Talk abstracts
This page collects talk titles, abstracts and short speaker biographies for GMTD 2026.
← Back to programme Jump to 30 September ↓
29 September 2026
Dr. Alessandro Bonetti
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Dr. Mikhail Kabeshov
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Fabio Bove and Marco Ferrarini
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Dr. Gabriele Corso
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Dr. Esther Wershof
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Dr. Fabian Frohlich
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Dr. Michail Mamalakis
From Genes to Brain Imaging: Interpretable and Adaptable AI for Understanding Brain Disease
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Understanding brain disease requires integrating molecular changes with macroscopic alterations observed through magnetic resonance imaging (MRI). I present complementary artificial intelligence frameworks for analysing brain MRI and single-cell RNA sequencing data. AlcaMoena, a three-dimensional MRI foundation model pretrained without manual labels on more than 425,000 volumes and derived maps, combines volumetric encoding, latent diffusion, and Graph-Blueprint Pruning to expand across clinical domains while limiting the loss of previously acquired capabilities. Its representations support image synthesis, disease classification, survival modelling, and postoperative prediction. At the molecular level, I integrate graph-based gene embeddings with transformer-based language models to capture temporal gene dynamics and introduce a drift score that measures changes in gene relationships across biological states. Applied to ependymoma, the framework recovered 70 of 89 known disease-associated genes—a 30% improvement over the best individual method—and identified candidate biomarkers with disease-specific progression patterns. Together, these approaches provide scalable and interpretable tools for connecting cellular dynamics with imaging phenotypes and supporting biomarker discovery and clinical prediction
Speaker bio
Michail Mamalakis is an Assistant Research Professor at the Cancer Research UK Cambridge Institute, a Postdoctoral Transition Fellow at the CRUK Children’s Brain Tumour Centre of Excellence, and an Affiliated Lecturer at the University of Cambridge. His research develops explainable and trustworthy multimodal AI for biomedical discovery, integrating medical imaging, histology, and molecular data. His current work focuses on foundation models and cross-scale computational methods for paediatric and adult brain tumours. He holds a PhD in Computer Science from the University of Sheffield
Dr. Tiansi Dong
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Dr. Larry Melidis
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Dr. Anurag Limdi
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Prof. Tom McClelland
The Problem of Artificial Consciousness
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The notion of artificial consciousness is now gaining significant traction with developers, users and researchers. How seriously should we take this idea? When should we conclude that an AI is conscious? And what are the ethical ramifications of creating conscious AI? I argue that it is very difficult to determine the prospects of consciousness scientifically and suggest how to navigate this uncertainty responsibly.
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Tom McClelland is a Lecturer in the Department of History and Philosophy of Science at the University of Cambridge and an Associate Fellow of the Leverhulme Centre for the Future of Intelligence. His work in AI explores whether AI has the potential to be conscious and/or creative.
30 September 2026
Dr. Johann Hawe
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Dr. Ilaria Billato
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Dr. Bianca Pierattini
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Dr. Tania Bobbo
Machine Learning for Probiotics Discovery: The PROB-AI Project
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Probiotics are microorganisms that confer health benefits to humans. Most known probiotics belong to the Lactobacillus, Bifidobacterium, Bacillus, Streptococcus, and Enterococcus genera, although new probiotic taxa continue to emerge. To be considered probiotic, bacterial strains must be safe for human use, survive passage through the gastrointestinal tract, and colonize the gut. These properties are typically assessed through extensive in vitro and in vivo experiments, making probiotic discovery slow and costly. The PROB-AI project aims to accelerate this process by developing machine-learning methods to identify probiotic bacteria directly from genomic data. ML models trained on confirmed probiotic genomes have been used to predict novel candidate strains, providing insights into the genomic determinants of probiotic traits and supporting the discovery of new microorganisms for biomedical and pharmaceutical applications. This ML-based approach represents an innovative and still underexplored strategy with significant potential for microbiology, industry, and human health. We analysed a dataset of approximately 1,000 bacterial genomes, including around 30 confirmed probiotic strains from public repositories. Genomic features were extracted from NCBI genome sequences using the GRRAP in-house developed tool, together with k-mers. Different ML models were fine-tuned using repeated k-fold cross-validation. The best-performing models were retrained on the training set and evaluated on a held-out subset. Finally, the best models were applied to an independent dataset of approximately 1,500 unlabelled bacterial genomes from NCBI to identify novel candidate probiotic strains to be experimentally validated in vitro.
Speaker bio
Tania Bobbo is a researcher at the National Research Council, CNR, Italy, with expertise in applying biostatistics, bioinformatics, and machine learning to the analysis of plant, animal, and human phenotypic, genetic, and genomic data. Her current research focuses on the development and application of AI algorithms for the analysis of bacterial genome sequences to identify novel probiotic strains.
