Professor Giovanni Montana

I am Professor of Data Science at the University of Warwick, jointly appointed to the Department of Statistics and Warwick Manufacturing Group (WMG). I develop machine learning for decision-making under uncertainty and its application to science and industry.

After earning my PhD in computational statistics from Warwick, I worked for two years as a postdoctoral researcher at the University of Chicago and one year as a data scientist at Bristol-Myers Squibb. I then joined the Mathematics Department at Imperial College London, progressing to Reader in Statistics (2005-2013), before moving to the Department of Biomedical Engineering at King's College London as Professor of Biostatistics and Bioinformatics (2013-2018). Throughout this period, I developed machine learning methods for medical imaging, neuroimaging, and genomics, addressing fundamental challenges in learning from high-dimensional data with complex structure. At Warwick, since 2018, I have broadened my focus toward reinforcement learning, addressing sequential decision-making in dynamic environments. My recent research has been supported by a UKRI Turing AI Acceleration Fellowship (2021–2025), alongside awards from EPSRC, MRC, CRUK, Wellcome, and Innovate UK, among others. This work enables applications in drug discovery, experimental design, and industrial automation.

My research outputs span methodological advances and applied collaborations (180+ publications), and I remain committed to education at all levels, teaching courses from introductory statistics to PhD-level machine learning. As a Chartered Statistician and Fellow of the Royal Statistical Society, I have also contributed to the research community through service on grant review panels for EPSRC, MRC, the Royal Society, and CRUK. Ultimately, I am driven by the goal of translating rigorous methodology into tangible impact.

Research interests and selected publications

A full listing can be found on my Google Scholar profile.

Recent ML methodology. Recent work focuses primarily on advancing reinforcement learning methodology across several core challenges including learning from limited or offline data, safe decision-making under constraints, incorporating structural knowledge (e.g. relational graphs and factorised actions) to improve sample efficiency and coordination, model-based planning with learned dynamics, and multi-agent systems. Much of this methodological development has been motivated by unlocking specific applications. Related work includes computer vision architectures for video understanding.

• Wang & Montana (2025) Retrosynthesis planning via worst-path policy optimisation in tree-structured MDPs. NeurIPS
• Zhu, Hepburn, Thorpe & Montana (2025) Uncertainty-based smooth policy regularisation for reinforcement learning with few demonstrations. NeurIPS
• Quan, Li & Montana (2025) Efficient verified unlearning for distillation. NeurIPS
• Neggatu, Houssineau & Montana (2025) Evaluation-time policy switching for offline reinforcement learning. AAMAS
• Utke, Houssineau & Montana (2025) Investigating relational state abstraction in collaborative MARL. AAAI
• Jin, Wei & Montana (2025) Achieving collective welfare in multi-agent RL via suggestion sharing. Machine Learning
• Wang, Jin & Montana (2025) Learning on one mode: Addressing multi-modality in offline RL. ICLR
• Zhu, Jin, Houssineau & Montana (2024) Mitigating relative over-generalization in multi-agent RL. Transactions on Machine Learning Research
• Beeson, Ireland & Montana (2024) An investigation of offline reinforcement learning in factorisable action spaces. Transactions on Machine Learning Research
• Hepburn, Jin & Montana (2024) State-constrained offline reinforcement learning. Transactions on Machine Learning Research
• Wang, Jin & Montana (2024) Goal-conditioned offline reinforcement learning through state space partitioning. Machine Learning
• Hepburn & Montana (2024) Model-based trajectory stitching for improved behavioural cloning and its applications. Machine Learning
• Beeson & Montana (2024) Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning. Machine Learning
• Ireland & Montana (2024) Revalued: Regularised ensemble value-decomposition for factorisable Markov decision processes. ICLR
• Utke, Houssineau & Montana (2024) Embracing relational reasoning in multi-agent actor-critic. AAMAS
• Wang, Yang, Chen, Sun, Fang & Montana (2023) GOPlan: Goal-conditioned offline RL by planning with learned models. Transactions on Machine Learning Research
• Gao, Yang, Han, Lu, Zheng & Montana (2023) Decoupling multimodal transformers for referring video object segmentation. IEEE Transactions on Circuits and Systems for Video Technology
• Gao, Han, Zheng, Yu & Montana (2023) Video object segmentation using point-based memory network. Pattern Recognition
• Pesce & Montana (2023) Learning multi-agent coordination through connectivity-driven communication. Machine Learning
• Ireland & Montana (2022) LeNSE: Learning to navigate subgraph embeddings for large-scale combinatorial optimisation. ICML
• Kilinc & Montana (2022) Reinforcement learning for robotic manipulation using simulated locomotion demonstrations. Machine Learning
• Charlesworth & Montana (2021) Solving challenging dexterous manipulation tasks with trajectory optimisation and reinforcement learning. ICML (Spotlight)
• Charlesworth & Montana (2020) PlanGAN: Model-based planning with sparse rewards and multiple goals. NeurIPS
• Pesce & Montana (2020) Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication. Machine Learning

Medical imaging. Work in medical imaging has driven the development of AI methods addressing distinctive deployment challenges: high-resolution images with low signal-to-noise ratios, severe class imbalance, longitudinal tracking, dataset shift from scanner and protocol variability across institutions, supervision ranging from pixel annotations to radiologist text reports, and clinical requirements for calibrated, interpretable, externally validated models.

