Die vielen Gesichter von GESIS
Vita
Taimoor Khan is a postdoctoral researcher in the KTS department at GESIS. He is part of the Big Data Analytics team and contributes to the services related to infrastructure and analytics of big data. His research interests include data analytics, natural language processing and machine learning. He received his PhD in Computer Science from Bahria University Islamabad in 2018 with the thesis titled "Lifelong machine learning topic modeling for large-scale aspect extraction". During his PhD, he worked with continuous machine learning models for sequential task processing that maintain a common knowledge base to avoid relearning the repetitive patterns in different tasks and improve them.Service
Methods HubForschung
Natural language processingmachine learning
Veröffentlichungen
Zeitschriftenaufsatz
Wajid, Usman, Muhammad Hamza, M. Taimoor Khan, and Nouman Azam. 2024. "A Three-way Decision Approach for Dynamically Expandable Networks." International Journal of Approximate Reasoning 166 (March 2024): 109105. doi: https://doi.org/10.1016/j.ijar.2023.109105.
Khan, M. Taimoor, Nouman Azam, Shehzad Khalid, and Furqan Aziz. 2022. "Hierarchical lifelong topic modeling using rules extracted from network communities." PLoS one: Public Library of Science 3 (17): 1-22. doi: https://doi.org/10.1371/journal.pone.0264481.
Beitrag im Sammelwerk
Gangopadhyay, Susmita, M. Taimoor Khan, and Hajira Jabeen. 2024 (Forthcoming). "Linguistic_Hygenist at PAN 2024 TextDetox: HybridDetox - A Combination of Supervised and Unsupervised Methods for Effective Multilingual Text Detoxification." https://ceur-ws.org/Vol-3740/paper-236.pdf.
Saadi, Khouloud, and M. Taimoor Khan. 2022. "Effective Prevention of Semantic Drift in Continual Deep Learning." In Intelligent Data Engineering and Automated Learning – IDEAL 2022: 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, edited by Hujun Yin, David Camacho, and Peter Tino, Lecture Notes in Computer Science 13756, 456-464. Cham: Springer. doi: https://doi.org/10.1007/978-3-031-21753-1_44. https://link.springer.com/chapter/10.1007/978-3-031-21753-1_44.