DrKirill Veselkov
Associate Professor in Cancer Informatics
Department of Surgery & Cancer - Faculty of Medicine
Orcid identifier0000-0002-1164-0359 (opens in a new tab)
- Associate Professor in Cancer InformaticsDepartment of Surgery & Cancer - Faculty of Medicine
- 020 7594 3899 (Work)
- Sir Alexander Fleming Building, South Kensington Campus, United Kingdom
BIO
Dr Veselkov is Associate Professor of Cancer Informatics and Computational Medicine in the Department of Surgery and Cancer at Imperial College London. His research group pioneers the application of advanced computational and artificial intelligence (AI) technologies to drive innovation in personalised medicine, precision nutrition, digital health and population-scale disease management.
Expertise within the group centres on developing and applying state-of-the-art machine learning, deep learning, graph neural networks, causality-inspired AI, and foundational models to extract clinically actionable insights from complex, heterogeneous datasets, including multi-omics, biomedical imaging, and health data.
The team leads AI innovation in large-scale initiatives such as the EU-wide AIDA project, with a focus on AI-driven diagnostic assistants for gastric inflammation. In partnership with the Vodafone Foundation, they also direct the DreamLab project which harnesses the idle processing power of tens of thousands of smartphones, network-based AI and large -omics data to hunt for drug-food combinations against cancer genomes and emerging COVID-19 disease.
Dr Veselkov’s research and leadership have received international recognition, including the World Economic Forum Young Scientist Award, and have been featured by major media outlets such as BBC Click and Sky Swipe.
1. Higgins K, Nyssen OP, Southern J, Laponogov I; AIDA CONSORTIUM; Veselkov D, Gisbert JP, Kanonnikoff TF, Veselkov K. The Helicobacter pylori AI-clinician harnesses artificial intelligence to personalise H. pylori treatment recommendations. Nature Communications. 2025 Jul 14;16(1):6472. https://doi.org/10.1038/s41467-025-61329-5
2. Gonzalez G, Lin X, Herath I, Veselkov K, Bronstein M, Zitnik M. Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks. Nature Biomedical Engineering. 2025; 1-18. https://doi.org/10.1038/s41551-025-01481-x
3. Rita L, Southern J, Laponogov I, Higgins K, Veselkov K. Optimizing ingredient substitution using large language models to enhance phytochemical content in recipes. Machine Learning and Knowledge Extraction. 2024;6:2738-2752. https://doi.org/10.3390/make6040131
4. Kerdegari H, Higgins K, Veselkov D, Laponogov I, Polaka I, Coimbra M, Pescino JA, Leja M, Dinis-Ribeiro M, Fleitas Kanonnikoff T, Veselkov K. Foundational models for pathology and endoscopy images: application for gastric inflammation. Diagnostics. 2024;14:1912. https://doi.org/10.3390/diagnostics14171912
5. Wei W, Southern J, Zhu K, Cordeiro F, Veselkov K. Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography. Scientific Reports. 2023;13:8296. https://doi.org/10.1038/s41598-023-35414-y
6. Rita L, Neumann NR, Laponogov I, et al. Veselkov K Alzheimer’s disease: using gene/protein network machine learning for molecule discovery in olive oil. Human Genomics. 2023;17:57. https://doi.org/10.1186/s40246-023-00503-6
7. Laponogov I, Gonzalez G, Shepherd M, Qureshi A, Veselkov D, Charkoftaki G, Vasiliou V, Youssef J, Mirnezami R, Bronstein M, Veselkov K. Network machine learning maps phytochemically rich “Hyperfoods” to fight COVID-19. Human Genomics. 2021;15:1. https://doi.org/10.1186/s40246-020-00297-x
8. Aksenov AA, Laponogov I, et al, Dorrestein PC, Veselkov K. Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data. Nature Biotechnology. 2021; 39(2):169-173. https://doi.org/10.1038/s41587-020-0700-3.
