I am a doctoral candidate in clinical psychology at The Pennsylvania State University. My research primarily focuses on the comorbidity between anxiety and depression, and statistics. I am interested in explaining the causal relationship between anxiety and depression, using anxiety as a risk factor for later depression. My research spans across diverse methodologies including fMRI, ecological momentary assessments (e.g. data collection using smartphones), self-report questionnaires, and clinician diagnostic interviews. I and computer scientists have recently developed a smartphone monitoring application entitled Mood Triggers, and helps users to find health behavior, and psychologically relevant triggers of their anxiety and depression in their daily lives. My interests also include developing longitudinal statistical methods, including the Differential Time-Varying Effect Model (DTVEM) which allows one to determine when processes optimally predict one another over time (discover and model optimal time lags). My experience in statistics is within the general linear, generalized linear model, and non-parametric models. I am fluent in hierarchical linear modeling (also known as multilevel modeling and latent growth curve modeling), confirmatory and exploratory factor analysis, longitudinal factor models, structural equation modeling, multiple imputation, data manipulation, linear and nonlinear regression, ANOVA, ANCOVA, MANOVA, spectral analysis, meta-analyses, and dynamical systems modeling. I prefer Bayesian statistical interpretation, as they provide more robust and consistent answers than found in p-values (or confidence intervals for that matter). My preferred statistical programs of choice are R, MPlus, and SAS, but I also use JAGS, LISREL, SPSS, and Minitab, and Python.