I am working as a PhD student with Professor Christian Holz at the Sensing, Interaction & Perception Lab at ETH Zurich. My focus lies on applying statistics and statistical machine learning to large medical datasets.
My research primarily revolves around biomedical time series for disease (risk) modeling. Initially, I analyzed perceived health using wearable sensor data in intensive longitudinal studies (see my publications on perceived health). Since then, I have developed new methods to extract information from wearables (e.g., Nightbeat) and modeled disease and mortality risk based on wearable sensors at a population scale on the UK Biobank (currently under review). Currently, I am exploring methodologies to link irregular, multimodal biomedical time series to disease outcomes with a focus on interpretability and causality.
Below are some of the most recent publications I’ve been involved in. You can check out a full list of my publications here.
There are a few common themes: interpretable modeling, mobile health, perceived health, and human computer interaction.
Most of my past projects involved interpretable modeling techniques to better understand the outcome of interest rather than simply predicting it. A few publications focus on perceived health, such as fatigue or sleep quality, and I’ve been a sidekick on a few publications in human computer interaction, where I mainly contributed to the (interpretable) statistical analysis.