9. Veselkov K, Gonzalez G, Aljifri S, Galea D, Mirnezami R, Youssef J, Bronstein M, Laponogov I. HyperFoods: machine intelligent mapping of cancer-beating molecules in foods. Scientific Reports. Journal Top 100 Collection. 2019;9:9237. https://doi.org/10.1038/s41598-019-45349-y
10. Veselkov KA, Mirnezami R, Strittmatter N, Goldin RD, Kinross J, Speller AVM, Abramov T, Jones EA, Darzi A, Holmes E, Nicholson JK, Takats Z. Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer. PNAS. 2014;111(3):1216-1221. https://doi.org/10.1073/pnas.1310524111
Expertise within the group centres on developing and applying state-of-the-art machine learning, deep learning, graph neural networks, causality-inspired AI, and foundational models to extract clinically actionable insights from complex, heterogeneous datasets, including multi-omics, biomedical imaging, and health data.
The team leads AI innovation in large-scale initiatives such as the EU-wide AIDA project, with a focus on AI-driven diagnostic assistants for gastric inflammation. In partnership with the Vodafone Foundation, they also direct the DreamLab project which harnesses the idle processing power of tens of thousands of smartphones, network-based AI and large -omics data to hunt for drug-food combinations against cancer genomes and emerging COVID-19 disease.
Dr Veselkov’s research and leadership have received international recognition, including the World Economic Forum Young Scientist Award, and have been featured by major media outlets such as BBC Click and Sky Swipe.
1. Higgins K, Nyssen OP, Southern J, Laponogov I; AIDA CONSORTIUM; Veselkov D, Gisbert JP, Kanonnikoff TF, Veselkov K. The Helicobacter pylori AI-clinician harnesses artificial intelligence to personalise H. pylori treatment recommendations. Nature Communications. 2025 Jul 14;16(1):6472. https://doi.org/10.1038/s41467-025-61329-5
2. Gonzalez G, Lin X, Herath I, Veselkov K, Bronstein M, Zitnik M. Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks. Nature Biomedical Engineering. 2025; 1-18. https://doi.org/10.1038/s41551-025-01481-x
3. Rita L, Southern J, Laponogov I, Higgins K, Veselkov K. Optimizing ingredient substitution using large language models to enhance phytochemical content in recipes. Machine Learning and Knowledge Extraction. 2024;6:2738-2752. https://doi.org/10.3390/make6040131
4. Kerdegari H, Higgins K, Veselkov D, Laponogov I, Polaka I, Coimbra M, Pescino JA, Leja M, Dinis-Ribeiro M, Fleitas Kanonnikoff T, Veselkov K. Foundational models for pathology and endoscopy images: application for gastric inflammation. Diagnostics. 2024;14:1912. https://doi.org/10.3390/diagnostics14171912
5. Wei W, Southern J, Zhu K, Cordeiro F, Veselkov K. Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography. Scientific Reports. 2023;13:8296. https://doi.org/10.1038/s41598-023-35414-y
6. Rita L, Neumann NR, Laponogov I, et al. Veselkov K Alzheimer’s disease: using gene/protein network machine learning for molecule discovery in olive oil. Human Genomics. 2023;17:57. https://doi.org/10.1186/s40246-023-00503-6
7. Laponogov I, Gonzalez G, Shepherd M, Qureshi A, Veselkov D, Charkoftaki G, Vasiliou V, Youssef J, Mirnezami R, Bronstein M, Veselkov K. Network machine learning maps phytochemically rich “Hyperfoods” to fight COVID-19. Human Genomics. 2021;15:1. https://doi.org/10.1186/s40246-020-00297-x
8. Aksenov AA, Laponogov I, et al, Dorrestein PC, Veselkov K. Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data. Nature Biotechnology. 2021; 39(2):169-173. https://doi.org/10.1038/s41587-020-0700-3.
9. Veselkov K, Gonzalez G, Aljifri S, Galea D, Mirnezami R, Youssef J, Bronstein M, Laponogov I. HyperFoods: machine intelligent mapping of cancer-beating molecules in foods. Scientific Reports. Journal Top 100 Collection. 2019;9:9237. https://doi.org/10.1038/s41598-019-45349-y
10. Veselkov KA, Mirnezami R, Strittmatter N, Goldin RD, Kinross J, Speller AVM, Abramov T, Jones EA, Darzi A, Holmes E, Nicholson JK, Takats Z. Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer. PNAS. 2014;111(3):1216-1221. https://doi.org/10.1073/pnas.1310524111
FACULTY
- Faculty of Medicine
POSITION NAME
- Associate Professor in Cancer Informatics