Alexander Schubert is a Ph.D. Student in Computational Precision Health at UC Berkeley and UCSF. His research interests focus on the application of machine learning as a tool to improve decision-making in health and to ensure that healthcare innovations are accessible and equally impactful to everyone. A particular focus of his work lies on applications of artificial intelligence to cardiovascular disease, where he aims to leverage unstructured data modalities such as ECG waveforms to improve the assessment and understanding of cardiovascular risk in order to better target medical interventions. He is a Fellow at the Eric and Wendy Schmidt Center of the Broad Institute. Previously, he was a senior consultant at McKinsey & Company, working on analytics projects for clients in the Pharmaceutical and Healthcare industry.
Machine Learning, AI for Medicine, Cardiovascular Disease Prediction, Biomedical Data Science
