![Nitesh Kumar](https://profiles-cardiff.imgix.net/attachments/4597?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Dr Nitesh Kumar
- Available for postgraduate supervision
Teams and roles for Nitesh Kumar
Overview
My research focuses on explainable and trustworthy artificial intelligence. I am interested in how AI systems, particularly large language models and agentic AI systems, can be made more reliable, interpretable, and useful in settings where reasoning, uncertainty, and explanations matter. My work draws on ideas from neurosymbolic AI, probabilistic logic programming, causal discovery, natural language processing, and representation learning.
Prospective MPhil/PhD Students
I welcome enquiries from prospective MPhil and PhD students interested in research projects on explainable and trustworthy AI.
When contacting me, please briefly describe your research interests, how they fit my work, and whether you already have funding or plan to apply for scholarships. Please also include evidence that you meet the University’s entry requirements, including English language requirements where applicable.
Applicants who are self-funded or who already have funding from other sources, such as government scholarships, employer sponsorship, or external studentships, are especially encouraged to get in touch. I am also happy to support strong applicants who wish to apply for competitive PhD scholarships, including the Cardiff University–China Scholarship Council scheme.
Publication
2025
- Kumar, N. , Chatterjee, U. and Schockaert, S. 2025. Extracting conceptual spaces from LLMs using prototype embeddings. Presented at: Findings of EMNLP Suzhou, China 4-9 November 2025. Published in: Christodoulopoulos, C. et al., Findings of the Association for Computational Linguistics: EMNLP 2025. Suzhou, China: Association for Computational Linguistics. , pp.9275-9298. (10.18653/v1/2025.findings-emnlp.493)
2024
- Kumar, N. , Chatterjee, U. and Schockaert, S. 2024. Ranking entities along conceptual space dimensions with LLMs: An analysis of fine-tuning strategies. Presented at: The 62nd Annual Meeting of the Association for Computational Linguistics Bangkok, Thailand 11-16 August 2024. Published in: Ku, L. , Martins, A. and Srikumar, V. eds. Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics. , pp.7974-7989. (10.18653/v1/2024.findings-acl.474)
2023
- Kumar, N. , Kuzelka, O. and De Raedt, L. 2023. First-order context-specific likelihood weighting in hybrid probabilistic logic programs. Journal of Artificial Intelligence Research 77 , pp.683-735. (10.1613/jair.1.13657)
- Kumar, N. and Schockaert, S. 2023. Solving hard analogy questions with relation embedding chains. Presented at: Conference on Empirical Methods in Natural Language Processing, EMNLP Singapore 6-10 December 2023. Published in: Bouamor, H. , Pino, J. and Bali, K. eds. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. , pp.6224–6236. (10.18653/v1/2023.emnlp-main.382)
2022
- Kumar, N. , Kuželka, O. and De Raedt, L. 2022. Learning distributional programs for relational autocompletion. Theory and Practice of Logic Programming 22 (1), pp.81-114. (10.1017/S1471068421000144)
2021
- Kumar, N. and Kuželka, O. 2021. Context-specific likelihood weighting. Presented at: The 24th International Conference on Artificial Intelligence and Statistics Virtual 13 -15 April 2021. Published in: Banerjee, A. and Fukumizu, K. eds. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021. Vol. 130.ML Research Press. , pp.2125-2133.
2020
- Zuidberg Dos Martires, P. et al., 2020. Symbolic learning and reasoning with noisy data for probabilistic anchoring. Frontiers in Robotics and AI 7 100. (10.3389/frobt.2020.00100)
Articles
- Kumar, N. , Kuzelka, O. and De Raedt, L. 2023. First-order context-specific likelihood weighting in hybrid probabilistic logic programs. Journal of Artificial Intelligence Research 77 , pp.683-735. (10.1613/jair.1.13657)
- Kumar, N. , Kuželka, O. and De Raedt, L. 2022. Learning distributional programs for relational autocompletion. Theory and Practice of Logic Programming 22 (1), pp.81-114. (10.1017/S1471068421000144)
- Zuidberg Dos Martires, P. et al., 2020. Symbolic learning and reasoning with noisy data for probabilistic anchoring. Frontiers in Robotics and AI 7 100. (10.3389/frobt.2020.00100)
Conferences
- Kumar, N. , Chatterjee, U. and Schockaert, S. 2025. Extracting conceptual spaces from LLMs using prototype embeddings. Presented at: Findings of EMNLP Suzhou, China 4-9 November 2025. Published in: Christodoulopoulos, C. et al., Findings of the Association for Computational Linguistics: EMNLP 2025. Suzhou, China: Association for Computational Linguistics. , pp.9275-9298. (10.18653/v1/2025.findings-emnlp.493)
- Kumar, N. , Chatterjee, U. and Schockaert, S. 2024. Ranking entities along conceptual space dimensions with LLMs: An analysis of fine-tuning strategies. Presented at: The 62nd Annual Meeting of the Association for Computational Linguistics Bangkok, Thailand 11-16 August 2024. Published in: Ku, L. , Martins, A. and Srikumar, V. eds. Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics. , pp.7974-7989. (10.18653/v1/2024.findings-acl.474)
- Kumar, N. and Schockaert, S. 2023. Solving hard analogy questions with relation embedding chains. Presented at: Conference on Empirical Methods in Natural Language Processing, EMNLP Singapore 6-10 December 2023. Published in: Bouamor, H. , Pino, J. and Bali, K. eds. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. , pp.6224–6236. (10.18653/v1/2023.emnlp-main.382)
- Kumar, N. and Kuželka, O. 2021. Context-specific likelihood weighting. Presented at: The 24th International Conference on Artificial Intelligence and Statistics Virtual 13 -15 April 2021. Published in: Banerjee, A. and Fukumizu, K. eds. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021. Vol. 130.ML Research Press. , pp.2125-2133.
Teaching
CMT651 - Agile Software Development (Autumn Sem 2024/25, 2025/26)
CM6113 - Software Development Skills 1 (Autumn Sem 2025/26)
Biography
I am a Lecturer in Artificial Intelligence in the School of Computer Science and Informatics at Cardiff University. I joined the School in 2022 as a Research Associate and became a Lecturer in 2024.
Before joining Cardiff, I completed my PhD in the Department of Computer Science at KU Leuven, Belgium, where I was associated with the Declarative Languages and Artificial Intelligence (DTAI) group and Leuven.AI. My supervisors were Prof. Luc De Raedt and Prof. Ondřej Kuželka.
I received my B.Tech. in Computer Science and Engineering from the National Institute of Technology (NIT) Rourkela, and my M.S.R. in Electrical Engineering (Computer Technology) from the Indian Institute of Technology Delhi (IIT Delhi). Before my PhD, I worked as a software engineer at Samsung R&D Institute India - Delhi (SRID).
Honours and awards
Fellowship, Higher Education Academy (2026)
Contact Details
Research themes
Specialisms
- Artificial intelligence
- Natural language processing
- Knowledge representation and reasoning