## 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.