We are a multidisciplinary group that uses statistical and machine learning tools to address both theoretical questions and real-world problems.
As statisticians, we look not only for a solution but also aim to estimate its uncertainty.
Current theoretical research questions include machine learning tools for missing data and survival data, uncertainty estimation in machine learning, and estimation in high-dimensional problems. Current research topics originating in real-world systems include service engineering, change-point detection in medical and industrial settings, public health problems including COVID-19, and mental health problems.
Our tools include statistical theory, machine learning, survival analysis, missing data, kernel machines, reinforcement learning, and mixed-effect models.