I recently joined the PPG project and am enthusiastic about contributing to the project.  In my role, I specialize in analyzing data from adipocytes – the cells in this tissue. A significant part of my work involves employing a range of tools and methods, including advanced machine learning algorithms, to decipher how these cells interact with each other. This process includes using various data classifiers to detect and interpret patterns in the perivascular adipose tissue (PVAT) data. My role represents a fusion of biology and data science. Through computational techniques and machine learning, I delve into the complexities of cell-to-cell communication within adipose tissue. This approach is vital in deepening our knowledge of how these tissues operate and their interactions with other bodily systems.

Ms. Leah Terrian, PhD student in Biomedical Engineering and a graduate research assistant in the Bhattacharya lab, has a background in mathematics and computational modeling. She will take part in both single cell RNA-Seq data generation in collaboration with PPG Projects, and computational analysis of the data. Specifically, she will work on prediction of health to disease transitions in PVAT cell types using pseudo-time analysis of single-cell data with critical state transition theory.