Tengyao Wang

Department of Statistics
London School of Economics and Political Science
Columbia House Room 7.05
69 Aldwych
London WC2B 4RR, United Kingdom

Email:

About me

I am an Associate Professor in Statistics in the Department of Statistics, London School of Economics. Previously, I was a lecturer at University College London, a research fellow at Cantab Capital Institute for the Mathematics of Information, University of Cambridge and a PhD student of Prof Richard Samworth.

I am an Associate Editor of Journal of Royal Statistical Society, Series B. I currently hold a three-year EPSRC New Investigator Award on 'Change-point analysis in high dimensions' [EP/T02772X/1].

Research interests

I am broadly interested in the area of high-dimensional statistics. My research aims to develop computationally efficient procedures for high-dimensional problems, while at the same time understanding the potential statistical limitations imposed by computational constraints. Below are some of my current research topics.

• Sparse signal detection in high-dimensional data
• Change-point detection and estimation problems
• Dimension reduction techniques
• Robust statistics
• Nonparametric statistical inference
• Applications, including bioinformatics, financial data analysis and statistical learning-assisted material discovery.

Publications and preprints [ by date | by area ]

• Cai, H. and Wang, T. (2023) Estimation of high-dimensional change-points under a group sparsity structure. Electron. J. Statist., 17, 858–894. [pdf]
• Wu, Q., Wu, J., Karim, M. K. A., Chen, X., Wang, T., Iwama, S., Carobbio, S., Keen, P., Vidal-Puig, A., Kotter, M. R. and Basset, A. (2023) Massively parallel characterization of CRISPR activation efficacy in human induced pluripotent stem cell and neurons. Mol. Cell, to appear.
• Wen, K., Wang, T. and Wang, Y. (2022+) Residual permutation test for high-dimensional regression coefficient testing. Preprint, arxiv:2211.16182. [pdf]
• Jie, L., Fearnhead, P., Fryzlewicz, P. and Wang, T. (2022+) Automatic change-point detection in time series via deep learning. Preprint, arxiv:2211.03860. [pdf]
• Gao, F. and Wang, T. (2022+) Sparse change detection in high-dimensional linear regression. Preprint, arxiv:2208.06326. [pdf][slides] The accompanying R package charcoal is available from GitHub.
• Zhu, Z., Wang, T. and Samworth, R. J. (2022) High-dimensional principal component analysis with heterogeneous missingness. J. Roy. Statist. Soc., Ser. B, 84, 2000–2031. [pdf][slides] The accompanying R package primePCA is available from CRAN.
• Gao, F. and Wang, T. (2022) Two-sample testing of high-dimensional linear regression coefficients via complementary sketching. Ann. Statist., 50, 2950–2972. [pdf][slides]
• Follain, B., Wang, T. and Samworth R. J. (2022) High-dimensional changepoint estimation with heterogeneous missingness. J. Roy. Statist. Soc., Ser. B, 84, 1023–1055. [pdf][slides] Implementation code of the MissInspect algorithm is available from GitHub.
• Chen, C. Y.-H., Okhrin, Y. and Wang, T. (2022) Monitoring network changes in social media. J. Bus. Econ. Statist., to appear. [pdf]
• Chen, Y., Wang, T. and Samworth, R. J. (2022) High-dimensional, multiscale online changepoint detection. J. Roy. Statist. Soc., Ser. B, 84, 234–266. [pdf][slides] The accompanying R package ocd is available from CRAN and GitHub.
• Chen, Y., Wang, T. and Samworth R. J. (2021+) Inference in high-dimensional online changepoint detection. Preprint, arxiv:2111.01640. [pdf] Implementation code is available from GitHub.
• Wang, G., Fearn, T., Wang, T. and Choy, K.-L. (2021) Machine learning approach for predicting the discharging capacities of doped lithium nickel-cobalt-manganese cathode materials in Li-ion batteries. ACS Cent. Sci., 7, 1551–1560. [pdf]
