Edoardo Airoldi

Assistant Professor of Statistics

Harvard University
Science Center, Room 604
1 Oxford Street
Cambridge, MA 02138
Tel: 617-496-8318
Email: airoldi@fas.harvard.edu

Website:

http://www.fas.harvard.edu/~airoldi/
Lab Size: Between 15 and 20

Summary

We develop and apply statistical methodology and algorithms that enable new lines of inquiry in systems biology and integrative genomics. Our focus is on developing mechanistic models that produce predictions testable at the bench. Our approach leads to quantitative insights into molecular and chemical aspects of cellular biology that are not directly measurable with experimental technologies. We are broadly interested in regulatory mechanisms that drive cellular proliferation and growth, the cell cycle, the metabolic cycle, C and N metabolism, nutrient and stress response, protein-mRNA regulation, and cancer systems.Markowetz, F., Mulder, K.W., Airoldi, E.M., Lemischka, I., & Troyanskaya, O.G. (2010). Mapping dynamic histone acetylation patterns to gene expression in Nanog-depleted murine embryonic stem cells. PLoS Computational Biology, In press.

Publications

Katz, Y., Wang, E., Airoldi*, EM., & Burge*, CB. (2010). Analysis and design of RNA sequencing experiments for identifying mRNA isoform regulation. Nature Methods, 7, 1009-1015.

Silva, R., Heller, K.A., Ghahramani, Z., & Airoldi E.M. (2010). Ranking relations using analogies in biological and information networks. Annals of Applied Statistics, 4, 615-644.

Goldenberg, A., Zheng, A.X., Fienberg, S.E., & Airoldi E.M. (2009). A survey of statistical network models. Foundations and Trends in Machine Learning, 2, 129-233.

Lu, R., Markowetz, F., Unwin, R.D., Leek, J.T., Airoldi E.M., MacArthur, B.D., Lachmann, A., Rozov, R., Ma’ayan, A., Boyer, L.A., Troyanskaya, O.G., Whetton, A.D., & Lemischka, I.R (2009). Systems-level dynamic analyses of fate change in murine embryonic stem cells. Nature, 462, 358-362.

Barutcuoglu, Z., Airoldi, E.M., Dumeaux, V., Schapire, R.E., & Troyanskaya, O.G. (2009). Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields. Bioinformatics, 25: 1307-1313.