Application of AI to the Life Sciences domain: Seminar Series

By attending the full seminar series, participants will gain:

• A panoramic understanding of biomedical AI How AI is applied across clinical, molecular, and systems‑level problems, from emergency triage to drug discovery.
• Insight into cutting‑edge research methods Including AI agents, sentence transformers, genomic deep learning, speech biomarkers, and single‑cell analysis.
• Practical knowledge of clinical AI applications How AI supports mental health, stroke imaging, cardiology, and diagnostic workflows.
• Exposure to molecular and structural biology AI Techniques for modeling aging, protein energetics, and molecular interactions.
• Understanding of end‑to‑end AI pipelines From data preprocessing and model design to validation, deployment, and ethical considerations.
• Awareness of challenges and limitations Bias, data quality, interpretability, and responsible use of AI in healthcare and research.
• Opportunities for interdisciplinary collaboration Connecting computational scientists, clinicians, biologists, and industry partners around shared challenges.

AI Factory Scientific Seminar Series

Friday 29/05 – AI Methods & Systems in Health and Biomedical Research

• 10:00 – Miguel Vázquez.
AI Agents as Research Assistants
• 11:00 – Miguel Rodríguez.
Exploring Sentence Transformers and Pre-trained Language Models for Materials Science Data Extraction
• 12:00 – Eduard Rodríguez.
Building a Multi-Agent AI System for Pediatric Emergency Pre-Triage

Monday 01/06 – Clinical AI Applications
• 10:00 – Cleofe Peña.
AI in Mental Health
• 11:00 – Simón Orozco.
Deep Learning for Genomics: The Flying Gene Challenge
• 12:00 – Uma Lal.
Deep Learning for Stroke Imaging

Tuesday 02/06 – Advanced Topics in Biomedical AI
• 11:00 – Marc Grau.
Circadian Gene Expression in the Developing Brain at Single-Cell Resolution
• 14:00 – Paula Petrone.
From Imaging to Diagnosis: AI in Cardiology and Ultrasound

Wednesday 03/06 – Molecular AI & Drug Discovery
• 10:00 – Natàlia Pujol.
Multimodal data analysis reveals asynchronous aging dynamics across female reproductive organs
• 11:00 – Franco Simonetti.
Energetic Frustration in Proteins: Implications for Function and Disease
• 12:00 – Ruite Xiang.
AI-Driven Approaches to Hit Identification in Drug Discovery

• Cleofé Peña-Gómez
• Eduard Rodriguez
• Franco Simonetti
• Marc Grau
• Miguel Rodriguez
• Miguel Vazquez
• Natalia Pujol
• Ruite Xiang
• Simon Orozco
• Uma Maria Lal Trehan
• Paula Petrone

• Dr. Cleofé Peña-Gómez have a background in psychology, neuroscience, data science, and machine learning. He completed his PhD at the University of Barcelona, where his research focused on brain network connectivity, neuroimaging, and cognitive and emotional processing. He subsequently held postdoctoral positions at Indiana University and Harvard University, where he studied individual brain “fingerprints” using graph theory, machine learning and artificial intelligence approaches. More recently, He worked as a data scientist in the Mental Health Unit at Hospital Parc Taulí. He is currently an “AI for Science (AI4S)” Fellow at the Barcelona Supercomputing Center (BSC), working at the intersection of artificial intelligence and life sciences.
• I am Eduard Rodriguez Lopez, a researcher in the NLP4BIA team at Barcelona Supercomputing Center, with a background in Mathematical Engineering in Data Science. My work focuses on applying the power of Large Language Models (LLMs) in clinical settings for classification and decision-support tasks. Examples include early diagnosis of disorders based on patients’ medical history, automated clinical pre-triage systems, and the extraction and structuring of information from unstructured medical records. I am particularly interested in developing neurosymbolic and multi-agent AI systems that are not only accurate and explainable, but also auditable and reliable enough for real-world healthcare applications.
• Franco Simonetti is a bioinformatician working at the intersection of protein biophysics, evolution, and artificial intelligence. His research focuses on developing computational methods to understand how protein sequences encode structure, dynamics, and function, and how alterations in these properties relate to health and disease. He obtained his PhD from the University of Buenos Aires, where he developed tools to study protein sequence coevolution in protein families. He later joined the Max Planck Institute for Multidisciplinary Sciences in Göttingen as a postdoctoral researcher, developing Bayesian approaches to identify long-range regulatory interactions using biobank-scale transcriptomic data. He is currently an AI4Science fellow in the Life Sciences department at the Barcelona Supercomputing Center (BSC), where he works on local energetic frustration and AI-based approaches to study protein function, dynamics, and evolution.
• Marc Grau Leguia is a postdoctoral AI4Science fellow at the Barcelona Supercomputing Center, working in Marta Melé’s Transcriptomics and Functional Genomics Lab. He earned his PhD in 2019 through a joint doctorate between the Faculty of Information Studies (Slovenia) and Universitat Pompeu Fabra, as part of the Horizon 2020 COSMOS network. As a postdoc in the E-Lab at Inselspital (Bern), he showed that seizures in focal epilepsy follow robust circadian and multiday cycles, and that recurrent neural networks trained on these rhythms can forecast individual seizure risk days in advance. He now applies unsupervised methods for circadian phase inference to single-cell transcriptomic data of the developing human cortex, investigating how the brain clock is established during development and how sex shapes this process at the cellular level.
• Miguel Rodríguez is a Research Engineer at the NLP for Biomedical Information Analysis group (NLP4BIA), Barcelona Supercomputing Center (BSC). His work focuses on applying NLP and leveraging large language models to the biomedical and healthcare domains, covering tasks such as text classification and summarization, semantic similarity, knowledge graph construction, and text embedding analysis. He holds a Master’s degree in Nuclear Physics from the Complutense University of Madrid (UCM) and has previous experience as an NLP Research Engineer at the National University of Distance Education (UNED) and Polytechnic University of Madrid (UPM).
• Miguel Vázquez García is Principal Investigator and Head of the Genome Informatics Unit at the Barcelona Supercomputing Center. He has more than 15 years of experience developing bioinformatics platforms, reproducible scientific workflows, cancer genomics tools, biomedical text-mining systems and AI-assisted research environments. He is the creator of Rbbt and Scout, software frameworks for scientific workflows, data integration, knowledge bases and computational services, and more recently Scout-AI, an agentic AI layer that connects large language models with workflows, tools, documents and domain-specific knowledge. His work spans international cancer genomics projects such as ICGC and PCAWG, biomedical NLP systems such as CollecTRI/CollecTRI2, precision medicine infrastructure, Boolean/cellular modelling and AI-for-science initiatives including the Trillion Parameter Consortium. At BSC, he focuses on building practical computational systems that help scientists reason over complex biological data and transform AI prototypes into reproducible, useful research tools.