• Wang, G., Fearn, T., Wang, T. and Choy, K.-L. (2021) Insight gained from using machine learning techniques to predict the discharge capacities of doped spinel cathode materials for lithium‐ion batteries applications. Energy Technol., 9, 202100053. [pdf]
• Wu, Q., Suo, C., Brown, T., Wang, T., Teichmann, S. A. and Bassett, A. R. (2021) INSIGHT: a scalable isothermal NASBA-based platform for COVID-19 diagnosis. Sci. Adv., 7, eabe5054. [pdf]
• Janssen, B. V., van Laarhoven, S., Elshaer, M., Cai, H., Praseedom, R., Wang, T. and Liau, S.-S. (2020) A comprehensive classification of anatomical variants of the main biliary ducts. Br. J. Surg., 108, 458–462. [pdf]
• Gataric, M., Wang, T. and Samworth, R. J. (2020) Sparse principal component analysis via axis-aligned random projections. J. Roy. Statist. Soc., Ser. B, 82, 329–359. [pdf][slides] The accompanying R package SPCAvRP is available from CRAN.
• Mitchell P. D., Brown, R. Wang, T. [et al.] (2019) Multicentre study of physical abuse and limb fractures in young children in the East Anglia Region, UK. Arch. Dis. Child., 104, 956–961. [pdf]
• Han, Q., Wang, T., Chatterjee, S. and Samworth, R. J. (2019) Isotonic regression in general dimensions. Ann. Statist., 47, 2440–2471. [pdf][slides]
• Wang, T. and Samworth, R. J. (2018) High dimensional change point estimation via sparse projection. J. Roy. Statist. Soc., Ser. B, 80, 57–83. [pdf][slides] The accompanying R package InspectChangepoint is available from CRAN and GitHub.
• Feretis, M., Wang, T., Ghorani, E. [et al.] (2017) Development of a prognostic model that predicts survival following Whipple's resection for ampullary adenocarcinoma. Pancreas, 46, 1314–1321. [pdf]
• Wang, T. (2016) Spectral methods and computational trade-offs in high-dimensional statistical inference. Ph.D. thesis, University of Cambridge. [pdf]
• Wang, T., Berthet, Q. and Plan, Y. (2016) Average-case hardness of RIP certification. Adv. Neur. Inf. Proc. Syst., 29. [pdf]
• Wang, T., Berthet, Q. and Samworth, R. J. (2016) Statistical and computational trade-offs in estimation of sparse principal components. Ann. Statist., 44, 1896–1930. [pdf][slides]
• Yu, Y., Wang, T. and Samworth, R. J. (2015) A useful variant of the Davis–Kahan theorem for statisticians. Biometrika, 102, 315–323. [pdf][slides]
• Wang, T. (2013) Applications of Empirical Process Theory. Part III Essay, University of Cambridge. [pdf]
• Bubeck, S., Wang, T. and Viswanathan, N. (2013) Multiple identifications in multi-armed bandits. Proceedings of the 30th International Conference on Machine Learning. [pdf]
• Bolotnikov, V., Wang, T. and Weiss, J. M. (2012) Boundary angular derivatives of generalized schur functions. J. Aust. Math. Soc., 93, 203–224. [pdf]
• Wang, T. and Weiss, J. M. (2011) Nevanlinna–Pick interpolation by rational functions with a single pole inside the unit disk. J. Comput. Appl. Math., 236, 1497–1501. [pdf]

Statistical publications

Statistical software packages

• Gao, F. and Wang, T. (2022) charcoal: Novel changepoint detection algorithms in high-dimensional linear regression. R package. version 0.13 [GitHub].
• Chen, Y., Wang, T. and Samworth, R. J. (2020) ocd: High-Dimensional Multiscale Online Changepoint Detection. R package. version 1.1 [CRAN] [GitHub].
• Zhu, Z., Wang, T. and Samworth, R. J. (2019) primePCA: Projected Refinement for Imputation of Missing Entries in PCA. R package. version 1.2 [CRAN].
• Gataric, M., Wang, T. and Samworth, R. J. (2019) SPCAvRP: Sparse Principal Component Analysis via Random Projections. R package. version 0.4 [CRAN].
• Wang, T. and Samworth, R. J. (2018) InspectChangepoint: High-Dimensional Changepoint Estimation via Sparse Projection. R package. version 1.1 [CRAN] [GitHub].

Applied collaborations

Others

Teaching

I am teaching ST443 and ST436 in Michaelmas and Lent terms 2022/2023.