Biography
I am a PhD student in Department of Statistics at the University of British Columbia under the supervision of Matías Salibián-Barrera. My research interests primarily lie in the area of computational statistics, with a special focus on robust statistics and functional data analysis. Previously, I received an MA in Statistics from University of Michigan-Ann Arbor and BSc in Statistics from Renmin University of China.
Interests
- Computational Statistics
- Robust Statistics
- Functional Data Analysis
- Machine learning
Education
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PhD in Statistics, 2022 (Expected)
University of British Columbia
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MS in Statistics, 2015
University of Michigan-Ann Arbor
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BSc in Statistics, 2013
Renmin University of China
Experience
Research and teaching assistant
University of British Columbia
- Worked on PhD thesis titled “Boosting for regression problems with complex data” under the supervision of Matías Salibián-Barrera. Developed gradient boosting algorithms for inhomogeneous data, functional data, and a combination of both.
- Worked on the summer research project “High-dimensional regression with instrumental variables”. Extended two-stage regression with instrumental variables to high dimensional data and examined the performance of post-LASSO with SNP data for phenotype prediction.
- Teaching assistant:
— labs for STAT 200, STAT 251, STAT 344 in the Department of Statistics
— labs DSCI 571, DSCI 513 for Master of Data Science
— tutorials for CPSC 540 in the Department of Computer Science.
Research intern (Mitacs Accelerate program)
1QB Information Technologies, Inc
- Applied deep learning to screen molecules for finding drug candidates; fitted graph convolutional networks (referring to Deepchem) to predict docking scores with molecular data provided by Vancouver Prostate Center.
- Explored the use of active learning to select the molecules to be docked for regression and classification tasks.
Short Term Consulting Services (STCS)
University of British Columbia
- Served on the managing team from Sep 2017. Managed consulting requests; organized meetings to discuss incoming projects.
- Selected projects: Medical students’ health research (Prof. Erica Frank, UBC). Modelling beggiatoa population at aquaculture sites in British Columbia (Mainstream Biological Consulting Inc)
Data scientist intern
Ford Motor Credit Company
- Developed a two-way survival model to predict the renewal rate of car lease contracts, which took into account the seasonal effect, lifetime effect, and time-varying covariates.
Publications
(2021).
Tree-based boosting with functional data.
arXiv:2109.02989.
(2021).
Robust boosting for regression problems.
Computational Statistics & Data Analysis 153 (2021).
(2019).
Accelerating boosting via accelerated greedy coordinate descent.
In Optimization for Machine Learning workshop (OPT 2019).
In progress
Xiaomeng Ju, Matías Salibián-Barrera. RTFBoost: robust tree-based functional boosting (Expected 2022).
- Initial results were presented at International Conference on Robust Statistics, 2021. R package is avaiable here.