To make data-driven personalised healthcare a reality.

Publications

A more up-to-date list of papers can be found on Google Scholar profile.

Causal AI for Personalised Treatments

  1. V. K. Chauhan, D. S. Dhami, B. Gao, X. Wang, L. Clifton and D. A. Clifton (2025) "Beyond Correlations: The Necessity and the Challenges of Causal AI", TechRxiv, DOI: 10.36227/techrxiv.175554759.96327720/v1, preprint.
  2. V. K. Chauhan, L. Clifton, G. Nigam and D. A. Clifton (2025) "Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes", The 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Bella Center, Copenhagen, Denmark, July 14-17, 2025, preprint, poster.
  3. V. K. Chauhan, J. Zhou, G. Ghosheh, S. Molaei, D. A. Clifton (2024) "Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation", The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), May 2-4, 2024, Valencia, Spain, paper, poster, code.
  4. V. K. Chauhan, J. Zhou, P. Lu, S. Molaei, D. A. Clifton (2024) "A Brief Review of Hypernetworks in Deep Learning", Artificial Intelligence Review, paper, preprint.
  5. O. H. I. Chou*, V. K. Chauhan*, C. T. Chung, L. Lu, T. T. L. Lee, Z. M. W. Ng, K. K. Wang, S. Lee, H. Liu, W. T. Wong, R. T. K. Pang, A. K., B. M.Y. Cheung, G. Tse, J. Zhou (2024) "Comparing the risks of new-onset gastric cancer or gastric diseases in type 2 diabetes mellitus patients exposed to SGLT2I, DPP4I or GLP1A: a population-based cohort study", Gastric Cancer, paper, preprint.
  6. O. H. I. Chou, V. K. Chauhan, C. T. Chung, L. Lu, T. T. L. Lee, Z. M. W. Ng, K. K. Wang, S. Lee, H. Liu, W. T. Wong, R. T. K. Pang, A. K., B. M.Y. Cheung, G. Tse, J. Zhou (2023). "1567P The effect of SGLT2i and DPP4i on new-onset gastric cancer and gastric diseases in type 2 diabetes mellitus: A population-based cohort study", Annals of Oncology, 34, S876, abstract.
  7. V. K. Chauhan, S. Molaei, MH. Tania, A. Thakur, T. Zhu and D. A. Clifton (2023) "Adversarial De-confounding in Individualised Treatment Effects Estimation", The 26th International Conference on Artificial Intelligence and Statistics (AISTATS), paper, poster, preprint, code, video.

