CONTACT

• Assistant Professor
• Department of Statistics and Applied Probability
• National University of Singapore (NUS)
• Email: statsll@nus.edu.sg
• Office: S16-07-112
• Tel: 65168473
• ORCID: 0000-0002-5002-847X
• Web of Science Researcher ID: P-6292-2019
• Scopus Author ID: 55763797400
• Google Scholar
• Dimensions

RESEARCH INTERESTS

• Bayesian inference
• Variational approximation
• Hierarchical models
• Mixture models
• Stochastic optimization
• Network modeling

EDUCATION

• PhD (2013), Statistics & Data Science, National University of Singapore (Advsior: David J. Nott)
• Postgraduate Diploma in Education (2004), Nanyang Technological University
• BSc (2003), Mathematics, National University of Singapore

ACADEMIC POSITIONS HELD

• Assistant Professor (2017- present), Statistics & Data Science, National University of Singapore
• Instructor (2014-2017), Statistics & Data Science, National University of Singapore
• Visiting Fellow (2015-2016), Statistics, University College London (Advisor: Maria De Iorio)
• Postdoctoral Research Scientist (2014-2015), Statistics, Columbia University (Advisor: Tian Zheng)
• Research Assistant/Fellow (2013-2014), Statistics & Data Science, National University of Singapore

RESEARCH GRANTS

• Ministry of Education Academic Research Fund Tier 1 Start-Up Grant (2017-2021)
• Ministry of Education Academic Research Grant Tier 2 Grant (2023-2026)

PUBLICATIONS

1. Tan, L. S. L. and Chen, A. (2024). Variational inference for a subclass of closed skew normals. Journal of Computational and Graphical Statistics. https://doi.org/10.1080/10618600.2024.2402278. (arXiv:2306.02813)
2. Tan, L. S. L. (2023). Efficient data augmentation techniques for some classes of state space models. Statistical Science, 38 (2), 240-261.  https://doi.org/10.1214/22-STS867 (arXiv:1712.08887)
3. Tan, L. S. L. (2021). Use of model reparametrization to improve variational Bayes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 83 (1), 30-57. https://doi.org/10.1111/rssb.12399  (arXiv:1805.07267)
4. Tan, L. S. L. and Friel, N. (2020). Bayesian variational inference for exponential random graph models. Journal of Computational and Graphical Statistics, 29 (4), 910-928. https://doi.org/10.1080/10618600.2020.1740714  (arXiv:1811.04249)
5. Tan, L. S. L., Bhaskaran, A. and Nott, D. J. (2020). Conditionally structured Gaussian variational approximation with importance weights. Statistics and Computing, 30, 1255–1272. https://doi.org/10.1007/s11222-020-09944-8  (arXiv:1904.09591)
6. Tan, L. S. L. (2019). Explicit inverse of tridiagonal matrix with applications in autoregressive modeling. IMA Journal of Applied Mathematics, 84 (4), 679-695. https://doi.org/10.1093/imamat/hxz010  (arXiv:1803.04516)
7. Tan, L. S. L. and De Iorio, M. (2019). Dynamic degree-corrected blockmodels for social networks: a nonparametric approach. Statistical Modelling, 19 (4), 386-411. https://doi.org/10.1177/1471082X18770760  (arXiv:1705.09088)
8. Tan, L. S. L. and Nott, D. J. (2018). Gaussian variational approximation with sparse precision matrices. Statistics and Computing, 28, 259-275. https://doi.org/10.1007/s11222-017-9729-7  (arXiv:1605.05622)
9. Tan, L. S. L., Jasra, A., De Iorio, M. and Ebbels, T. M. D. (2017). Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks. Annals of Applied Statistics, 11 (4), 2222–2251. https://doi.org/10.1214/17-AOAS1076  (arXiv:1603.06358, pdf)
10. Tan, L. S. L. (2017). Stochastic variational inference for large-scale discrete choice models with adaptive batch sizes. Statistics and Computing, 27, 237–257. https://doi.org/10.1007/s11222-015-9618-x  (arXiv:1405.5623 )
11. Mensah, D. K., Nott, D. J., Tan, L. S. L. and Marshall, L. (2016). Functional longitudinal modeling with covariate dependent smoothness. Electronic Journal of Statistics, 10 (1), 527–549. https://doi.org/10.1214/16-EJS1113
12. Tan, L. S. L., Chan, A. H. and Zheng, T. (2016). Topic-adjusted visibility metric for scientific articles. Annals of Applied Statistics, 10 (1), 1–31. https://doi.org/10.1214/15-AOAS887  (arXiv:1502.07190)
13. Tan, L. S. L., Ong, V. M. H., Nott, D. J. and Jasra, A. (2016). Variational inference for sparse spectrum Gaussian process regression. Statistics and Computing, 26, 1243–1261. https://doi.org/10.1007/s11222-015-9600-7  (arXiv:1306.1999)
14. Tan, L. S. L. and Nott, D. J. (2014). A stochastic variational framework for fitting and diagnosing generalized linear mixed models. Bayesian Analysis, 9 (4), 963–1004. https://doi.org/10.1214/14-BA885  (arXiv:1208.4949 )
15. Tan, S. L. and Nott, D. J. (2014). Variational approximation for mixtures of linear mixed models. Journal of Computational and Graphical Statistics, 23 (2), 564–585. https://doi.org/10.1080/10618600.2012.761138  (arXiv:1112.4675)
16. Tan, L. S. L. and Nott, D. J. (2013). Variational inference for generalized linear mixed models using partially non-centered parametrizations. Statistical Science, 28 (2), 168–188. https://doi.org/10.1214/13-STS418  (arXiv:1205.3906)
17. Nott, D. J., Tan, S. L., Villani, M. and Kohn, R. (2012). Regression density estimation with variational methods and stochastic approximation. Journal of Computational and Graphical Statistics, 21 (3), 797–820. https://doi.org/10.1080/10618600.2012.679897

 MANUSCRIPTS

1. Tan, L. S. L. (2022). Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation. (arXiv:2109.00375)