Ziyan (Cecilia) X.

Ziyan (Cecilia) X. is a data analyst at Revvo AI. Ziyan previously worked as a data science consultant at LivaNova from February to May 2022. In this role, they used public data sources to estimate the number of drug-resistant epilepsy (DRE) patients by both hospital and zip code. Prior to that, Ziyan was a teaching assistant at Carnegie Mellon University from August 2021 to May 2022, where they led labs, held office hours, graded exams and homework for 36-202: Methods for Statistics and Data Science. Ziyan also has experience as an undergraduate researcher, having worked at the University of California, San Francisco from August 2020 to March 2021, where they built activity recognition models to identify different types of activity and recognize relative intensity. In this role, they conducted time series segmentation using a peak detection algorithm, achieved clustering segmented time series into different intensity levels using dynamic time wrapping and k-means clustering, developed scalable tools using Javascript for efficient annotations of activity graphs, and designed R Shiny web apps to realize interactive visualization of data and analysis result.

Ziyan (Cecilia) X.'s educational career includes a Master's degree in Statistics and Data Science from Carnegie Mellon University, a Bachelor's degree in Statistics from Central China Normal University, and an exchange student in Statistics at University of California, Berkeley. Ziyan also has certification from the CITI Program in Social and Behavioral Research - Basic/Refresher.