AI in Healthcare - Postdoc in AI for Healthcare at the University of Oxford

  1. V. K. Chauhan, D. S. Dhami, B. Gao, X. Wang, L. Clifton and D. A. Clifton (2025) "Beyond Correlations: The Necessity and the Challenges of Causal AI", TechRxiv, DOI: 10.36227/techrxiv.175554759.96327720/v1. [ preprint ]
  2. V. K. Chauhan, L. Clifton, A. Salaun, Y. Lu, K. Branson, P. Schwab, G. Nigam and D. A. Clifton (2025) "Sample Selection Bias in Machine Learning for Healthcare", ACM Transactions on Computing for Healthcare. [ paper, code ]
  3. V. K. Chauhan, L. Clifton, G. Nigam and D. A. Clifton (2025) "Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes", The 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Bella Center, Copenhagen, Denmark, July 14-17, 2025.
  4. F. De Fabritiis, V. K. Chauhan, D. A. Clifton, K. Gryllias (2025) "A context-aware methodology for fault detection and severity classification under variable operating conditions", The 31st International Congress on Sound and Vibration (ICSV31) to be held in Incheon, 06-11 July 2025.
  5. V. K. Chauhan, J. Zhou, G. Ghosheh, S. Molaei, D. A. Clifton (2024) "Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation", The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), May 2-4, 2024, Valencia, Spain.
  6. V. K. Chauhan, A. Thakur, O. O'Donoghue, O. Rohanian, S. Molaei, and D. A. Clifton (2024) "Continuous Patient State Attention Model for Addressing Irregularity in Electronic Health Records", BMC Medical Informatics and Decision Making.
  7. V. K. Chauhan, J. Zhou, P. Lu, S. Molaei, D. A. Clifton (2024) "A Brief Review of Hypernetworks in Deep Learning", Artificial Intelligence Review.
  8. O. H. I. Chou*, V. K. Chauhan*, C. T. Chung, L. Lu, T. T. L. Lee, Z. M. W. Ng, K. K. Wang, S. Lee, H. Liu, W. T. Wong, R. T. K. Pang, A. K., B. M.Y. Cheung, G. Tse, J. Zhou (2024) "Comparing the risks of new-onset gastric cancer or gastric diseases in type 2 diabetes mellitus patients exposed to SGLT2I, DPP4I or GLP1A: a population-based cohort study", Gastric Cancer.
  9. S. Molaei, N. G. Bousejin, G. Ghosheh, A. Thakur, V. K. Chauhan, T. Zhu, D. Clifton (2024) "CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation using Graph Neural Networks", Journal of Healthcare Informatics Research.
  10. S. Molaei, G. Niknam, G. Ghosheh, V. K. Chauhan, H. Zare, T. Zhu, S. Pan, D. Clifton (2024) "Temporal Dynamics Unleashed: Elevating Variational Graph Attention", Knowledge-Based Systems.
  11. O. H. I. Chou, V. K. Chauhan, C. T. Chung, L. Lu, T. T. L. Lee, Z. M. W. Ng, K. K. Wang, S. Lee, H. Liu, W. T. Wong, R. T. K. Pang, A. K., B. M.Y. Cheung, G. Tse, J. Zhou (2023). "1567P The effect of SGLT2i and DPP4i on new-onset gastric cancer and gastric diseases in type 2 diabetes mellitus: A population-based cohort study", Annals of Oncology, 34, S876.
  12. V. K. Chauhan, S. Molaei, MH. Tania, A. Thakur, T. Zhu and D. A. Clifton (2023) "Adversarial De-confounding in Individualised Treatment Effects Estimation", The 26th International Conference on Artificial Intelligence and Statistics (AISTATS).
  13. O. Rohanian, H. Jauncey, M. Nouriborji, V. K. Chauhan, B. P. Gonçalves, C. Kartsonaki, ISARIC Clinical Characterisation Group, L. Merson, D. A. Clifton (2023) "Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints", The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks.
  14. A. Khodadadi, N. G. Bousejin, S. Molaei*, V. K. Chauhan, T. Zhu, D. A. Clifton (2023) "Improving Diagnostics with Deep Forest Applied to Electronic Health Records", Sensors.
  15. V. K. Chauhan, A. Thakur, O. O'Donoghue and D. A. Clifton (2022) "COPER: Continuous Patient State Perceiver", IEEE International Conference on Biomedical and Health Informatics.
  16. T. Ceritli, G. O. Ghosheh, V. K. Chauhan, T. Zhu, A. P. Creagh, D. A. Clifton (2023) "Synthesizing Mixed-type Electronic Health Records using Diffusion Models", arXiv, (not published).
  17. Benjamin O´Brien, Oliver Redfern, Jonathan Bedford, Stephen Gerry, V. K. Chauhan, Rachel Henning, Rui Providência, …, and Peter Watkinson (2025) "Clinical prediction of Atrial Fibrillation after Cardiac Surgery: the AFACS risk scores", (under review).

Aviation, Manufacturing & Supply Chains - Postdoc in Industrial ML at the University of Cambridge

  1. V. K. Chauhan, M. Bass, A. K. Parlikad and A. Brintrup (2024) "Trolley Optimisation for Loading Printed Circuit Board Components", Operation Research Forum.
  2. V. K. Chauhan, Stephen Mak, A. K. Parlikad, Muhannad Alomari, Linus Casassa, A. Brintrup (2023) "Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing", Computers & Industrial Engineering.
  3. V. K. Chauhan, Muhannad Alomari, James Arney, A. K. Parlikad, A. Brintrup (2023) "Multi-tier material consolidation in complex supply chains", Supply Chain Analytics.
  4. V. K. Chauhan, Anna Ledwoch, A. Brintrup, Manuel Herrera, Vaggelis Giannikas, Goran Stojkovic, D. Mcfarlane (2023) "A network science approach to identify disruptive elements of an airline", Data Science and Management.
  5. V. Giannikasa, A. Ledwoch, G. Stojkovic, P. Costas, A. Brintrup, A. A. S. Al-Ali, V. K. Chauhan, D. McFarlane (2022) "A data-driven method to assess the causes and impact of delay propagation in air transportation systems", Transportation Research Part C: Emerging Technologies.
  6. V. K. Chauhan, Supun Perera, A. Brintrup (2020) "The relationship between nested patterns and the ripple effect in complex supply networks", International Journal of Production Research.
  7. B. Lee, M. Alomari, V. K. Chauhan, M. Farsi; A. Brintrup (2023) "Predicting supply chain procurement bid prices with uncertainty quantification and machine learning: a case study in aerospace manufacturing", (under review).

