Dr Vinod Kumar Chauhan Kumar

Strathclyde Chancellor's Fellow

Computer and Information Sciences

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Personal statement

I am a Strathclyde Chancellor's Fellow in AI (Lecturer/Assistant Professor) in the Department of Computer and Information Sciences at the University of Strathclyde. I also hold a Visiting Scholar position and the MPLS Enterprise and Innovation Fellowship (2025–26) at the University of Oxford, UK. My primary research interests are in Causality, Healthcare, and Artificial Intelligence, with a long-term vision of realising personalised treatments through data-driven Causal AI. Before joining Strathclyde, I was a Postdoctoral Researcher at the Department of Engineering Science, University of Oxford, and the Department of Engineering, University of Cambridge, where I worked for over six years. During this time, I had the opportunity to collaborate with world-renowned clinicians and industry leaders, including Boeing and Rolls-Royce. At Cambridge, I was honoured to receive the Institute for Manufacturing Postdoctoral Award for Research Excellence in recognition of the practical impact of my work. I completed my PhD in optimisation for large-scale machine learning at Panjab University, Chandigarh, supported by the University Grants Commission’s JRF and SRF fellowships. I actively contribute to the research community and have reviewed over 200 articles for more than 50 international conferences and journals. I currently serve as an Associate Editor for PLOS Digital Health and as an Editorial Board Member for the International Journal of Artificial Intelligence in Healthcare. I welcome enquiries from motivated MSc (Research) and PhD students interested in causal AI, machine learning, and healthcare applications. I am also open to collaborations with academic, clinical, and industry partners who are committed to developing impactful solutions for real-world healthcare challenges.

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Publications

Sample selection bias in machine learning for healthcare
Chauhan Vinod Kumar, Clifton Lei, Salaün Achille, Lu Huiqi Yvonne, Branson Kim, Schwab Patrick, Nigam Gaurav, Clifton David A
ACM Transactions on Computing for Healthcare Vol 6, pp. 1-24 (2025)
https://doi.org/10.1145/3761822
A brief review of hypernetworks in deep learning
Chauhan Vinod Kumar, Zhou Jiandong, Lu Ping, Molaei Soheila, Clifton David A
Artificial Intelligence Review Vol 57 (2024)
https://doi.org/10.1007/s10462-024-10862-8
Individualised treatment effects estimation with composite treatments and composite outcomes
Chauhan Vinod Kumar, Clifton Lei, Nigam Gaurav, Clifton David A
2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2025)
https://doi.org/10.1109/EMBC58623.2025.11252651
A context-aware methodology for fault detection and severity classification under variable operating conditions
de Fabritiis F, Gryllias K, Chauhan V K, Clifton D A
Proceedings of the 31th International Congress on Sound and Vibration, ICSV 2025 31th International Congress on Sound and Vibration, ICSV 2025 Proceedings of the International Congress on Sound and Vibration (2025)
HCR-Net : a deep learning based script independent handwritten character recognition network
Chauhan Vinod Kumar, Singh Sukhdeep, Sharma Anuj
Multimedia Tools and Applications Vol 83, pp. 78433-78467 (2024)
https://doi.org/10.1007/s11042-024-18655-5
Temporal dynamics unleashed : elevating variational graph attention
Molaei Soheila, Niknam Ghazaleh, Ghosheh Ghadeer O, Chauhan Vinod Kumar, Zare Hadi, Zhu Tingting, Pan Shirui, Clifton David A
Knowledge-Based Systems Vol 299 (2024)
https://doi.org/10.1016/j.knosys.2024.112110

More publications

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Research Interests

My research focuses on Causal AI, situated at the intersection of causality, healthcare, and artificial intelligence. I draw on a blend of theory and practice to develop robust methodologies that address real-world healthcare challenges, with a particular emphasis on enabling data-driven personalised treatments that are transparent and impactful. Through interdisciplinary collaborations with clinicians, industry partners, and healthcare providers, my work aims to translate advances in causality and AI into tangible improvements in patient outcomes and healthcare delivery.

Topics of interest include, but are not limited to:

  • Personalised treatments and individualised treatment effect estimation
  • Causal inference and discovery from observational data at scale
  • Counterfactual reasoning for fairness, explainability, and clinical decision support
  • Causal foundation models
  • Causal digital twins
  • Uncertainty quantification and conformal prediction
  • Multimodal, federated, and continual learning
  • Synthetic data generation, causal benchmarking, and evaluation
  • Optimisation methods for causal and AI models
  • Domain adaptation and out-of-distribution detection

Professional Service & Leadership

I actively contribute to advancing Causal AI and AI for Healthcare through research leadership, peer review, and editorial service.

  • Reviewed over 200 manuscripts for more than 50 international journals and conferences, including ICML and AI in Medicine
  • Associate Editor: PLOS Digital Health
  • Editorial Board Member: International Journal of Artificial Intelligence in Healthcare
  • Guest Editor: Special Issue on Causal AI: Integrating Causality and Machine Learning for Robust Intelligent Systems for the Frontiers in AI, Frontiers in Digital Health and Frontiers in Big Data journals, with full manuscript submissions by 27 February 2026.

Opportunities for Students and Collaborators

I welcome collaborations with academic, clinical (including NHS), and industry partners who share the goal of addressing real-world healthcare challenges with robust and trustworthy AI.

I also invite applications from motivated MSc and PhD students interested in causal AI, personalised treatments, and healthcare applications of machine learning.

A fully funded PhD studentship in Causal AI for Personalised Healthcare is currently available; interested candidates are encouraged to get in touch for an informal discussion.

Professional Memberships

  • Association for Computing Machinery (ACM)
  • IEEE & IEEE Engineering in Medicine and Biology Society (EMBS)
  • EMBS Technical Committee on Biomedical and Health Informatics
  • Computer Society of India (Lifetime Member)
  • Indian Society for Technical Education (Lifetime Member)

Professional Activities

Frontiers in Artificial Intelligence (Journal)
Guest editor
24/7/2025
International Conference on Learning Representations (Event)
Peer reviewer
2026
Area Chair for NeurIPS AI4Science (Event)
Editorial board member
6/12/2025
[CHAI Hub ECR Workshop]: Bridging Perspectives: Engaging with Causality Across Research Sectors
Participant
5/12/2025
Session Chair: Tuning for Patient Specific Care at the 47th EMBC 2025 (Event)
Peer reviewer
14/7/2025
PLOS Digital Health (Journal)
Editor
2025

More professional activities

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Contact

Dr Vinod Kumar Chauhan Kumar
Strathclyde Chancellor's Fellow
Computer and Information Sciences

Email: vinod.kumar@strath.ac.uk
Tel: Unlisted