GenAImede
GenAImede is a pioneering research group at the intersection of generative artificial intelligence and biomedicine. We specialize in leveraging cutting-edge AI models to unlock new insights in systems biology, identify drug repurposing opportunities, and discover hidden patterns in complex biological data.
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Our mission is to empower scientists with sophisticated AI tools that enhance research efficiency, accelerate discoveries, and tackle complex biological challenges. Led by Dr. Fernando M. Delgado Chaves, our team of dedicated professionals collaborates with academic institutions, pharmaceutical companies, and AI leaders to make biomedical research more accessible, collaborative, and impactful. Based in Hamburg, Germany, we’re committed to transforming healthcare outcomes through AI precision.
Dr. Fernando M. Delgado Chaves
fernando.miguel.delgado-chaves[a.t_))uni-hamburg.de
I began my scientific journey in biotechnology and biomedicine, working in wet lab research on molecular biology, genetics, and disease mechanisms. My early work involved experimental techniques such as CRISPR-Cas9 gene editing, transcriptomics, and cellular modeling to study cancer, developmental biology, and infectious diseases.
As I delved deeper into biomedical research, I recognized the transformative potential of computational methods in deciphering complex biological systems. This led me to transition into bioinformatics, where I specialized in network biology, multi-omics data integration, and algorithm development for gene network reconstruction. My doctoral research focused on designing advanced computational models to extract disease insights from large-scale transcriptomics data.
Building on this foundation, I now lead a research group at the University of Hamburg dedicated to generative AI in biomedicine. My work integrates large language models (LLMs) and AI-driven analytics to enhance drug repurposing, personalized medicine, and disease module identification. By combining expertise in bioinformatics with the latest advancements in generative AI, my team aims to accelerate biomedical discoveries and transform data-driven healthcare.
Simon Süwer
simon.suewer[a.t_))uni-hamburg.de
Since January 2024 I am a PhD student at CoSy.Bio for the projects FeatureCloud (https://featurecloud.ai/) and dAIbetes. I completed my Bachelor of Science in Applied Computer Science at the University of Applied Sciences and Arts Hannover, followed by a Master of Science in Computer Science at the University of Vienna, specialising in Data Science. In my master thesis I worked on the combination of session- and sequence-based recommender systems in a dynamic Graph Neural Network (GNN), in particular on the development of hierarchical dynamic GNNs.
My current research aims at developing privacy-preserving tools that make federated collaboration not only easier, but almost effortless. By merging theory and practice, my goal is to create innovative solutions to complex challenges that redefine the way we think about data collaboration and privacy