Latent variable modeling of complex high-dimensional data

点击翻译按钮获取中文摘要

As an Assistant Professor in Data Science my research work consists of developing novel statistical methods to analyze large complex data structures so that important scientific questions can be answered. In particular, my proposed research program is focused on the development of techniques to analyze data when crucial information is absent. While my methods can be applied to a wide range of areas in the Natural Sciences and Engineering, I will consider two main application themes over the next five years: 1) analysis of electrical load data from substations and 2) DNA and RNA sequencing data from single cells. In Theme 1, I will consider aggregated energy consumption data measured over time at substations that serve a fixed number of consumers from different types (e.g., residential, commercial and industrial). The individual consumer-level energy usage curves are not observed, only the sums of individuals' energy usage. My goal is to develop novel statistical methods to infer the typical weekly energy usage curve for each type of consumer using the substation aggregated data and additional information, such as temperature and characteristics of the substations (e.g., capacity and number of low voltage feeders). In addition, I will cluster substations into different groups according to their consumer type-specific energy usage curves. This work will allow power companies around the world to better understand energy usage in order to provide adequate energy at low cost. In Theme 2, I will analyze DNA and RNA sequencing data obtained from individual cells. Because the amount of DNA/RNA material per cell is limited, the resulting single-cell sequencing data contain technical noise and a large amount of missing information. My goal is to build new statistical tools to infer the different groups of cells comprising a tissue based on their DNA/RNA sequencing data taking into account the challenges arising from this type of technology. My work on single-cell genomics will provide scientists in various areas of biology with the adequate set of statistical tools to assess the genomic composition of cells, which will lead to a better understanding of how individual cells differentiate to form tissues and how tissues work. My proposed research team consists of two MSc and two PhD students working under Theme 1 and one MSc and two PhD students working under Theme 2. All the resulting research work under Themes 1 and 2 will be submitted for publication at high-impact scientific journals, as well as presented by the students and myself at relevant conferences. In addition, all statistical methods developed will be implemented in the free software environment R and will include user-friendly tutorial guides.