Dr. Davide Rigoni
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Flavio Sartori
Scoring Tissue Health from Microscopy to Enable Causal Analysis in Dry AMD
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Age-related macular degeneration (AMD) is a common cause of central vision loss, and its dry form still has no treatment for advanced stages. A key difficulty is distinguishing a genuine drug effect on the retinal pigment epithelium from technical noise. We address this with a health score derived directly from microscopy images. After cleaning and normalising the data, each tissue region is scored according to its deviation from the healthy control state. This score then becomes one of the per-patch variables used in a causal model, alongside the applied compound and other local measurements, helping disentangle how much of the observed change is actually caused by each compound in dry AMD.
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Flavio Sartori is a PhD candidate at the Doctoral School in Complex Systems for Quantitative Biomedicine at the University of Turin, Italy, with a background in physics. He is currently a PhD Intern at Hoffmann-La Roche in Basel, where he works on image analysis for organoid experiments. His research focuses on applying d
Matteo Baldan
Learning to Simulate: Neural Operators and the Generation of Physical Dynamics with Applications in Cardiac Electrophysiology
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Many physical and biological processes can be represented as dynamical systems whose internal state evolves in response to initial conditions, external inputs, and system parameters. Mechanistic simulators approximate this evolution by combining mathematical models with numerical discretisation. Although these approaches remain essential for scientific understanding, their computational cost can limit parameter exploration, uncertainty quantification and the simulation of complex, subject-specific systems. Neural operators provide an emerging alternative: instead of learning predictions at a fixed number of coordinates, they learn mappings between functions and can therefore approximate the evolution or solution operator of an entire family of dynamical systems.
This talk introduces neural operators from an engineering and state-space perspective. After briefly tracing their development from classical operator theory and data-driven system identification to modern deep operator networks, Fourier neural operators and geometry-aware architectures, we describe their mathematical foundations through the concepts of state, input, observation and evolution operators. In this formulation, a neural operator may learn either the map from system conditions to a complete trajectory or the transition operator that advances the state through time.
Particular attention is given to how a continuous operator becomes a computational architecture. Its kernel integration must ultimately be approximated over grids, meshes or graphs, requiring aggregation rules that are independent of node ordering and robust to changes in spatial discretisation. The choice of kernel variables—such as physical distance, relative position, direction and material properties—therefore provides an important engineering inductive bias linking the learned architecture to the dynamics of the system.
Examples from biological modelling illustrate how operator learning can represent multiscale systems whose states are spatially distributed and only partially observed. Cardiac electrophysiology is then presented as a central application: electrical activation emerges from nonlinear reaction-diffusion dynamics on heterogeneous, anisotropic and patient-specific cardiac geometries. We discuss the basics of electrocardiophysiology and of learning surrogate models of transmembrane potential regression
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Coming soon
Massimiliano Caretti
Title to be announced
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Abstract coming soon.
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Massimiliano Caretti’s research focuses on artificial intelligence for molecular diagnostics and computational biology. He develops interpretable machine-learning models for vibrational spectroscopy, including FTIR and micro-Raman data, as well as single-cell and spatial omics methods to support cancer diagnosis, tumour heterogeneity characterisation and precision oncology.
Andrea G. Di Francesco
RAG Beyond NLP: Learning Relevant Context for Cellular Prediction
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Retrieval-augmented generation (RAG) is usually presented as a paradigm for language models: retrieve relevant documents using a fixed similarity function, then condition generation on them. Outside NLP, however, relevance may be neither obvious nor context-independent.
I will present PT-RAG, a differentiable retrieval framework for predicting single-cell responses to genetic perturbations. PT-RAG first retrieves candidate perturbations using semantic gene representations, then learns which candidates are useful for a particular query and cellular state through an end-to-end differentiable selector. A central empirical finding is that naïve retrieval can actively degrade prediction, whereas learned, cell-state-aware selection improves reconstruction and the fidelity of predicted cell-population distributions across different generative backbones.
The broader message is that extending RAG beyond language is not simply a matter of attaching a vector database to a new model. It requires us to reconsider what the retrievable units should be, how information should be represented, and whether and how relevance itself must be learned from the downstream objective. I will discuss how cellular perturbation prediction exposes these challenges and outline opportunities for neural retrieval in scientific domains, including context-conditioned retrieval and possible directions in richer multimodal representations
Speaker bio
Andrea Giuseppe Di Francesco is a third-year Ph.D. student in Data Science at the Dipartimento di Ingegneria Informatica, Automatica e Gestionale (DIAG), Sapienza University of Rome. His research focuses on the theoretical foundations of Graph Neural Networks, with emphasis on expressivity, inductive biases, and their integration with GraphRAG systems. He actively collaborates with the Institute of Information Science and Technologies (ISTI-CNR) in Pisa.
David Miller
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Salvo Romano
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Andrea Rubbi
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Wageesha Widuranga
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