Jabir Alshehabi Al-Ani

Jabir Alshehabi Al-Ani serves as a Lecturer in the Data Science department at York St John University's York Business School. With over 10 years of expertise in Machine Learning, AI, NLP, Data Retrieval, and Information Extraction across academic, research, and industry settings, he contributes significantly to the university's technical education programs.

Dr. Alshehabi Al-Ani earned his PhD in computer science from the Department of Computer Science and Electronic Engineering at the University of Essex, UK. His academic foundation supports his current teaching responsibilities which include delivering lectures in Machine Learning and Practice in Interdisciplinary Problem Solving (PIIPS), building on seven years of prior teaching experience across numerous technical subjects.

His research interests focus on Natural Language Processing, Image Processing, and interdisciplinary applications of AI in healthcare and sociology studies. This dual focus on technical AI development and social applications demonstrates his commitment to creating technology with real-world impact. His work bridges computer science with social sciences, particularly in analyzing gender bias in employment contexts and developing classification systems for diverse applications.

Analysis of his publication record from 2017-2022 reveals a clear research trajectory evolving from technical computer vision and NLP methods toward more socially conscious applications of AI. His recent work emphasizes gender bias detection in job advertisements and interdisciplinary research management, reflecting growing interest in AI ethics and the societal implications of algorithmic systems. The publications span multiple venues including Frontiers Big Data, SocArXiv, and IEEE conferences, demonstrating both academic rigor and practical relevance.

As a postgraduate research supervisor, Dr. Alshehabi Al-Ani contributes to the development of emerging scholars in data science and AI fields. His teaching spans foundational programming and database concepts to advanced machine learning and natural language processing topics, creating a comprehensive educational pathway for students.

His research activities demonstrate strong interdisciplinary collaboration, particularly evident in his work on gender bias in job advertisements which involved researchers from multiple institutions across various disciplines. This collaborative approach extends to his contributions to parliamentary evidence submissions regarding diversity in STEM fields.