• Natalia Pujol – I am a biomedical scientist with a PhD from the University of Tartu and the University of Oulu, where I investigated the genetic basis of female reproductive phenotypes using large-scale genomic analyses of biobank and prospective cohort data. I am currently a postdoctoral fellow in the Transcriptomics and Functional Genomics Lab, focusing on -omics analyses to understand sex differences in aging and disease susceptibility, with emphasis on the role of key reproductive events such as menopause.

• Ruite Xiang is a researcher at the Barcelona Supercomputing Center (BSC) and is currently completing his PhD in Bioinformatics. His doctoral research has focused on molecular modeling and the application of artificial intelligence to proteins. At BSC, he is part of the AI Factory within the Life Sciences department, where he contributes as a domain expert in proteins and small molecules. His work centers on supporting the development and application of AI methodologies in biomolecular contexts, bringing domain knowledge in structural biology and molecular systems into AI-driven projects.
• Simon Orozco-Arias is a computational engineer and bioinformatician specializing in transposable elements (also called flying genes), genomics, and deep learning. He holds a Ph.D. in Engineering from Universidad de Caldas (Colombia), where he developed advanced machine learning approaches for the annotation and classification of genomic elements.
  He later moved to Barcelona to pursue a postdoctoral position at the Institute of Evolutionary Biology (IBE), where he deepened his research on the role of transposable elements in genomic adaptation to environmental changes and their implications in disease. Currently, he is an AI4Science postdoctoral fellow at the Barcelona Supercomputing Center (BSC-CNS), where he focuses on developing foundation models and scalable AI-driven methodologies for genomic analysis. His work integrates high-performance computing with state-of-the-art neural network architectures, including transformers, to address complex biological questions at scale.
• Uma Lal is a Biomedical Engineer with a PhD in Artificial Intelligence for Medical Imaging from the University of Girona, conducted in collaboration with the University of Texas Health Science Center at Houston. Her research focuses on deep learning for medical imaging, with a particular emphasis on acute ischemic stroke.
  She has experience developing machine learning and deep learning models for complex, high-dimensional data, especially in medical imaging. Her work involves close collaboration with clinicians to ensure clinical relevance. In addition to her research, she has experience teaching artificial intelligence at both undergraduate and graduate levels.
• Dr. Paula Petrone is Head of the Digital Health Unit at the Barcelona Supercomputing Center (BSC), where she leads the development of AI-driven solutions for early disease detection, risk assessment, and personalized treatment using multimodal health data, including medical imaging, wearables, and electronic health records. A physicist trained at Instituto Balseiro with a Ph.D. in Biophysics from Stanford University, she brings over 15 years of international experience across academia, industry, and digital health innovation. She has worked at Roche and Novartis, applying machine learning to drug discovery and translational research. Her work focuses on chronic conditions, with experience spanning cardiovascular disease, liver disorders, and mental health, alongside a strong emphasis on medical imaging and clinically deployable AI. She collaborates closely with hospitals, biotech, and medtech companies to deliver explainable and trustworthy AI solutions, and has led multiple European-funded innovation projects from scientific strategy through regulatory planning.
  Dr. Petrone is an active speaker on AI, ethics, and health equity, a former Women in Data Science (WiDS) Ambassador (2021–2024), and an advocate for responsible and inclusive AI in healthcare.

This seminar series offers a comprehensive journey through the rapidly evolving landscape of AI in biomedical research. Across four thematic days, participants explore how artificial intelligence is transforming scientific discovery, clinical workflows, molecular biology, and drug development.

The programme brings together experts working on AI agents, clinical applications, genomics, speech analysis, single‑cell biology, cardiology imaging, aging research, protein energetics, and drug discovery pipelines.

Designed for researchers, clinicians, and industry professionals, the series blends conceptual foundations with real‑world case studies, showcasing how modern AI methods—from deep learning to multimodal modeling—are reshaping the future of health and life sciences.

Certificate of Attendance

Learn More About the Speakers & Their Work