The many faces of GESIS
Vita
Taimoor Khan is Senior researcher in KTS department at GESIS. He is part of the Big data analytics team and is contributing to the services related to the infrastructure and analysis of Big Data. His research interest include data analysis, natural language processing and machine learning. He did PhD in computer science from Bahria university Islamabad in 2018 with thesis title "Lifelong machine learning topic modeling for large-scale aspect extraction". During his PhD, he worked with continual machine learning models for sequential task processing, maintaining a common knowledge base to avoid relearning the repeating patterns in different tasks and improve on them.Publications
Publication
Journal article
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 (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.
Chapter in an edited book
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.