|Position||Lecturer in Statistical Data Science|
|Phone (external)||+44(0)20 7679 1862|
|Themes||General Theory and Methodology, Multivariate and High Dimensional Data|
- 2018–now: Lecturer in Statistical Data Science, University College London
- 2016–2018: Postdoc, University of Cambridge
- 2013–2016: PhD, University of Cambridge, supervisor Richard J. Samworth
Tengyao is broadly interested in the area of high-dimensional statistics. His 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.
- Chen, Y., Wang, T. and Samworth, R. J. (2020+) High-dimensional, multiscale online changepoint detection. Preprint. arxiv:2003.03668.
- 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.
- Han, Q., Wang, T., Chatterjee, S. and Samworth, R. J. (2019) Isotonic regression in general dimensions. Ann. Statist., 47, 2440–2471.
- Wang, T., Samworth, R. J. (2018) High-dimensional change point estimation via sparse projection. J. Roy. Statist. Soc., Ser. B, 80, 57–83.
- 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.
- Yu, Y., Wang, T. and Samworth, R. J. (2015) A useful variant of the Davis–Kahan theorem for statisticians. Biometrika, 102, 315–323.