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| Guido Brambilla | |
|---|---|
| Name | Guido Brambilla |
| Birth date | c. 1960s |
| Birth place | Italy |
| Alma mater | University of Milan |
| Occupation | Computer scientist, bioinformatician |
| Known for | Computational biology, machine learning applications to genomics |
Guido Brambilla is an Italian computer scientist and bioinformatician noted for contributions to computational biology, machine learning applied to genomics, and interdisciplinary work bridging computer vision and biological data analysis. He has held academic appointments in European research institutions and collaborated with industrial laboratories on translational bioinformatics projects. His work spans algorithm design, high‑performance computing, and applied studies in biomedical imaging and genomics.
Brambilla was born in Italy and completed his formative studies at institutions including the University of Milan where he earned degrees in Computer Science and related computational disciplines. During his graduate training he undertook research influenced by developments at laboratories such as CERN, Politecnico di Milano collaborations, and exchanges with groups at Massachusetts Institute of Technology, Stanford University, and École Polytechnique Fédérale de Lausanne. His doctoral and postdoctoral work integrated methods from Machine Learning, Signal Processing, and Computer Vision with applications inspired by projects at centers like European Molecular Biology Laboratory and European Bioinformatics Institute.
Brambilla’s academic appointments have included faculty and research positions at universities and national laboratories across Europe, involving collaborations with departments linked to University of Pavia, University of Bologna, University of Padua, and research centers such as Istituto Italiano di Tecnologia and CNR. He contributed to consortia funded by the European Commission and participated in collaborative projects with industrial partners including IBM Research, Siemens Healthineers, Philips, and biotech firms connected to Novartis and GlaxoSmithKline. His research groups combined expertise from labs working on High Performance Computing, Parallel Computing, and multidisciplinary teams from institutes like Max Planck Society and CNRS.
Brambilla developed algorithms and software addressing challenges in genomics, transcriptomics, and biomedical imaging, drawing on methodologies from Deep Learning, Support Vector Machine, and graph‑based models influenced by research at Google DeepMind and Facebook AI Research. He proposed pipeline designs for next‑generation sequencing analysis integrating tools and standards from Ensembl, GenBank, and UniProt annotations, and interfaced workflows with platforms such as Galaxy Project and Bioconductor. In biomedical imaging, his approaches combined techniques from Convolutional Neural Network research inspired by work at ImageNet and reconstruction methods related to efforts at National Institutes of Health. He addressed scalability by leveraging architectures and middleware associated with OpenMP, MPI, and cloud resources from Amazon Web Services and Microsoft Azure used in translational projects with clinical partners like Mayo Clinic and Karolinska Institute. Brambilla also contributed to standards and ontologies aligning with Gene Ontology initiatives and interoperability practices promoted by ELIXIR and Global Alliance for Genomics and Health.
His recognitions include grants and awards from European and national bodies such as the European Research Council and national academies connected to Accademia dei Lincei and research prizes sponsored by organizations like IEEE, ACM, and thematic awards associated with European Molecular Biology Organization. He received conference best‑paper and distinguished reviewer awards from venues including NeurIPS, ICML, ISMB, and MICCAI, and was invited to give keynote lectures at symposia organized by EMBL, CIBIO, and industrial research summits hosted by EMEA branches of major corporations.
Brambilla’s selected works span peer‑reviewed journals and conference proceedings, with publications in outlets related to Nature Biotechnology, Bioinformatics (journal), IEEE Transactions on Pattern Analysis and Machine Intelligence, and proceedings of NeurIPS, ISMB, and MICCAI. Representative topics include scalable alignment pipelines, machine‑learning classifiers for variant interpretation, and image segmentation methods for histopathology informed by collaborations with hospitals such as Ospedale Niguarda and research centers like Harvard Medical School. He is listed as inventor on patents covering computational pipelines and software tools licensed to industry partners and has contributed to open‑source projects hosted within communities such as GitHub and SourceForge.
Category:Italian computer scientists Category:Computational biologists Category:Bioinformaticians