Xinghao Qiao

Associate Professor of Statistics

Office: Columbia House, Room 5.15

Mail: Department of Statistics
London School of Economics
Houghton Street
London, WC2A 2AE
United Kingdom

Phone: +44 (0)20 7955 6880

Email: x.qiao@lse.ac.uk

Education

PhD in Business Statistics, Marshall School of Bussiness, University of Southern California, Los Angeles, CA, USA.
M.S. in Statistics, University of Chicago, Chicago, IL, USA.
B.S. in Mathematics and Physics, Academic Talent Program, Tsinghua University, Beijing, China.

Teaching

ST311 Artificial Intelligence (Deep Learning and Reinforcement Learning)

ST443 Machine Learning and Data Mining (Statistical Learning)

ST498 MSc in Data Science Capstone Project

ST510 Foundations of Machine Learning

ST300 Regression and Generalised Linear Models

Research interests

Functional data analysis: high-dimensional functional data, partially observed functional data, non-Euclidean functional data.
Complex time series analysis: functional time series, high-dimensional time series, factor models, spectral analysis.
High-dimensional statistics: covariance and graphical models, concentration inequalities, simultaneous testing, .
Bayesian nonparametrics: variational inference, graph neural network.
High-dimensional econometrics.
Reinforcement learning.

Some research papers

Dynamic Covariance Matrix Estimation via Locally/Globally Adaptive Thresholdings (with S. Guo). Working paper, 2023.
Factor-Assisted Clustering and Covariance Matrix Estimation for High-Dimensional Time Series (with D. Li and C. Yu). Working paper, 2023.
Testing for White Noise in High-Dimensional Functional Time Series (with J. Chang, Q. Jiang and L. Yang). Working paper, 2023.
Inference for High-Dimensional Error-in-Variables Vector Autoregression (with C. Chen). Working paper, 2023.
Empirical Likelihood for Nonparametric Functions with Applications to Regression Discontinuity Designs: Robust Bias-Correction and Wilks Phenomenon (with S. Guo and Y. Hong). Preprint, 2023.
Large-Scale Multiple Testing of Cross-Covariance Functions with Applications to Functional Network Models (with Q. Fang and Q. Jiang). Preprint, 2023.
Factor Models for Matrix-Variate Functional and Longitudinal Time Series in High Dimensions (with D. Li and Z. Wang). Preprint, 2023.
Knockoffs Selection in Hilbert Spaces with Applications to High-Dimensional Functional Data Analysis (with Q. Li and M. Long). Preprint, 2023.
DF2M: An Explainable Deep Bayesian Nonparametric Model for High-Dimensional Functional Time Series (with Y. Liu, Y. Pei and L. Wang). Preprint, 2023.
Factor-Guided Estimation of Large Covariance Matrix Function with Conditional Functional Sparsity (with D. Li and Z. Wang). Preprint, 2023.
From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective (with S. Guo, D. Li and Y. Wang). Preprint, 2023.
On the Modeling and Prediction of High-Dimensional Functional Time Series (with J. Chang, Q. Fang and Q. Yao). Under revision, 2023.
Factor Modelling for High-Dimensional Functional Time Series (with S. Guo and Q. Wang). Under revision, 2023.
EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model (with Y. Liu, L. Wang, and J. Lam). The 26th International Conference on Artificial Intelligence and Statistics, 2023.
Adaptive Functional Thresholding for Sparse Covariance Function Estimation in High Dimensions. (with S. Guo and Q. Fang). Journal of the American Statistical Association, 2023, in press.
An Autocovariance-based Learning Framework for High-Dimensional Functional Time Series (with J. Chang, C. Chen and Q. Yao). Journal of Econometrics, 2023, in press.
Finite Sample Theory for High-Dimensional Functional/Scalar Time Series with Applications (with Q. Fang and S. Guo). Electronic Journal of Statistics, 2022, 16, 527-591.
CATVI: Conditional and Adaptively Truncated Variational Inference for Bayesian Nonparametrics (with Y. Liu and J. Lam). The 25th International Conference on Artificial Intelligence and Statistics, 2022. Python code to implement the proposed method.
On Consistency and Sparsity for High-Dimensional Functional Time Series with Application to Autoregressions (with S. Guo). Bernoulli, 2023, 29, 451-472. Supplementary material is available here.
Functional Linear Regression: Dependence and Error Contamination (with C. Chen and S. Guo). Journal of Business and Economic Statistics, 2022, 40, 444-457.
Doubly Functional Graphical Models in High Dimensions (with C. Qian, G. James and S. Guo). Biometrika, 2020, 107: 415-431.
Homogeneity Pursuit in Single Index Models based Panel Data Analysis (with H. Lian and W. Zhang). Journal of Business and Economic Statistics, 2021, 39, 386-401.
Functional Graphical Models (with S. Guo and G. James). Journal of the American Statistical Association, 2019, 114, 211-222.
Index Models for Sparsely Sampled Functional Data (with P. Radchenko and G. James). Journal of the American Statistical Association, 2015, 110, 824-836.
Invited discussion of "Clustering Random Curves Under Spatial Dependence" (with G. James and W. Sun). Technometrics, 2012, 54, 123-126.

PhD students

Cheng Chen: "Autocovariance-based Statistical Inference for High-Dimensional Function/Scalar Time Series'', Research Offier at at SWUFE.
Yirui Liu: "Three Essays in Bayesian Nonparametric Machine Learning'', Quantitative Researcher at J. P. Morgan.
Qin Fang: "High-Dimensional Functional Data/Time Series: Finite-Sample Theory, Adaptive Functional Thresholding and Prediction'', Research Offier at LSE, then Assistant Professor at the University of Sydney Business School.

Travel plan

With Dong Li (Tsinghua), George Michailidis (UCLA) and Qiwei Yao (LSE), co-organize Tsinghua Sanya International Workshop on "Complex Time Series Analysis: High-Dimensionality, Change-Points, Forecasting and Causality", Sanya, China, 3-7 January, 2024.