Natural Language Processing, Knowledge Graphs, and Deep Learning, with significant applications in medical-engineering, particularly cardiovascular disease prediction. His blend of academic excellence, industry innovation at Meituan, and active involvement in IEEE and CCF showcases his commitment to advancing AI technologies for real-world healthcare solutions.

Education πŸŽ“

  • πŸ“š Ph.D. in Computer Science and Technology (Expected 2025)
    University of Science and Technology Beijing, China
  • πŸŽ“ Master of Engineering in Computer Science and Technology (2018)
    Harbin Engineering University, China
  • πŸŽ“ Bachelor of Engineering in Computer Science and Technology (2015)
    Zaozhuang University, China

Professional Experience πŸ’Ό

  • πŸ’» System R&D Engineer (2018–2019)
    Meituan, Beijing

    • Developed large-scale distributed systems 🌐
    • Collaborated with cross-functional teams 🀝
  • πŸ‘¨β€πŸ« Computer Science Lecturer (2019–2021)
    Zaozhuang University

    • Taught courses like Data Structures, Algorithms, and Networks πŸ“Š
    • Mentored students in academic and research projects πŸ“š

Professional DevelopmentΒ 

πŸš€ Hongzhen Cui has demonstrated continuous professional growth through diverse roles in academia and industry. At Meituan, he honed his skills in large-scale system development πŸ’», contributing to high-performance solutions. Transitioning to academia, he served as a Lecturer πŸ‘¨β€πŸ« at Zaozhuang University, inspiring students in subjects like Algorithms and Software Engineering πŸ“˜. His membership in IEEE and CCF 🌐 reflects his commitment to staying at the forefront of technological advancements. Currently pursuing his Ph.D., he actively engages in cutting-edge research in NLP, knowledge graphs, and AI-driven healthcare applications ❀️.

Research FocusΒ 

πŸ” Hongzhen Cui’s research spans multiple domains within computer science and medical technology. His primary focus is on Natural Language Processing (NLP) πŸ—£οΈ, enabling machines to understand human language. He explores Knowledge Graphs 🌐 to structure complex data relationships and applies Deep Learning πŸ€– for intelligent data analysis. In the medical field, he specializes in Cardiovascular Disease Feature Mining ❀️, Disease Information Extraction πŸ₯, and Disease Prediction and Analysis πŸ“Š. His interdisciplinary approach bridges technology and healthcare, aiming to improve diagnostic accuracy and predictive modeling in medical informatics.

Publication Top Notes:

πŸ“„ Multi-label text classification of cardiovascular drug attributes based on BERT and BiGRU β€” H. Cui, Hongzhen, L. Zhang, Longhao, X. Zhu, Xiaoyue, X. Guo, Xiuping, Y. Peng, Yunfeng Β 1 Citation πŸ“Š