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Dr Nitesh Kumar
- Available for postgraduate supervision
Teams and roles for Nitesh Kumar
Overview
Dr. Nitesh Kumar joined the School of Computer Science and Informatics in 2022. In his first two years, he worked as a research associate and was promoted to the lecturer position in 2024. His research in the area of Artificial Intelligence focuses on Probabilistic Logic Programming, Neurosymbolic Artificial Intelligence, Causal Reasoning, and Large Language Models.
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.
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 Presented at 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 Presented at 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 2021Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, Vol. 130. Proceedings of Machine Learning Research (PMLR) pp. 2125-2133.
2020
- Zuidberg Dos Martires, P., Kumar, N., Persson, A., Loutfi, A. and De Raedt, L. 2020. Symbolic learning and reasoning with noisy data for probabilistic anchoring. Frontiers in Robotics and AI 7, article number: 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., Kumar, N., Persson, A., Loutfi, A. and De Raedt, L. 2020. Symbolic learning and reasoning with noisy data for probabilistic anchoring. Frontiers in Robotics and AI 7, article number: 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.
- 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 Presented at 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 Presented at 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 2021Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, Vol. 130. Proceedings of Machine Learning Research (PMLR) pp. 2125-2133.
Teaching
CMT651 - Agile Software Development (Autumn Sem 2024/25, 2025/26)
CM6113 - Software Development Skills 1 (Autumn Sem 2025/26)
Biography
He completed his Ph.D. from the Department of Computer Science, KU Leuven, Belgium. He was associated with the Declarative Languages and Artificial Intelligence (DTAI) group and Leuven AI. His supervisors were Prof. Luc De Raedt and Prof. Ondřej Kuželka.
He did his undergraduate studies at the National Institute of Technology (NIT) Rourkela in Computer Science and Engineering. He finished his master's (M.S.R) in Electrical Engineering (Computer Technology) from the Indian Institute of Technology Delhi (IITD). For a few years, he worked as a software engineer for Samsung R&D Institute India - Delhi (SRID).
Contact Details
Research themes
Specialisms
- Artificial intelligence
- Natural language processing
- Knowledge representation and reasoning