About me

I am a PhD student in the Quantitative and Computational Biology program at Princeton University,
working with Prof. Barbara E. Engelhardt. I am developing statistical methods at the intersection of genomics and machine learning. My main interest is in single cell data analysis and experimental design, and how they can benefit from careful structural bayesian analysis. Before Princeton, I studied mathematics at MIT.


netNMF: A network regularization algorithm for dimensionality reduction and imputation of single-cell expression data
Rebecca Elyanow, Bianca Dumitrascu, Barbara E Engelhardt, and Benjamin J Raphael
In RECOMB 2019

PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
Bianca Dumitrascu, Karen Feng, and Barbara E Engelhardt
In the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018)

Statistical tests for detecting variance effects in quantitative trait studies
Bianca Dumitrascu, Gregory Darnell, Julien Ayroles, and Barbara E Engelhardt
In Bioinformatics, 2018

BIISQ: Bayesian nonparametric discovery of Isoforms and Individual Specific Quantification from RNA-seq data
Derek Aguiar, Li-Fang Cheng, Bianca Dumitrascu, Fantine Mordelet, Athma Pai, and Barbara E Engelhardt
In Nature Communications, 9(1), 2018


GT-TS: Experimental design for maximizing cell type discovery in single-cell data
Bianca Dumitrascu, Karen Feng, and Barbara E Engelhardt

Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction
Li-Fang Cheng, Gregory Darnell, Bianca Dumitrascu, Corey Chivers, Michael E Draugelis, Kai Li, and Barbara E Engelhardt

Workshop Papers

End-to-end training of deep probabilistic CCA for joint modeling of paired biomedical observations
Gregory Gundersen, Barbara Dumitrascu, Jordan T Ash, and Barbara E Engelhardt
In the NeurIPS Workshop on Bayesian Deep Learning, 2018

Invited Talks


Curriculum Vitae

My CV can be found here.