Dr Vinod Kumar Chauhan Kumar

Strathclyde Chancellor's Fellow

Computer and Information Sciences

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.

Expertise

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Prize And Awards

MPLS Enterprise and Innovation Fellow 2025-26, University of Oxford: Recipient; 2025
The Institute for Manufacturing Postdoctoral Award for Research Excellence: Recipient; 2021
Junior Research Fellow (non-stipendiary) - Lucy Cavendish College, University of Cambridge UK: Recipient; 2020
Student Travel Grant Award: Recipient; 11/2017
University Grants Commission - Senior Research Fellowship: Recipient; 2017
University Grants Commission - Junior Research Fellowship: Recipient; 2015

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Publications

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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 (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
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
Comparative effectiveness of sodium-glucose cotransporter-2 inhibitors for new-onset gastric cancer and gastric diseases in patients with type 2 diabetes mellitus : a population-based cohort study: Chou Oscar Hou In, Chauhan Vinod Kumar, Chung Cheuk To Skylar, Lu Lei, Lee Teddy Tai Loy, Ng Zita Man Wai, Wang Karin Kai Wing, Lee Sharen, Liu Haipeng, Pang Ronald Ting Kai, Kaewdech Apichat, Cheung Bernard Man Yung, Tse Gary, Zhou Jiandong; Gastric Cancer Vol 27, pp. 947-970 (2024); https://doi.org/10.1007/s10120-024-01512-7
CliqueFluxNet : unveiling EHR insights with stochastic edge fluxing and maximal clique utilisation using graph neural networks: Molaei Soheila, Bousejin Nima Ghanbari, Ghosheh Ghadeer O, Thakur Anshul, Chauhan Vinod Kumar, Zhu Tingting, Clifton David A; Journal of Healthcare Informatics Research Vol 8, pp. 555-575 (2024); https://doi.org/10.1007/s41666-024-00169-2

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Research

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Research Interests

My research focuses on Causal AI, at the intersection of artificial intelligence and causality, drawing on a blend of theory and practice to develop methodologies that address real-world healthcare challenges. The ultimate vision is to enable data-driven personalised treatments and trustworthy clinical decision support, making a sustained impact on personalised healthcare in the long run.

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, explanations, and clinical decision support
Uncertainty quantification and out-of-distribution generalisation
Deep and multimodal learning, federated and continual learning for healthcare
Causal benchmarking and evaluation
Applications in personalised healthcare and digital twins