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. 🚀

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.

🎓 Education & Experience

PhD in Information Technology (Language Technology) – 2019 🎓
Addis Ababa University, Ethiopia
- Thesis: Morphological Analysis Using Suffix-Sequences Based Machine Learning Approach
MSc in Information Science – 2009 🎓
Addis Ababa University, Ethiopia
- Thesis: Parts of Speech Tagging for Afaan Oromo

Assistant Professor (2018 – Present) 👨‍🏫
- Lecturing MSc courses: NLP, AI, Machine Learning, Python, Data Science
- Supervising postgraduate students
Lecturer (2009 – 2017) 🎓
- Teaching undergraduate courses: Java, C++, DBMS, Networking
- Supervising student projects
Graduate Assistant – Assistant Lecturer (2005 – 2008) 🎓
- Assisting and teaching IT-related courses
Chairperson, Department of IT (2006-2007, 2008-2011) 🏛️
Dean, Faculty of Computing and Informatics (2018-2020, 2022-Present) 🎖️

📈 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. 🚀📖

🔬 Research Focus

Dr. Getachew Mamo’s research primarily revolves around Natural Language Processing (NLP) 🤖, Artificial Intelligence (AI) 🧠, and Machine Learning (ML) 📊. 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. 🌍💡

🏆 Awards & Honors

🏅 Best Teaching Performance Award – College of Engineering and Technology, Jimma University (2010)

Publication Top Notes:

📌 Parts of speech tagging for Afaan Oromo – GM Wegari, M Meshesha | International Journal of Advanced Computer Science and Applications 1(3), 📑 Cited by: 12

📌 Suffix sequences based morphological segmentation for Afaan Oromo – GM Wegari, M Melucci, S Teferra | AFRICON 2015, 1-6 📑 Cited by: 2

📌 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 Engineering 📑 Cited by: 1

📌 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 Technology 📑 Cited by: N/A

📌 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 📑 Cited by: N/A

Xiaoling Shu | Large Language Models | Best Researcher Award

Ms. Xiaoling Shu, Northwest Normal University , China.

Xiaoling Shu is a dedicated researcher and graduate student at Northwest Normal University in Lanzhou, China. Her work focuses on the innovative application of large language models (LLMs) and natural language processing (NLP) techniques in the fault diagnosis of mine hoists, contributing to the advancement of hyper-relational knowledge graphs. Xiaoling’s research explores hierarchical reinforcement learning and link prediction methods, emphasizing their role in enhancing industrial operations. Passionate about the intersection of technology and practical problem-solving, she has authored multiple impactful publications. Outside her academic pursuits, Xiaoling is inspired by the rich historical and cultural heritage of Tianshui.

Education and Experience

Graduate Student in Progress (Computer Science and Engineering)
Northwest Normal University, Lanzhou, China (Since 1999-02)

Researcher in Mine Hoist Fault Analysis and Knowledge Graphs
Specializing in advanced NLP and hierarchical learning techniques.

Suitability For The Award

Ms. Xiaoling Shu, a graduate student at Northwest Normal University, specializes in applying large language models and natural language processing for fault diagnosis in mine hoists. Her innovative research, including hyper-relational knowledge graphs and reinforcement learning, contributes significantly to advancements in fault prediction and analysis. Ms. Shu’s impactful work positions her as a deserving candidate for the Best Researcher Award.

Professional Development

Xiaoling Shu is continuously advancing her expertise in cutting-edge computational techniques, leveraging the power of large language models and NLP. Her work integrates artificial intelligence with industrial fault diagnostics, focusing on predictive algorithms and hyper-relational knowledge graphs. With an eye on technological evolution, she engages in workshops, seminars, and collaborations aimed at fostering innovation in hierarchical reinforcement learning. Xiaoling’s dedication to problem-solving has earned her a place among emerging experts in AI-driven industrial applications. Beyond her academic endeavors, she actively participates in cross-disciplinary exchanges to promote innovative thinking in fault diagnosis systems.

Research Focus

Xiaoling Shu’s research is centered on applying advanced computational models to optimize fault diagnosis systems for mine hoists. Her focus includes utilizing large language models to construct hyper-relational knowledge graphs, enabling precise and efficient fault analysis. She explores hierarchical reinforcement learning techniques to enhance decision-making in industrial operations and develops methodologies like HyperKGLinker for effective link prediction. Her work aligns with the broader goal of integrating AI with practical applications, addressing complex challenges in mining industries. Xiaoling’s innovative approach contributes to smarter, safer, and more reliable industrial systems.

Awards and Honors

Best Research Contribution Award for advancements in NLP-based fault diagnostics.
Innovation in AI Award for hyper-relational knowledge graph applications.
Outstanding Researcher for publications on hierarchical reinforcement learning.
Certificate of Excellence for contributions to link prediction methods.
Technology Pioneer Award for integrating LLMs in industrial applications.

Publication Top Notes

“Utilizing Large Language Models for Hyper Knowledge Graph Construction in Mine Hoist Fault Analysis” – 2024, cited by 0,
“Research on Fault Diagnosis of Mine Hoists Based on Hierarchical Reinforcement Learning” – 2024, cited by 0.