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Lynn
Lin
Assistant Professor
Lynn Lin

Biography

Lynn Lin is an Assistant Professor of Statistics at Penn State.

She completed her Ph.D. in the Department of Statistical Science at Duke University in July 2012.

Lin works on developing statistical, machine learning methods and computational tools for large-scale and high-dimensional data exploration and analysis to facilitate efficient data-driven scientific discoveries.

Before coming to Penn State, she was a postdoctoral fellow with Dr. Raphael Gottardo at the Fred Hutchinson Cancer Research Center in Seattle, Washington.

 

Honors and Awards

Mitchell Prize by the International Society of Bayesian Analysis, 2016

 

Publications

  • Li, W., Lin, L., Malhotra, R., Yang, L., Acharya, R., & Poss, M. (in press). A computational framework to assess genome-wide distribution of polymorphic human endogenous retrovirus-K in human populations. PLoS Computational Biology.

  • Lin, L., & Fong, D. K. H. (2019). Bayesian multidimensional scaling procedure with variable selection. Computational Statistics & Data Analysis, 129, 1-13.

  • Guo, W., Huang, S., Tao, Y., Xing, X., & Lin, L. (2018). Explaining deep learning models -- A Bayesian non-parametric approach. In Advances in Neural Information Processing Systems (pp. 4519-4529).

  • Lin, L., & Li, J. (2017). Clustering with hidden Markov model on variable blocks. The Journal of Machine Learning Research, 18(1), 3913-3961.

  • Shah, J.A., Musvosvi, M., Shey, M., Horne, D.J., Wells, R.D., Peterson, G.J., Cox, J.S., Daya, M., Hoal, E.G., Lin, L., Gottardo, R., Hanekom, W.A., Scriba, T.J., Hatherill, M., & Hawn, T.R. (2017). A functional toll-interacting protein variant is associated with Bacillus calmette-guérin–specific immune responses and tuberculosis. American journal of respiratory and critical care medicine, 196(4), 502-511.

  • Seshadri, C., Lin, L., Scriba, T.J., Peterson, G., Freidrich, D., Frahm, N., DeRosa, S.C., Moody, D.B., Prandi, J., Gilleron, M., Mahomed, H., Jiang, W., Finak, G., Hanekom, W.A., Gottardo, R., McElrath, M.J., & Hawn, T.R. (2015). T cell responses against mycobacterial lipids and proteins are poorly correlated in South African adolescents. The Journal of Immunology, 195(10), 4595-4603.

  • Lin, L., Finak, G., Ushey, K., Seshadri, C., Hawn, T.R., Frahm, N., Scriba, T.J., Mahomed, H., Hanekom, W., Bart, P.A., Pantaleo, G., Tomaras, G.D., Rerks-Ngarm, S., Kaewkungwal, J., Nitayaphan, S., Pitisuttithum, P., Michael, N.L., Kim, J.H., Robb, M.L., O’Connell, R.J., Karasavvas, N., Gilbert, P., DeRosa, S., McElrath, M.J., & Gottardo, R. (2015). COMPASS identifies T-cell subsets correlated with clinical outcomes. Nature Biotechnology, 33(6), 610. (Mitchell Prize 2016)

  • Lin, L., Frelinger, J., Jiang, M., Finak, G., Bart, P.A., Pantaleo, G., McElrath, M.J., DeRose, S.C., & Gottardo, R. (2015). Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data. Cytometry Part A, 87(7), 675-682.

  • Lin, L., Chan, C., & West, M. (2015). Discriminative variable subsets in Bayesian classification with mixture models, with application in flow cytometry studies. Biostatistics, 17(1), 40-53.

  • Lin, L., Chan, C., Hadrup, S. R., Froesig, T. M., Wang, Q., & West, M. (2013). Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies. Statistical applications in genetics and molecular biology, 12(3), 309-331.

  • Cron, A., Gouttefangeas, C., Frelinger, J., Lin, L., Singh, S.K., Britten, C.M., Welters, M.J.P., van der Burg, S.H., West, M., & Chan, C. (2013). Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples. PLoS computational biology, 9(7), e1003130.

 

Teaching

STAT 461 - Analysis of Variance, Spring 2016, Fall 2018

STAT 501 - Regression Methods, Spring 2016, 2017

STAT 597 - Bayesian Studies, Spring 2017, Fall 2018

STAT 500 - Applied Statistics, Fall 2017

STAT 200 - Elementary Statistics, Fall 2017