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Department of Computer Science and Technology

  • Departmental Early Career Fellow
  • This person is no longer active in the department.

I develop statistical and computational methods to characterize the interplay between morphology and genomics in the early development of organisms. 




Previously, I was a Member in the School of Mathematics at the Institute for Advanced Study (IAS).

I received my PhD in Computational Biology from Princeton University. Before that, I did my undergraduate

studies at MIT where I earned a B.S. in Mathematics.


My long term goal is understanding how single cells aggregate temporal and spatial, chemical and physical

information to make decisions about their identity, and how these decisions can be altered through optimal

perturbations and experimental design. Informed by both experiment and theory,

I am interested in interpretable and principled techniques, deep and shallow alike,  with roots in

hypothesis testing, domain adaptation, representational learning, and active learning.


Current research directions and projects include:

* interpretable dimensionality reduction, linear and non-linear, for single cell gene expression studies

* multi-modal models of morphology, gene expression and epigenetics for the study of the early mouse embryo

* experimental design of combinatorial perturbations 

* statistics of organic pattern formation and synchronization phenomena

* interpretable models for multi-agent trajectory prediction

Professional Activities

Invited Talks and Seminars

  • Eliciting structure in genomics data, Institute for Mathematical and Statistical Innovation, Chicago, USA, 2021
  • Data Science Lunch Seminar Series, NYU Data Science, NYC, USA, 2021
  • The Computer Science & Engineering Department Seminar, University of Connecticut, Storrs, CN, USA, 2021
  • Department of Statistics & Actuarial Science Seminar, University of Hong Kong, Hong Kong, 2021
  • Models, Inference, and Algorithms Seminar, Broad Institute of MIT and Harvard, Boston, MA, USA, 2018 
  • Probabilistic Modeling in Genomics, Oxford University, Oxford, UK, 2016
  • New York Area Population Genomics Workshop, NYC, USA, 2015 


[* equal contribution]

[♦ corresponding author, co-corresponding authors]

Peer-reviewed (Journal & Conferences)

  • [new!] Deep learning for bioimage analysis in developmental biology
    Adrien Hallou, Hannah G. Yevick, ♦Bianca Dumitrascu, ♦Virginie Uhlmann 
    Development 15 September 2021; 148 (18): dev199616, doi:
  • Optimal marker 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,
  • 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,
  • 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 
  • 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 
  • 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 
  • 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 
  • PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
    Bianca Dumitrascu*, Karen Feng*, and Barbara E Engelhardt
    NeurIPS 2018 
  • Statistical tests for detecting variance effects in quantitative trait studies
    Bianca Dumitrascu, Gregory Darnell, Julien Ayroles, and Barbara E Engelhardt
    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
    Nature Communications, 9(1), 2018 


  • Sequential Gaussian Processes for Online Learning of Nonstationary Functions
    Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt 
  • 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 


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