intro

Our lab is dedicated to advancing the field of neuroscience by developing in silico models that harness machine learning to reveal biological insights. Our mission is to not only contribute to foundational scientific knowledge but also to promote a future with reduced reliance on animal experimentation. To achieve these goals, we work with multimodal omic data (e.g., scRNA-seq) as well as functional data (e.g., Ca imaging). At the core of our research, we are actively developing computational approaches that leverage the latest advances in deep generative modeling and artificial intelligence.

latest news 🎉

• February 2026: We’re delighted to announce the launch of VISI-ON-BRAIN, a €4.5 million Horizon Europe doctoral network. This network aims to advance human-relevant neuroscience tools beyond animal models. In our case, we will soon be looking for a PhD candidate to work on Multimodal Deep Learning for Neurodevelopmental Modelling!
• January 2026 (II): Our paper on the integration of single-cell calcium imaging data has been accepted to ICASSP 2026, which will take place this year in Barcelona. This work was brilliantly led by Berta Ros, a PhD student in our lab. See you at ICASSP 2026!
• January 2026 (I): Our PMLR paper on the age of onset of Huntington’s disease was published in PMLR in December. Here are a few articles on this work: UB News, UB homepage, Institut de Neurosciences; social posts: LinkedIn, X, Bluesky; and videos by first author Caterina Fuses: Instagram, YouTube, TikTok.
• December 2025: We welcome David Bonet, who joins the lab as a PhD student co-supervised by Alex Ioannidis (Stanford University)!
• October 2025: We are excited to welcome David García (undergraduate intern) and Zinnera Tariq (master’s thesis student) to our lab for the fall semester!
• September 2025: Congratulations to Berta Ros, a recent master’s student in our lab, for receiving the FI-STEP fellowship to pursue her PhD! Berta will develop deep generative models for multimodal analysis of transcriptomic and functional data, with a focus on applications to neurodegenerative disease research such as HD.
• August 2025: Our paper on identifying genetic modifiers in Huntington’s disease using a multimodal phenotype prediction model has been accepted for presentation at MLCB 2025! The work will be published in PMLR later this year. Caterina F. will present our findings at the conference, highlighting how our approach integrates diverse data types to advance understanding of neurodegenerative disease mechanisms.
• April 2025: Our lab, in collaboration with the Canals, del Toro, and Giralt labs at the Universitat de Barcelona, along with other esteemed European institutions, has been awarded a prestigious MSCA Doctoral Network grant! This funding will support cutting-edge research and training opportunities for the next generation of scientists at the intersection of neuroscience and machine learning. Stay tuned for more updates on this exciting initiative!

research areas

Our work develops machine learning and probabilistic methods to connect molecular variation and cellular states with brain function and behavior. Current projects span multi-omics integration, neural data foundation models, and clinically motivated genomics and neuroscience applications.

single-cell and multi-omics genomics

• Computational pipelines for single-cell and multi-omics data analysis and QC
• Multimodal integration and representation learning across omics layers (for example transcriptomics, proteomics, and epigenomics)
• Latent factor and trajectory methods to map cellular programs and disease-associated perturbations, including exposure and mental health contexts

genotype–phenotype modeling and genomic AI

• Genotype-to-phenotype prediction in neurobiology and neurodegeneration settings
• Sequence-based representation learning (genomic language models) and multimodal genomic prediction
• Projects linking inherited variation, molecular readouts, and downstream phenotypes, including telomere-related traits

computational neuroscience and neural foundation models

• Deep generative and probabilistic models for neuronal functional recordings, with emphasis on calcium imaging
• Single-neuron and population-level dynamics, simulation-based validation, and scalable inference
• Foundation-model approaches for neural data and neurotechnology, including photostimulation-oriented modeling

integrative neurogenomics and translational brain research

• Multi-modal integration of molecular and functional data to study brain-related phenotypes
• Collaborative projects combining omics and neuroscience data in disease and intervention contexts, including multi-partner networks

environment

Our lab is part of various world-class research institutions and groups in Barcelona, including:

• Institute of Neurosciences at UB (UBNeuro) [URL]
• TECNIO Group of Data Science and Artificial Intelligence at UB [URL]
• Group of Pathophysiology and Treatment of Neurodegenerative Disorders at IDIBAPS [URL]

Our lab’s research pushes the boundaries of what can be achieved with in silico models in the life sciences, and we are excited to be at the forefront of discoveries that have the potential to transform neuroscience. We welcome collaborations and discussions with others interested in our work.