News!

I lead the Computational Morphogenomics Group . We are moving to Columbia University Spring 2023, and we have many opportunities for interns, students, and postdocs. Get in touch if interested at bmd39@cam.ac.uk!

About me

I am an Affiliated Lecturer in the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge. I am a Departmental Early Career Fellow in the Accelerate Programme for Scientific Discovery. I work at the intersection of machine learning and genetics. My main research interest is understanding how local molecular rules give raise to emergent spatial patterns in the context of biological dynamical systems. To this end, I use techniques from statistical optimization, statistical physics and domain adaptation to identify contextual phenotypes in spatial transcriptomic data and to understand the identity of single cells and their interactions in early developement. I am also interested in active learning and graphical neural networks as models to study message passing in multi-agent systems.

Previously, I was a Member in the School of Mathematics at the Institute for Advanced Study and I attended the semester long program in deep learning at the Statistical and Applied Mathematical Sciences Institute, where I was graciously hosted by David Dunson in the Duke Statistical Science Department. I received my PhD in Computational Biology at Princeton University, under the mentorship of Barbara E. Engelhardt. My PhD research focused on the effect of experimental design in single cell gene expression studies and on method development for structured, high-dimensional medical and genomic data. I did my undergraduate studies in Mathematics at MIT.

Peer-Reviewed Publications

[* equal contribution] [♦ corresponding author]

☕ Deep learning for bioimage analysis in developmental biology
Adrien Hallou*, Hannah G. Yevick*, ♦ Bianca Dumitrascu, ♦ Virginie Uhlmann
Development 15 September 2021; 148 (18): dev199616 [paper]

☕ Optimal gene selection for cell type discrimination in single cell analyses
Bianca Dumitrascu*, Soledad Villar*, Dustin G Mixon, Barbara E Engelhardt
Nature communications 12 (1), 1-8, 2021 [paper]

☕ Causal Network Inference from Gene Transcriptional Time Series Response to Glucocorticoids
Jonathan Lu*, Bianca Dumitrascu*, Ian C McDowell, Brian Jo, Alejandro Barrera, Linda K. Hong,Sarah M. Leichter, Timothy E. Reddy, Barbara E. Engelhardt
PLOS Computational Biology, 2021 [bioRxiv paper]

☕ Sparse multi-output Gaussian processes for online medical time series prediction
Li-Fang Cheng, Bianca Dumitrascu, Gregory Darnell, Corey Chivers, Michael E Draugelis, Kai Li, and Barbara E Engelhardt
BMC Medical Informatics and Decision Making 20.1 (2020): 1-23

☕ Nonparametric Bayesian multi-armed bandits for single cell experiment design
Federico Camerlenghi*, Bianca Dumitrascu*, Federico Ferrari*, Barbara E. Engelhardt, Stefano Favaro
Annals of Applied Statistics, 2020 [paper]

☕ Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
Li-Fang Cheng, Bianca Dumitrascu, Michael Zhang, Corey Chivers, Michael Draugelis, Kai Li, Barbara E Engelhardt
AISTATS 2020 [arXiv paper]

☕ netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction insingle-cell expression analysis
Rebecca Elyanow, Bianca Dumitrascu, Barbara E Engelhardt, and Benjamin J Raphael
Genome Research 30: 195-20, 2020; RECOMB 2019 [paper]

☕ End-to-end training of deep probabilistic CCA for joint modeling of paired biomedical observations
Gregory Gundersen, Bianca Dumitrascu , Jordan T. Ash, Barbara E. Engelhardt
UAI 2019 [paper]

☕ PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
Bianca Dumitrascu*, Karen Feng*, and Barbara E Engelhardt
NeurIPS 2018 [paper]

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

☕ 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
Nature Communications, 9(1), 2018 [paper]

Preprints

☕ Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt [arXiv paper]

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

☕ 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 [arXiv paper]

Curriculum Vitae

My CV can be found here.