After years of being a concert pianist, Hae-Won Uh studied math as a hobby. She obtained MSc and PhD in Mathematical Statistics from University of Amsterdam. She has been working as a researcher in the multidisciplinary environment, specializing in Statistical Modeling and Data Science. She has been involved in various EU-funded projects: MIMOmics, BigData@Heart, IMforFUTURE, and MSA-Omics.

Her research focuses on the analysis of high-dimensional biological and medical data: specifically, statistical leaning with sparsity. In the era of big data, extracting hidden patterns from noisy heterogeneous data is essential but challenging. She develops methods for integrated analysis of various omics datasets (e.g. genetics, transcriptomics, metabolomics) to obtain more insight into biological mechanisms underlying traits. Such extracted patient-specific molecular profiles from multiple omics datasets will lead to better predictions of health outcomes.

Compositional data describe amounts of components of specimens, when the size of each specimen is constant; relative portions sum up to 100%. Because of this constraint of constant sum, direct applications of multivariate statistical methods may result in spurious conclusions. She develops statistical methods specifically for microbiome study: in particular, longitudinal models to fit the dynamic association among microbiome, environments and host.

A smartphone with a camera can detect heart rhythms, without medical assistance or additional instruments. She investigates the usage of  smartphone apps to acquires pulse waveforms via photoplethysmography (PPG). PPG is  related to cardiac-induced fluctuations in tissue blood volume using the built-in cameras and LED smartphone flash. She develops an algorithm to reconstruct the sharp spikes of a true ECG from a PPG record. This will lead to an efficient model that well discriminates an irregular pulse during AF from sinus rhythm.