Scholar
Abdulhady Abas Abdullah
Google Scholar ID: fxIiXlMAAAAJ
Researcher in Artificial Intelligence UKH Centre
Citations & Impact
Citations
125
H-index
8
i10-index
6
Publications
20
Co-authors
5
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Published over 20 research papers spanning deep learning, natural language processing, etc., Total Citations: 101, h-index: 6, i10-index: 4.
Research Experience
- 2024–Present: Research Assistant in Artificial Intelligence, AIIC, UKH, Erbil, Iraq, Conducting cutting-edge research in AI, specializing in speech processing, multilingual NLP, and Large Language Models (LLMs), Developed the first end-to-end Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) systems for Kurdish, Research focuses on LLM optimization, prompt engineering, and adapting transformer models for Kurdish and other under-resourced languages, Collaborates with interdisciplinary teams to publish findings; 2019–2022: Graduate Researcher (NLP/Speech Group), Soran University & AsoSoft Research Team, Key R&D contributions in Kurdish language technologies, including developed the first end-to-end ASR and TTS systems for Kurdish using deep learning, Created the first large-scale Kurdish speech corpus (170 hours) and text corpus (3M+ tokens), Built the first large language model for Kurdish (CKB), Developed OCR and handwriting recognition systems for Kurdish using modern deep learning architectures.
Education
- 2020–2022: M.Sc. in Artificial Intelligence, Soran University – Erbil, Iraq, Thesis: 'Kurdish Speech Recognition using Deep Learning'; 2016–2019: B.Sc. in Computer Science – Hardware, Soran University – Erbil, Iraq, Senior Project: 'Design and Implementation of a Real Estate Web and Mobile Application'.
Background
- Research Interests: Multilingual NLP & LLMs, Optimizing large language models (LLMs) for low-resource and multilingual applications, Deep Learning & Transformers - Efficient architectures, fine-tuning, and prompt engineering for NLP tasks, Speech & Multimodal AI - End-to-end ASR, Audio LLMs, and vision-language models (VLMs). Current research focuses on LLM optimization, prompt engineering, VLMs, Audio LLMs, and multimodal AI systems.
