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Jiazheng Li

I am a first-year PhD student at the University of Warwick in the Department of Computer Science, under the supervision of Prof. Yulan He and Dr Lin Gui. My research focuses on natural language processing (NLP) and pays particular interest in language model interpretability. I am also the 1st Year representative of the Computer Science PGR SSLC.

Prior to joining Warwick, I obtained my bachelor’s degree in computer science with First-Class Honours from the University College Dublin in 2021. I completed my final year project under the supervision of Prof. Pádraig Cunningham.

In 2020, I worked as a data scientist under the lead of senior data scientist expert, Dr Ying Wu at SAP Ireland. During my undergraduate study, I worked as a research assistant at the Science Foundation Ireland's Insight Centre for Data Analytics for two years. I conducted NLP research under the supervision of Prof. Barry Smyth and Dr Ruihai Dong, and signal processing research under the supervision of Dr Andrew Hines.


Academic Papers:

L. Yang, J. Li, R. Dong, Y. Zhang, B. Smyth. NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting. AAAI '22: Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

J. Geraghty, J. Li, A. Ragano, A. Hines. AQP: An Open Modular Python Platform for Objective Speech and Audio Quality Metrics. ACM MMsys '22.

L. Yang, J. Li, P. Cunningham, Y. Zhang, B. Smyth, R. Dong. Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment AnalysisLink opens in a new window. ACL-IJCNLP '21: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

J. Li, L. Yang, B. Smyth, R. Dong. MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk PredictionLink opens in a new window. CIKM ’20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020


Feature Selection Based on Unsupervised Learning (July 2020)
Guided Composite Relationship Discovery Framework (June 2020)