Olga Demler, Ph.D.
- olga.demler@ inf.ethz.ch
Biomedical Informatics Group
- CAB F52.2
I hold a joint appointment as a Senior Scientist Research at ETH Zürich in the Biomedical Informatics lab and as an Assistant Professor/Biostatistician at the Division of Preventive Medicine at MassGeneral Brigham Hospital / Harvard Medical School, Boston, USA.
At the BMI lab at ETH, I am expanding the methodological part of my research. I am interested in incorporating change-point detection when performing signal correction of metabolomics data. I also work on the assessment of the role of intransitive stochastic relationships in the analysis of medical data. Additionally, I work on expanding the capabilities of existing image data in disease diagnosis and prognosis using deep learning.
My research interests are three-fold: 1) conducting biomarker discovery studies using high throughput metabolomics and other -omics data; 2) building predictive models using Electronic Health Records and image data; 3) methodological research on building valid risk prediction models.
My clinical research focuses on the mechanistic pathways of cardiovascular disease development and progression, while my methodological research is applicable across medical fields.
I completed my Ph.D. at Boston University (Boston, USA), under the supervision of Ralph D’Agostino and Michael Pencina, working on methodological issues in the assessment of discrimination of risk prediction models. In 2012 I joined the Division of Preventive Medicine initially as a postdoc-level fellow and currently as an Assistant Professor, where I expanded my field of research by adding two more areas. I lead the design and analysis of high throughput metabolomics studies, primarily with Samia Mora’s group. I am also a Principal Investigator on a project adapting risk prediction models to clinical data. We have emulated a prospective cohort study by collecting medical history, laboratory biomarkers, and other data for 93K patients using Electronic Health Records data from large Boston-area hospitals with a median follow-up of 13 years. Recently we added image data to it.