My long-term research goal is to develop statistically effective and computationally efficient algorithmic framework to detect, represent, and manipulate functional, temporal, and statistical associations among random variables, to account for causal interactions in dynamic biological networks. I call this the three-association framework (3AF).
Broadly, my research interest is concerned with the interface at computer science, statistics, and applied mathematics. Based on principles of summarizing observations, statistics determines a quantity to be computed from observed data and evaluates how significantly a hypothesis is supported by the quantity. Computer science pursues efficient algorithms to compute the quantity. That quantity in my research is often formulated as fitting errors of dynamic models typically studied in applied mathematics. Challenging problems often involve all. I develop effective and efficient statistical modeling algorithms to compute dynamic models of the underlying mechanism that generates the observed data. I aim at mathematical representations that delineate the underlying mechanism by the functional, temporal, and statistical associations among the many variables in the observations.
Due to personal experiences and the quantitative research trend in life sciences, I am intrigued by computing applications in biological and medical sciences. This attachment started when I first participated in a research project on creating a single photon emission computer tomography machine in my junior year in college. Since then, I have designed a variety of algorithms and applied them in bioenergy, cancer research, microbiology, plant sciences, and neuroscience.