Jeffrey Liu

Jeffrey Liu is a software engineer and research assistant who is currently working at the University of Southern California. Jeffrey previously worked as a research assistant at UC Santa Barbara from April 2020 to October 2021, where they designed and implemented a Covid-19 spread simulation and prediction system. Jeffrey also simulated the spread of the virus and analyzed data using the SEIR-HCD model, the SARIMA model, and deep learning techniques. In addition, they implemented the Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) based on a paper using Python and Pytorch. Jeffrey also implemented the Transformer using Python and TensorFlow. Jeffrey applied the Transformer and DA-RNN models for time-series prediction and achieved 90% accuracy on Covid-19 time-series forecasting for different districts in the U.S. Jeffrey also worked on a series of deep learning and statistical model analyses for Covid-19 time series forecasting with the specialization in Inter-Series Attention Model and published research paper in ICDM Workshop 2021. Jeffrey also built a visualization tool to visualize the Covid-19 time series forecasting results and model analysis results. Prior to their work at UC Santa Barbara, Jeffrey interned as a software engineer at Stellar Cyber from July 2019 to September 2019.

Jeffrey Liu has a master's degree in computer science from the University of Southern California and a bachelor's degree in computer science from UC Santa Barbara.