• Cid et al. (2024) Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study. The Lancet Digital Health
• Macpherson, Muthuswamy, Amlani, Goh & Montana (2024) Automated ranking of chest x-ray radiological finding severity in a binary label setting. Proceedings of Machine Learning Research
• Zhu, Liakata & Montana (2024) A multi-task transformer model for fine-grained labelling of chest x-ray reports. Joint International Conference on Computational Linguistics
• Macpherson, Hutchinson, Horst, Goh & Montana (2023) Patient reidentification from chest radiographs: an interpretable deep metric learning approach and its applications. Radiology: Artificial Intelligence
• Damiani et al. (2023) Evaluation of an AI model to assess future breast cancer risk. Radiology
• Santeramo, Damiani, Wei, Montana & Brentnall (2024) Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case–control study. Breast Cancer Research
• Brentnall et al. (2023) An optimization framework to guide the choice of thresholds for risk-based cancer screening. NPJ Digital Medicine
• Byrne, Clough, Valverde, Montana & King (2022) A persistent homology-based topological loss for CNN-based multiclass segmentation of CMR. IEEE Transactions on Medical Imaging
• Annarumma et al. (2019) Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology
• Pesce et al. (2019) Learning to detect chest radiographs containing pulmonary lesions using visual attention networks. Medical Image Analysis

Neuroimaging and neuroimaging genetics. Work in neuroimaging addressed fundamental challenges in understanding brain structure and function: extracting meaningful patterns from high-dimensional, noisy neuroimaging data (fMRI, structural MRI), understanding how brain organization varies across individuals while identifying population-level commonalities, integrating imaging data with genetic and clinical information, characterizing network topology and dynamics, and developing methods that scale to large cohorts while remaining interpretable for neuroscience discovery.

• Lorenz et al. (2018) Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization. Nature Communications
• Krishnan et al. (2017) Machine learning shows association between genetic variability in PPARG and cerebral connectivity in preterm infants. PNAS
• Cole et al. (2017) Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage
• Monti, Lorenz, Braga, Anagnostopoulos, Leech & Montana (2017) Real‐time estimation of dynamic functional connectivity networks. Human Brain Mapping
• Monti, Anagnostopoulos & Montana (2017) Learning population and subject-specific brain connectivity networks via mixed neighborhood selection. The Annals of Applied Statistics
• Chung et al. (2016) Characterising brain network topologies: a dynamic analysis approach using heat kernels. NeuroImage
• Lorenz et al. (2016) The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI. NeuroImage
• de Brébisson & Montana (2015) Deep neural networks for anatomical brain segmentation. CVPR
• Monti et al. (2014) Estimating time-varying brain connectivity networks from functional MRI time series. NeuroImage
• Silver, Janousova, Hua, Thompson, Montana & Alzheimer's Disease Neuroimaging Initiative (2012) Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression. NeuroImage
• Vounou et al. (2010) Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach. NeuroImage

Bioinformatics and high-dimensional structure. Previous work in genomics and multi-omics addressed challenges inherent to molecular data: extreme dimensionality where features vastly outnumber samples, rich biological structure encoded in pathway databases and regulatory networks, substantial technical and biological variability, non-Gaussian measurement distributions, and the need to integrate heterogeneous data types (genomics, transcriptomics, methylation, imaging) to understand complex phenotypes.

• Wang, Yuan & Montana (2015) Sparse multi-view matrix factorization: a multivariate approach to multiple tissue comparisons. Bioinformatics
• Ruan, Young & Montana (2015) Differential analysis of biological networks. BMC Bioinformatics
• Wang & Montana (2014) The graph-guided group lasso for genome-wide association studies. In Regularization, optimization, kernels, and support vector machines
• Wang, Curry & Montana (2014) Network-guided regression for detecting associations between DNA methylation and gene expression. Bioinformatics
• Cozzini, Jasra, Montana & Persing (2014) A Bayesian mixture of lasso regressions with t-errors. Computational Statistics & Data Analysis
• Minas, Curry & Montana (2013) A distance-based test of association between paired heterogeneous genomic data. Bioinformatics
• Silver, Chen, Li, Cheng, Wong, Tai, Teo & Montana (2013) Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol in two Asian cohorts. PLoS Genetics
• Silver, Montana & Alzheimer's Disease Neuroimaging Initiative (2012) Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps. Statistical Applications in Genetics and Molecular Biology
• Berk, Ebbels & Montana (2011) A statistical framework for biomarker discovery in metabolomic time course data. Bioinformatics
• Minas, Waddell & Montana (2011) Distance-based differential analysis of gene curves. Bioinformatics

Selected media coverage

• AI as good as doctors at checking X-rays - study (BBC, 2023)
• World’s most advanced robotic hand is approaching human-level dexterity (Digital Trends, 2020)
• How AI could speed X-ray processing (BBC, 2019)
• See how this Artificial Intelligence can predict an abnormal chest X-ray (Forbes, 2019)
• This AI could hold the key to decoding human intelligence (Wired, 2017)
• The eyes don't have it: nose is most likely feature to be inherited (The Telegraph, 2017)
• Like mother, like daughter: face scanning technology reveals the features you're most likely to inherit from your parents (MailOnline, 2017)
• Deep-learning machine uses MRI scans to determine your brain age (MIT Technology Reviews, 2016)
• Predicting your brain age from MRI scans (NVIDIA Developer, 2016)

Contact details: g.montana@warwick.ac.uk