Data Science at eBay Search

As an eBay Search Scientist, I led initiatives to enhance search performance and user engagement. Integrating robust metrics into the search ranking algorithm, I boosted converted search sessions significantly.

Developing various search metrics logic, I empowered data engineers globally to streamline processes effectively. Spearheading efficiency initiatives, I reduced user scrolling time, enhancing overall experience.

Additionally, my insights guided product managers in informed decision-making for product launches and improvements. My role focused on delivering measurable enhancements in search performance, user experience, and business outcomes through data-driven insights and strategic initiatives.

Fellow at Insight Data Science

I had the honor of being chosen for the Data Science fellowship at Insight Data Science in September 2019. This invaluable experience not only immersed me in cutting-edge industry technologies but also provided me with the opportunity to connect and learn from distinguished alumni, many of whom are seasoned software developers. This exposure further fueled my passion for coding and inspired me to explore software engineering later.

Ph.D. Statistics

I earned my Ph.D. in Statistics from the Statistical Learning and Data Mining Lab at the Department of Statistics, Pennsylvania State University. Over a rewarding six-year journey, I explored diverse research topics, focusing on model-based clustering of massive dynamic, weighted and bipartite networks under the guidance of Dr. Lingzhou Xue. Much of my research was interdisciplinary, spanning GeoSciences, Genomics, and Business.

During my Ph.D., my primary research interests included large-scale network analysis, time-evolving community detection in dynamic networks, non-parametric clustering of weighted networks, and two-mode segmentation in bipartite networks. I also became interested into statistical machine learning, variational inference, stochastic optimization, Bayesian analysis, MCMC algorithms, parallel computing, and visualization.

To facilitate the utilization of the statistical network models I developed, I integrated them into an R package called ‘ergmclust.’ Feel free to explore my GitHub page, for a detailed portfolio of all research projects and applications I built during my Ph.D.

Collaborative projects

In collaboration with various fields, I worked on projects such as network applications in sparse spatiotemporal environmental big data, building polluter detection tools such as GeoNet using R Shiny and Leaflet, constrained penalized regression models for big chromosome matrices (Hi-C Data), and building a recommender system via two-mode segmentation for consumer-product review networks.