Dr Christina Azodi

Research Unit

Bioinformatics & cellular genomics


Post-doctoral fellow

Professional Experience

2019                  Post-doctoral fellow, Bioinformatics and Cellular Genomics,
                          St Vincent’s Institute of Medical Research, Melbourne, Australia
2018                  Visiting researcher, Insight Center for Data Analytics,
                          Dublin City University, Dublin, Ireland
2014 - 2019       PhD. Plant Biology, Michigan State University, East Lansing,
                          Michigan, USA
2012 - 2014       Research Assistant, Boyce Thompson Institute, Ithaca, New York,
2008 - 2012       BA, Molecular Biology and Biochemistry, Middlebury College,
                          Middlebury, Vermont, USA


2019                Best Flash Talk Award, Great Lakes Bioenergy Research Consortium
2018                Graduate Research Opportunities Worldwide Fellowship, National
                        Science Foundation (NSF)
2018                Best Flash Talk Award, Great Lakes Bioenergy Research Consortium
2017                Best Poster Award, Environmental Science Policy Program at MSU
2015                Graduate Research Fellowship, National Science Foundation (NSF)
2014                Recruitment Fellowship, Environmental Science Policy Program at
2011                Beck Botanical Research Fellowship, Middlebury College

Research Interests

Single Cell Methods
I am working on developing statistical methods and software to study the effects of DNA variation on gene expression in individual cells. Improving our ability to study functional genomics at the single cell level will make it possible to explain the contexts in which genetic variants affect important traits like gene expression, disease risk, and drug response.

Interpretable Machine Learning
Machine learning has emerged as a critical tool for making sense of the growing amount of genetic and genomic data available because of its ability to find complex patterns in high dimensional and heterogeneous data. I am interested in developing methods that make the inner workings of machine learning models understandable to researchers so that these models can provide novel insights into today’s most pressing questions in genetics, genomics, agriculture, and medicine.

Selected Publications

  1. Azodi, C. B., J. Tang, & S. Shiu. Illuminating the black box: Interpretable machine learning in genetics. Trends in Genetics. 36:5 (2020). doi: 10.1016/j.tig.2020.03.005
  2. Azodi, C. B., Pardo, J., VanBuren, R., de los Campos, G. & Shiu, S.-H. Transcriptome-Based Prediction of Complex Traits in Maize. The Plant Cell. 32, 139–151 (2020).
  3. *Uygun, S., *Azodi, C. B. & Shiu, S.-H. Cis-Regulatory Code for Predicting Plant Cell-Type Transcriptional Response to High Salinity. Plant Physiol. 181, 1739–1751 (2019).
  4. Azodi, C. B. et al. Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits. G3. 9, 3691–3702 (2019).
  5. Azodi, C. B. & Dietz, T. Perceptions of emerging biotechnologies. Environ. Res. Lett. 14, 114018–9 (2019).
  6. Lloyd, J. P., Bowman, M. J., Azodi, C. B. et al. Evolutionary characteristics of intergenic transcribed regions indicate rare novel genes and widespread noisy transcription in the Poaceae. Scientific Reports. 1–14 (2019). doi:10.1038/s41598-019-47797-y
  7. Panchy, N. L., Azodi, C. B., Winship, E. F., O’Malley, R. C. & Shiu, S.-H. Expression and regulatory asymmetry of retained Arabidopsis thaliana transcription factor genes derived from whole genome duplication. BMC Genomics. 1–17 (2019). doi:10.1186/s12862-019-1398-z
  8. Dowdell, A. S., Murphy, M. D., Azodi, C. B. et al. Comprehensive Spatial Analysis of the Borrelia burgdorferi Lipoproteome Reveals a Compartmentalization Bias toward the Bacterial Surface. Journal of Bacteriology 199, 1509–20 (2017).
    * Co-first authors