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Iqra Muhammad

Iqra Muhammad

Assistant Professor
  • Department of Computer Science
  • Iqra.Muhammad@nu.edu.pk
  • (041) 111-128-128
  • Ext: 361

Introduction

Dr. Iqra Muhammad obtained her Ph.D. degree from the University of Liverpool, UK in January 2023. Her research interests encompass Knowledge graphs and Clinical data analysis. The prime focus of her research was the generation, maintenance and utilisation of knowledge graphs in the context of curated document databases. In general, she is interested in other related problems that fall under the domain of Machine learning.

During her doctoral studies,Iqra was part of the Data Mining and Machine Learning Research Group at the University of Liverpool. She has also worked as a Summer intern for Google Summer of Code from May 2018 to September 2018. During her 4-months internship at Sugar Labs for Google Summer of Code, she developed a novel application called Primero1. She has also volunteered as an instructor for Code First Girls during her time in UK.

Education

Ph.D. (Computer Science), 2022
University of Liverpool (Liverpool, UK)

M.S. (Software Engineering), 2018
NUST Islamabad (Islamabad, Pakistan)

B.S. (Software Engineering), 2014
Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (Topi, Swabi Pakistan)

Publications

Muhammad, I., Coenen, F., Gamble, C., Kearney, A. and Williamson, P. (2020). Knowledge graph construction using open information extraction. Proc. the 2nd International Workshop on Machine Learning and Knowledge Graphs (MLKG2020). }

Muhammad, I., Coenen, F., Gamble, C., Kearney, A. and Williamson, P. (2020). Maintaining Curated Document Databases Using a Learning to Rank Model: The ORRCA Experience. Accepted for publication AI-2020, the 40th Annual International Conference of the British Computer Society’s Specialist Group on Artificial Intelligence (BCS – SGAI)}

  Muhammad, I., Bollegala, D., Coenen, F., Gamble, C., Kearney, A. and Williamson, P. (2021). Document Ranking for Curated Document Databases using BERT and Knowledge Graph Embeddings: Introducing GRAB-Rank. In: Golfarelli M., Wrembel R., Kotsis G., Tjoa A.M. and Khalil I. (Eds), Big Data Analytics and Knowledge Discovery, Proc. DaWaK 2021, LNCS 12925, Springer, pp116-127.

  Muhammad, I., Frans Coenen, Carol Gamble, Anna Kearney, and Paula Williamson. “Query Resolution of Literature Knowledge Graphs Using Hybrid Document Embeddings.” In Artificial Intelligence XXXIX: 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Cambridge, UK, December 13–15, 2022, Proceedings, pp. 98-111. Cham: Springer International Publishing, 2022

Collaborations at National and International Level

Detail of Funded Projects