Researcher Phd Candidate

• Sep 2016 - Present

Researcher Assistant

• May 2017 - Jun 2017

Project Title: Optimal Control Under Uncertainty Supervisor: Prof. Georg Stadler– Derived optimality conditions of elliptic linear-quadratic optimal control problem– Derived Generalized Newton algorithm for the optimal control problem– Design the code for nonlinear situation

Applied Scientist Intern

• May 2022 - Aug 2022

- Extracted, transformed, and loaded historical sales data w/o promotion events and utilized a hierarchical time series structure. - Built a demand forecast model on the open-to-buy (OTB) level using Fbprohet & DeepAR estimators with 50% accuracy improvement compared with the original rule-based model. - Extracted, transformed, and loaded historical sales data w/o promotion events and utilized a hierarchical time series structure. - Built a demand forecast model on the open-to-buy (OTB) level using Fbprohet & DeepAR estimators with 50% accuracy improvement compared with the original rule-based model.

Summer Research Intern

• Jun 2019 - Aug 2019

Team: Baidu Cognitive Research Lab Mentor: Dr. Ping Li –Solved PDEs by the neural networks and learned PDE-constrained inverse problems using these surrogate models. –Took research on the quantum Chernoff bound and proved the best lower bound. Team: Baidu Cognitive Research Lab Mentor: Dr. Ping Li –Solved PDEs by the neural networks and learned PDE-constrained inverse problems using these surrogate models. –Took research on the quantum Chernoff bound and proved the best lower bound.

Summer Research Intern

• May 2018 - Aug 2018

Team: Mathematics & Computational Science Division Mentor: Dr. Wendy Di – Designed parallel computation codes with PETSC on Tomography – Derived different regularizers for Tomography problem with Center of Rotation Team: Mathematics & Computational Science Division Mentor: Dr. Wendy Di – Designed parallel computation codes with PETSC on Tomography – Derived different regularizers for Tomography problem with Center of Rotation

Summer Research Intern

• Jul 2017 - Sep 2017

Team: Computational Sciences & Mathematics Mentor: Dr. Xiu Yang – Proved coefficients of n-th order ridge function can be approximated by n-sparsity vector under reasonable rotation matrix with O(\sqrt(n)) error – Design the Recurrent Neural Network(RNN) code to learn Wave Equation Team: Computational Sciences & Mathematics Mentor: Dr. Xiu Yang – Proved coefficients of n-th order ridge function can be approximated by n-sparsity vector under reasonable rotation matrix with O(\sqrt(n)) error – Design the Recurrent Neural Network(RNN) code to learn Wave Equation

Research Assistant

• Mar 2016 - Jun 2016

Project Title: Infinite Property of Local Structure of Compressive Sensing Supervisor: Prof. Ben Adcock – Expanded the local structure of Compressive Sensing to infinite situation – Calculated the sharpness of RIP of uniform recovery Project Title: Infinite Property of Local Structure of Compressive Sensing Supervisor: Prof. Ben Adcock – Expanded the local structure of Compressive Sensing to infinite situation – Calculated the sharpness of RIP of uniform recovery

Research Assistant

• Jul 2015 - Oct 2015

Project Title: Nonuniform Sampling and Efficient Compressed Sensing MRI Supervisor: Prof. Benjamin James Stevens Adcock(SFU) – Realized Split Bregman method with signals’ and images’ denosing by Matlab – Compiled the Matlab program of Split Bregman method on MRI – Found the upper bound of the error between theoretical measurements and practical mea- surements – Proved the RIP in levels can be controlled by some constant in the constraints (Open Prob- lem). Project Title: Nonuniform Sampling and Efficient Compressed Sensing MRI Supervisor: Prof. Benjamin James Stevens Adcock(SFU) – Realized Split Bregman method with signals’ and images’ denosing by Matlab – Compiled the Matlab program of Split Bregman method on MRI – Found the upper bound of the error between theoretical measurements and practical mea- surements – Proved the RIP in levels can be controlled by some constant in the constraints (Open Prob- lem).