Scholar
Patrick Schwab
Google Scholar ID: MG2IF00AAAAJ
GSK
Citations & Impact
Citations
1,737
H-index
19
i10-index
26
Publications
20
Co-authors
13
list available
Publications
Resume (English only)
Academic Achievements
- Publications:
- - Nature Medicine: Varicella-zoster virus and the risk of dementia
- - Nature Computational Science: In silico biological discovery with large perturbation models
- - Nature Biomedical Engineering: A collaborative large language model for drug analysis
- - arXiv preprint: Sensing Cardiac Health Across Scenarios and Devices: A Multi-Modal Foundation Model Pretrained on Heterogeneous Data from 1.7 Million Individuals
- - Nature Biomedical Engineering: Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World
- Conference Presentations and Organizational Activities:
- - Co-organized the MLDD 2024 symposium
- - Presented at the OxML summer school ML x Bio & Health track in 2024
- - Presented DiscoBAX work at ICML 2023
Research Experience
- Work Experience:
- - GSK.ai, Senior Director of Machine Learning and Artificial Intelligence, Head of the Biomedical AI group
- - Roche, Principal Architect working on Machine Learning for Personalized Medicine
- - Genentech, Principal Architect working on Machine Learning for Personalized Medicine
- - ETH Zurich, Doctoral Researcher in Machine Learning for Healthcare
- - Industry, 5 years building custom data-driven software solutions
Education
- PhD: 2019, ETH Zurich, Switzerland, Machine Learning
- MSc: 2015, University of Vienna, Austria, Computer Science (with distinction)
- BSc: 2013, Technikum Wien, Austria, Computer Science (with honors)
Background
- Research Interests: Machine Learning, Big Data, Deep Learning, Causality, Health Care
- Professional Field: Personalized Medicine, Computational Systems Biology, Utilizing Large-Scale Health Data
- Bio: Currently Senior Director of Machine Learning and Artificial Intelligence and Head of the Biomedical AI group at GSK.ai. Aims to advance personalized medicine by utilizing machine learning, computational systems biology methods, and large-scale health data (such as genetics, multi-omics, cell-based assays, and continuous measurements from smart devices and electronic health records) to better understand and treat complex diseases.
Miscellany
- Personal Interests: Not provided
