About me
I am a Lecturer in Statistical Data Science in the Department of Statistical Science, University College London. Previously, I was a research fellow at Cantab Capital Institute for the Mathematics of Information, University of Cambridge and a PhD student of Prof Richard Samworth.
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 statistical procedures in missing data settings
- Nonparametric statistical inference
- Applications, including medical statistics, financial data analysis and statistical learning-assisted material discovery.
Publications and preprints [ by date | by area ]
- Follain, B., Wang, T. and Samworth R. J. (2021+) High-dimensional changepoint estimation with heterogeneous missingness. Preprint, arxiv:2108.01525. [pdf] Implementation code of the MissInspect algorithm is available from GitHub.
- Cai, H. and Wang, T. (2021+) Estimation of high-dimensional change-points under a group sparsity structure. Preprint, arxiv:2107.08724. [pdf]
- 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., to appear. [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]
- Chen, Y., Wang, T. and Samworth, R. J. (2021) High-dimensional, multiscale online changepoint detection. J. Roy. Statist. Soc., Ser. B., to appear. [pdf][slides] The accompanying R package ocd is available from CRAN and GitHub.
- 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]
- Gao, F. and Wang, T. (2020+) Two-sample testing of high-dimensional linear regression coefficients via complementary sketching. Preprint, arxiv:2011.13624. [pdf][slides]
- Chen, C. Y.-H., Okhrin, Y. and Wang, T. (2020+) Monitoring network changes in social media. Preprint. [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] The accompanying R package SPCAvRP is available from CRAN.
- Zhu, Z., Wang, T. and Samworth, R. J. (2019+) High-dimensional principal component analysis with heterogeneous missingness. Preprint. arxiv:1906.12125. [pdf][slides] The accompanying R package primePCA 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. Advances in Neural Information Processing Systems, 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
- 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 STAT0004 (Introduction to Practical Statistics) in Spring 2021. [Moodle link]