My research lies in cutting-edge methodology development in streams of Bayesian statistics, complex survey inference, missing data imputation, causal inference, and data confidentiality protection. I have extensive collaboration experiences with health services researchers and epidemiologists to improve healthcare and public health practice, and have been providing statistical support to solve sampling and analysis issues on … Read more
I am an assistant professor in Department of Industrial and Manufacturing Systems Engineering (IMSE) at the University of Michigan-Dearborn. Prior to joining UM-Dearborn, I was a research assistant professor and postdoctoral research scholar at Vanderbilt University. My research areas of interest are uncertainty quantification, Bayesian data analytics, big data analytics, machine learning, optimization under uncertainty, … Read more
My research is mainly concerned with theoretical and computational hydrodynamics, with applications in nonlinear ocean wave prediction and dynamics, wave-body interactions, and wave turbulence theory. I have incorporated the data science tools in my research, especially in the following two projects: 1. Quantification of statistics of extreme ship motions in irregular wave fields: In this … Read more
Carter’s research combines quantitative, theoretical, and field approaches to address challenging local to global wildlife conservation issues in the Anthropocene. His work includes projects on endangered species conservation in human-dominated areas of Nepal, post-war recovery of wildlife in Mozambique, human-wildlife coexistence in the American West, and the effects of artificial lights and human-made noise on … Read more
My research focuses on how environmental change, including climate, invasion and habitat destruction influences freshwater ecological communities across space and time. I am involved in a collaborative interdisciplinary project funded by a MIDAS Propelling Original Data Science (PODS) Grant: CHANGES: Collections, Heterogeneous data, And Next Generation Ecological Studies.We are developing protocols for integrating heterogeneous natural … Read more
Dr. Liu’s research lab aims to develop machine learning approaches for real-world bioinformatics and medical informatics problems. We believe that computational methods are essential in order to understand many of these molecular biology problems, including the dynamics of genome conformation and nuclear organization, gene regulation, cellular networks, and the genetic basis of human diseases. Accomplishments … Read more
I develop probabilistic and statistical models to analyze genetic and genomic data. We use these methods to study evolution, natural selection, and human history. Recently, I have been interested in applying these techniques to study viral epidemics (e.g., HIV) and cancer.
I study cybercrime using data-driven methods to analyze, characterize, and measure the infrastructure and modus operandi used by criminal activities on the Internet. In particular, I focus on collection, analysis, and semantic characterization of cyber threat intelligence that comes in many shapes and forms (e.g., natural language, network traffic, system audit logs). The ultimate goal … Read more
My research interests span topics in Statistical Machine Learning, Computational Social Science, Natural Language Processing, and Field/Digital Experiments. Substantively, I am interested in understanding the impact of internet technologies on users by empirically studying their interactions with such systems. The research questions that I study are of both predictive as well as causal nature and … Read more