معرفی
Nico Pfeifer is a W3 Professor in Computer Science (Methods in Medical Informatics) at the Faculty of Science, University of Tübingen. He leads a research group focused on statistical learning in computational biology, with a strong emphasis on HIV and SARS-CoV-2 antibody dynamics, viral resistance, and machine learning applications in virology. His work bridges computer science and medical informatics, contributing to high-impact therapeutic and diagnostic developments.
Educational Background:
- Dr. rer. nat. in Bioinformatics/Computational Biology, University of Tübingen (2009)
- M.Sc. in Applied Computer Science, University of Göttingen (2005)
- B.Sc. in Applied Computer Science (Bioinformatics), University of Göttingen (2004)
His research interests include medical informatics, computational biology, machine learning in virology, HIV drug resistance, antibody dynamics, and next-generation sequencing analysis. His work leverages statistical learning and computational modeling to address critical challenges in viral evolution and immune response prediction.
The recent publications highlight a strong trend in applying machine learning and bioinformatics to understand HIV-1 and SARS-CoV-2 antibody therapies, viral resistance mechanisms, and immune responses. His research spans computational modeling, clinical data analysis, and translational medical informatics, with a consistent focus on improving viral treatment strategies and diagnostics.
Scientific Awards:
- No specific awards mentioned in the provided text.
Nico Pfeifer has been actively involved in advising and collaborative research, particularly in large-scale virology studies involving monoclonal antibodies and viral resistance. His work has been supported through institutional and collaborative grants, especially during his time at the Max Planck Institute and Microsoft Research. He has led significant research projects in computational virology and continues to contribute to high-impact interdisciplinary science.
He is associated with the Methods in Medical Informatics research group at the University of Tübingen, where he leads efforts in developing computational tools for viral genomics and antibody response modeling. His team likely includes researchers and students working on machine learning applications in medical informatics and virology.
