معرفی
Gisela Gabernet is an Associate Research Scientist in the Department of Pathology at Yale School of Medicine. She is affiliated with the Kleinstein Lab, focusing on computational and systems biology research. Her academic training includes a PhD from ETH Zurich in Computational Drug Design (2018), an MSc from TU Dresden in Molecular Bioengineering (2014), and a BSc from Universitat Autònoma de Barcelona in Biotechnology (2012).
Her research emphasizes the development of bioinformatics tools and pipelines, particularly for analyzing immune receptor repertoires, genomic data, and multi-omics datasets. She contributes to projects such as nf-core/airrflow, NCBench, and AMULETY, which advance reproducible research and computational workflows in immunology and genomics. Her work addresses clinical challenges like long COVID prediction, multiple sclerosis antibody dynamics, and cancer immunology. She collaborates extensively with interdisciplinary teams to translate computational methods into clinical and experimental applications.
Professional Roles:
- Associate Research Scientist, Yale School of Medicine Pathology
- Member of Kleinstein Lab
Key Research Themes:
- Immune repertoire analysis and computational immunology
- Development of bioinformatics pipelines (e.g., nf-core, Nextflow)
- Machine learning applications in healthcare and genomics
- Multi-omics approaches to disease mechanisms
Recent Projects: Includes studies on SARS-CoV-2 transmission dynamics, predictive modeling for long-term disease outcomes, and the role of immune cells in premature infants. Her work bridges computational innovation with clinical relevance in oncology, neurology, and infectious disease.
