CV

Other Activities: Director of the Quantitative Life Science Initiative. Dr. Clarke works with the Dean of the Agricultural Research Division and the Associate Vice Chancellor for Research and Innovation to enable and integrate 'big data' sciences at UNL and across the University of Nebraska system. She also leads the USDA National Agricultural Producers Data Cooperative and serves as Chair of the International Plant Phenotyping Network. 

Research Area: Dr. Clarke's research interests encompass statistical methodology (with an emphasis on high dimensional and predictive methods), statistical computation, bioinformatics/computational biology, multi-type data analysis, artificial intelligence/machine learning, and bacterial genomics/metagenomics.

Five Selected Publications:

Clarke, J., Cooper, L., Poelchau, M., Berardini, T, Elser, J., Farmer, A., Ficklin, S., Kumari, S., Laporte, M.-A., Nelson, R., Sadohara, R., Selby, S., Thessen, A., Whitehead, B., and Sen, T. Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the AgBioData consortium. Databases, 2023. https://doi.org/10.1093/database/baad076

Penas, C. , Maloof, M. , Stathias, V. , Long, J., Tan, S.-K., Mier, S., Fan, Y., Valdes, C., Rodriguez- Blanco, J., Chiang, C.M., Robbins, D., Liebl, D., Lee, J., Hatten, M., Clarke, J., and Ayad, N. Time-series Modeling of Cell Cycle Exit Identifies Brd4-dependent Regulation of Cerebellar Neurogenesis. Nature Communications, 2019, 10: 3028. http://doi.org/10.1038/s41467-019-10799-5

Dobra, A. , Valdes, C. , Ajdic, D., Clarke, B., and Clarke, J. Assessing Statistical Dependence within Microbial Communities with Clique Log-Linear Models. Annals of Applied Statistics, 2019, 13(2): 931-957. http://doi.org/10.1214/18-AOAS1229

Amiri, S., Clarke, B., Clarke, J., and Koepke, H. A general hybrid clustering technique. Journal of Computational and Graphical Statistics, 2019, 28: 541-444. https://doi.org/10.1080/10618600.2018.1546593

Xu, Z., Valdes, C., and Clarke, J. Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots. Special Issue of Agronomy on Precision Phenotyping in Plant Breeding, 2018; 8 (5), 71. https://doi.org/10.3390/agronomy8050071