A number of very talented undergraduate students have published with me and others in my group. I want to highlight these students and papers here, because I am so proud of this work. Undergraduate researcher in bold.

Tiana Fitzgerald, A Jones, BE Engelhardt (2022). A Poisson reduced-rank regression model for association mapping in sequencing data. BMC Bioinformatics (accepted) [bioRxiv] [Code]

R de Vito, Isabella N Grabski, D Aguiar, LM Schneper, A Verma, J Castillo Fernandez, C Mitchell, JT Bell, S McLanahan, DA Notterman, BE Engelhardt. Differentially methylated regions and methylation QTLs for teen depression and early puberty in the Fragile Families Child Wellbeing Study. [bioRxiv]

Jonathan Lu, B Dumitrascu, IC McDowell,  AK Barrera, L Hong, SM Leichter, TE Reddy, & BE Engelhardt (2020). Causal network inference from gene transcriptional time series response to glucocorticoids. PLoS Computational Biology. [PDF] [Code]

B Dumitrascu, Karen Feng, BE Engelhardt. GT-TS: Experimental design for maximizing cell type discovery in single-cell data. [bioRxiv] [Talk]

Isabella N Grabski, R De Vito, BE Engelhardt. Bayesian ordinal quantile regression with a partially collapsed Gibbs sampler. [arXiv]

Grace Guan & BE Engelhardt (2019). Predicting sick patient volume in a pediatric outpatient setting using time series analysis. Proceedings of Machine Learning for Health Care (MLHC). [PDF]

B Dumitrascu*, Karen Feng*, BE Engelhardt (2018). PG-TS: Improved Thompson sampling for logistic contextual bandits. Proceedings of Neural Information Processing Systems (NeurIPS). [PDF]

Ghassen Jerfel, ME Basbug, BE Engelhardt (2017). Dynamic collaborative filtering with compound Poisson factorization. Proceedings of Artificial Intelligence and Statistics (AISTATS) 54, 738-747. [PDF]

Vlad Feinberg, L-F Cheng, K Li, & BE Engelhardt. Large linear multi-output Gaussian process learning for time series. [arXiv]