CV

Education

• Ph.D candidate in Applied Mathematics, University of California - Davis, 2022 - 2026 (expected)
• M.S. in Computational and Applied Mathematics, University of Chicago, 2020 - 2022
• Visiting Student, Michigan State University, 2018 - 2019
• B.S. in Applied Mathematics, Xi’an Jiaotong-Liverpool University, 2016 - 2020

Skills

• Python
  • Pandas, Numpy and Pytorch
• C++
• AWS EC2

Publications

(Preprint) Accelerating Iterated Persistent Homology Computations with Warm Starts

(Preprint) Topology-Preserving Dimensionality Reduction via Interleaving Optimization

Research Experience(undergraduate)

• Cubical Homology and its application in robots’ motion planning, July 2019 - May 2020
  • Utilized cubical homology to compute the number of working cycles of n robots on some simple graphs.
  • Implemented Reduction algorithms for computing homology in Python and found some relationships between graphs and discretized configuration space.
• Functions of Perturbed Matrices, Febrarury 2019 - April 2019
  • Estimated the norm of f(A) − f(B) in terms of the norm of A − B and proved its boundedness on finite-dimensional spaces.
• Time Series Prediction of Sales by LSTM, July 2018 - August 2018
  • Combined ARIMA in time series with deep learning.
  • Predicted future phone cases sales via an LSTM model implemented in Python, whose hyper-parameters are optimized by grid search.
  • Utilized both standard deviation of sales and the parameters of autoregressive models as the fitting features to create hybrid models.
  • Evaluated the performance of hybrid models to select the models with the most effective prediction of future sales.