I love the natural world and its biodiversity, which I study from a statistical perspective. I am particularly interested in theory and methods for phylogenetic inference and microbial ecology. I have the fabulous committee of John Bunge, Sid Resnick and Louis Billera.
- Confidence sets: I just solved an open problem about construction of confidence sets for trees! (Software)
- Consistency: An alternative method of proof for a popular question in the field
- Uncertainty and visualization: Phylogenetic trees are estimated, with extremely high-dimensional uncertainty that is rarely discussed (Software)
- Species richness: with and without accounting for the bioinformatics
- Community modelling: Any statistic calculated from your OTU table needs an associated statement about error bars (Update Nov '16: paper just accepted to JRSS-C!)
- Admissibility: rarefaction belies many biodiversity models
- Software via git and CRAN
There just aren't enough statisticians out there for us to shirk collaborations. Over the course of my career I have worked as a statistician in the waste industry; in macreconomic modelling and forecasting; on human ecology projects; and on time series forecasting at Google, CA. Some ongoing and more recent projects:
- Copper depletion to prevent breast cancer relapse
- Women's health: hirsuitism and polycystic ovaries
- Blue-green algae blooms in Lake Champlain with the Shapiro Lab
- Correcting accessibility bias in the geologic record with Clément Bataille
I love teaching and science communication and have been a teaching assistant since 2009. I have taught numerous statistics courses at both the Australian National University and Cornell University, including experimental design, finite population sampling, regression, intro stats, and multivariate analysis. It has also been my great pleasure to have taught statistics for microbial ecologists (mostly alpha- and beta-diversity), as well as R workshops, at the course Strategies and Techniques for Analyzing Microbial Population Structure at the MBL in 2013, 2014, 2015 and 2016. See you there for STAMPS '17!
Please feel free to contact me with any questions about my research, software, or collaborations, or for my CV.
Willis, A. and Bell, R.C. (2016). Uncertainty in phylogenetic tree estimates.
Willis, A. (2016). Confidence sets for phylogenetic trees.
Willis, A. (2016). Extrapolating abundance curves has no predictive power for estimating microbial biodiversity. Proceedings of the National Academy of Sciences (Letters).
Willis, A. (2016). Species richness estimation with high diversity but spurious singletons.
Willis, A., Bunge, J. and Whitman, T. (2016). Improved detection of changes in species richness in high-diversity microbial communities. Journal of the Royal Statistical Society: Series C. Accepted.
Chan, N., Willis, A., Kornhauser, N., Vahdat, L., and others. (2016). Influencing the tumor microenvironment: Phase 2 Study of Copper depletion with tetrathiomolybdate in high risk breast cancer and preclinical models of lung metastases. Clinical Cancer Research, In Press.
Willis, A. and Bunge, J. (2016). breakaway: Species richness estimation and modelling. R package version 3.0.
Vanden Brink H., Willis, A., Jarrett, B.Y. and Lujan, M.E. (2015) Sonographic markers of ovarian morphology, but not hirsutism indices, predict serum total testosterone in women with regular menstrual cycles. Fertility and Sterility, In Press.
Willis, A. and Bunge, J. (2015). Estimating Diversity via Frequency Ratios. Biometrics 71 1042-1049.
RoyChoudhury, A., Willis, A. and Bunge, J. (2015). Consistency of a phylogenetic tree maximum likelihood estimator. Journal of Statistical Planning and Inference 161 73-80.
Samorodnitsky, G., Resnick, S., Towsley, D., Davis, R., Willis, A. and Wan, P. (2016). Nonstandard regular variation of in-degree and out-degree in the preferential attachment model. Journal of Applied Probability 53 146-161.
Bunge, J., Willis, A. and Walsh, F. (2014). Estimating the Number of Species in Microbial Diversity Studies. Annual Review of Statistics and Its Application 1 427-445.
Christ, J.P., Willis, A.D., Brooks, E.D., Vanden Brink, H., Jarrett, B.Y., Pierson, R.A., Chizen, D.R. and Lujan, M.E. (2014). Follicle number, not assessments of the ovarian stroma, represents the best ultrasonographic marker of polycystic ovary syndrome. Fertility and sterility 101 280-287.