About
I am a postdoctoral associate in the Stites Laboratory at Yale School of Medicine. My research combines computational biology, genomics, and experimental approaches to understand cellular signaling and gene regulation. I am particularly interested in cancer and rare diseases, with the goal of translating biological discoveries into improved therapies.
Research
Overview
My research focuses on understanding how molecular signaling and gene regulatory networks control cellular behavior in health and disease. I am particularly interested in integrating computational biology, genomics, quantitative modeling, and experimental approaches to uncover the mechanisms underlying cancer and rare diseases.
As a Postdoctoral Associate in the Stites Laboratory at Yale School of Medicine, I am studying signaling pathways that drive cancer initiation and progression. My current work combines wet-lab experimentation with computational analyses to investigate how alterations in signaling networks influence cellular decision-making and contribute to disease. By integrating experimental data with quantitative models, I aim to better understand the principles governing signal transduction and identify mechanisms that may inform future therapeutic strategies.
I earned my Ph.D. in Biology from Wesleyan University, where my research focused on transcriptional regulation using the budding yeast Saccharomyces cerevisiae as a model system. I investigated how changes in the dosage of the transcription factor Rap1 influence genome-wide gene expression under diverse environmental conditions. My work combined RNA sequencing, functional genomics, bioinformatics, statistical modeling, and gene network analyses to characterize transcriptional responses across multiple environmental perturbations. This research provided insights into the dynamic relationship between transcription factor abundance and cellular adaptation.
Throughout my graduate training, I developed expertise in high-throughput sequencing analysis, transcriptomics, statistical computing, machine learning, and biological data visualization. I have worked extensively with computational tools for RNA-seq analysis, differential gene expression, functional enrichment, and systems-level analyses, while also collaborating closely with experimental biologists to connect computational findings with biological mechanisms.
My long-term goal is to bridge computational and experimental biology to develop predictive models of cellular behavior. I am particularly interested in understanding how signaling and gene regulatory networks become dysregulated in cancer and rare diseases, and how quantitative approaches can be used to identify novel therapeutic targets and advance precision medicine.