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:

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About me

I am a 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.

Research interests

My research focuses on high-dimensional statistics and statistical machine learning, with particular interests in robust inference, adaptive learning, and change detection under complex nuisance variation. Below are some of my current research topics.

Publications and preprints [ by date | by area ]

  • Ma, T., Wang, T. and Samworth, R. J. (2026) Deep learning with missing data. J. Roy. Statist. Soc., Ser. B, to appear. [pdf].
  • Ma, T., Verchand, K., Berrett, T. B., Wang, T. and Samworth, R. J. (2026) Estimation beyond Missing (Completely) at Random. Ann. Statist., to appear. [pdf].
  • Wang, Y. and Wang, T. (2026+) Localized conformal model selection. Preprint, arxiv:2602.19284. [pdf].
  • Su, D., Chen, Y. and Wang, T. (2026+) Detecting change regions on spheres. Preprint, arxiv:2603.22071. [pdf]
  • Gao, F. and Wang, T. (2026) Detecting sparse change in regression coefficients in the presence of dense nuisance parameters. Inf. Inference, 15, iaag004. [pdf][slides] The accompanying R package charcoal is available from GitHub.
  • Wang, T. (2026) Invited discussion of Statistical exploration of the Manifold Hypothesis by Whiteley et al. J. Roy. Statist. Soc., Ser. B, to appear. [pdf]
  • Li, M., Chen, Y., Wang, T. and Yu, Y. (2026) Robust mean change point testing in high-dimensional data with heavy tails. IEEE Trans. Inform. Theory, 72, 571–609. [pdf][slides].
  • Ma, T., Wang, T. and Samworth, R. J. (2025+) Provable test-time adaptivity and distributional robustness of in-context learning. Preprint, arxiv:2510.23254. [pdf].
  • Yang, X., Azadkia, M. and Wang, T. (2025+) Coverage correlation: detecting singular dependencies between random variables. Preprint, arxiv:2508.06402. [pdf][slides]. Implementation code is available from CRAN and GitHub.
  • Wen, K., Wang, T. and Wang, Y. (2025) Residual permutation test for regression coefficient testing. Ann. Statist., 53, 724–748. [pdf][slides]
  • Wang, T., Dobriban, E., Gataric, M. and Samworth R. J. (2024) Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning. J. Amer. Statist. Assoc., 120, 395–407. [pdf].
  • Yang, X. and Wang, T. (2024) Multiple-output composite quantile regression through an optimal transport lens. COLT2024. [pdf][slides]
  • Li, J., Fearnhead, P., Fryzlewicz, P. and Wang, T. (2024) Automatic change-point detection in time series via deep learning. J. Roy. Statist. Soc., Ser. B (with discussions), 86, 273–285. [pdf]
  • Chen, Y., Wang, T. and Samworth R. J. (2024) Inference in high-dimensional online changepoint detection. J. Amer. Statist. Assoc., 119, 1461–1472. [pdf] Implementation code is available from GitHub.
  • Chen, C. Y.-H., Okhrin, Y. and Wang, T. (2024) Monitoring network changes in social media. J. Bus. Econ. Statist., 42, 391–406. [pdf]
  • 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, 83, 1–15. [pdf]
  • Wang, T. (2023) From inspiration to impact: Sir David Cox's influence on my research Havard Data Sci. Rev., 5, https://doi.org/10.1162/99608f92.971da134.
  • 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, 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.
  • 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]
  • Wang, L., Wang, T., Rushton, S. N., Parry, G., Dark, J. H. and Sheerin, N. S. (2020) The impact of severe acute kidney injury requiring renal replacement therapy on survival and renal function of heart transplant recipients — a UK cohort study. Transplant International, 33, 1650–1666. [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 ST449 and ST458 in Michaelmas and Lent terms 2024/2025. Please use LSE Moodle pages to access relevant course materials and use LSE Student Hub to book office hours.