Optimisation for Machine Learning - JRF and SRF (PhD) at Panjab University Chandigarh

  1. V. K. Chauhan, A. Sharma, K. Dahiya (2021) "LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms", Software Impacts, Elsevier.
  2. V. K. Chauhan, A. Sharma, K. Dahiya (2020) "Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning", Journal of Machine Learning and Cybernetics.
  3. V. K. Chauhan, A. Sharma, K. Dahiya (2019) "SAAGs: Biased Stochastic Variance Reduction Methods for Large-scale Learning", Applied Intelligence, Springer, Vol. 49, Issue 9, pp 3331–3361.
  4. V. K. Chauhan, K. Dahiya, A. Sharma (2019) "Problem Formulations and Solvers in Linear SVM: a Review", Artificial Intelligence Review, Springer, Vol. 52, Issue 2, pp 803–855.
  5. V. K. Chauhan, A. Sharma, K. Dahiya (2018) "Faster Learning by Reduction of Data Access Time", Applied Intelligence, Springer 48(12):4715--4729.
  6. V. K. Chauhan, K. Dahiya & A. Sharma, (2017) "Mini-batch Block-coordinate based Stochastic Average Adjusted Gradient Methods to Solve Big Data Problems", The Ninth Asian Conference on Machine Learning (ACML 2017) at Yonsei University, Seoul, Korea, in Proceedings of Machine Learning Research (PMLR) 77:49-64.
  7. V. K. Chauhan, K. Dahiya and A. Sharma, (2017) "Trust Region Levenberg-Marquardt Method for Linear SVM", IEEE Proceedings of the 9th International Conference on Advances in Pattern Recognition (ICAPR-2017), In celebration of the 125th Birth Anniversary of Professor P. C. Mahalanobis, December 27-30, 2017 at Indian Statistical Institute, Bangalore, India.
  8. K. Dahiya, V. K. Chauhan and A. Sharma, (2016) "Online Support Vector Machine Based on Minimum Euclidean Distance", In: Raman B., Kumar S., Roy P., Sen D. (eds) Proceedings of International Conference on Computer Vision and Image Processing (CVIP-2016). Advances in Intelligent Systems and Computing, vol 459 pp. 89-99. Springer, Singapore.
  9. V. K. Chauhan (2019) "Solving large scale linear support vector classification using an optimization framework based on stochastic approximation and coordinate descent approaches", Panjab University Chandigarh, PhD Thesis.

Handwriting Recognition Using Deep Learning - Collaborations

  1. V. K. Chauhan, S. Singh and A. Sharma (2024) "HCR-Net: A transfer learning based script independent handwriting character recognition network", Multimedia Tools and Applications.
  2. S. Singh, A. Sharma and V. K. Chauhan (2023) "Indic Script Family and Its Offline Handwriting Recognition for Characters/ Digits and Words: A Comprehensive Survey", Artificial Intelligence Review.
  3. S. Singh, V. K. Chauhan, Elisa H. Barney Smith (2020) "A Self Controlled RDP Approach for Feature Extraction in Online Handwriting Recognition using Deep Learning", Applied Intelligence.
  4. S. Singh, A. Sharma, V. K. Chauhan (2021) "Online handwritten Gurmukhi word recognition using fine-tuned Deep Convolutional Neural Network on offline features", Machine Learning with Applications.
  5. S. Singh, A. Sharma, V. K. Chauhan (2024) "GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks", arXiv:2403.15077.
    [ preprint ]

Books

  1. V. K. Chauhan (2021) "Stochastic Optimization for Large-scale Machine Learning", CRC Press (Taylor & Francis). ISBN 9781032131757, Book.
NOTE: The paper list was translated to html using LLMs so they may have mistakes and they have also lost links to code and related stuff. You may refer to these earlier papers at here.