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, spectral analysis.
High-dimensional statistics: covariance and graphical models, concentration inequalities, simultaneous testing, .
Bayesian nonparametrics: conditional variational inference, graph and neural network.
Machine learning and deep learning with applications to business, economics and neuroimaging analysis.

Some research papers

Testing for White Noise in High-Dimensional Functional Time Series (with J. Chang, Q. Jiang and L. Yang). Working paper, 2023.
Large-Scale Multiple Testing of Cross-Covariance Functions with Applications to Functional Network Models (with Q. Fang and Q. Jiang). Working paper, 2023.
Inference for High-Dimensional Error-in-Variables Vector Autoregression (with C. Chen). Working paper, 2023.
Knockoffs Selection in Hilbert Spaces with Applications to High-Dimensional Functional Data Analysis (with Q. Li and M. Long). Working paper, 2023.
Deep Functional Factor Models (with Y. Liu). Preprint, 2023.
Factor-Guided Estimation of Large Covariance Matrix Function with 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.
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, accepted.
On the Modeling and Prediction of High-Dimensional Functional Time Series (with J. Chang, Q. Fang and Q. Yao). Preprint, 2022.
Adaptive Functional Thresholding for Sparse Covariance Function Estimation in High Dimensions. (with S. Guo and Q. Fang). Journal of the American Statistical Association, 2022, accepted.
Factor Modelling for High-Dimensional Functional Time Series. (with S. Guo and Q. Wang). Preprint, 2022.
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. Supplimentary 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'', Postdoc 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 Lecturer (Assistant Professor) at the University of Sydney Business School.
Ziqing Ho: ongoing.