Jennifer L. Clarke, PhD
Professor
Biostatistics
“Food security should be recognized as a basic human right. Achieving this goal depends on the power of statistics, imaging and AI along with plant sciences advances to develop resilient, high-yield crops. Public health plays a key role in ensuring that food availability translates into meaningful improvements in nutrition.”
Biography
Jennifer Clarke, Ph.D., is a professor of biostatistics and chair of the International Plant Phenotyping Network. She leads the National Agricultural Producers Data Cooperative, a U.S. Department of Agriculture project to stand up a neutral cyber-ecosystem for storing, sharing and analyzing producer data. Dr. Clarke has held previous faculty positions at the University of Nebraska, the University of Miami and Duke University.
Education
- PhD
- Statistics, The Pennsylvania State University, 2000
- MS
- Statistics, Carnegie Mellon University, 1995
- BS
- Mathematics, Skidmore College, 1993
- BS
- Psychology, Skidmore College, 1993
Research interests
Statistical methodology for prediction, open AI and policy, deep learning and image analysis in plant sciences, data federation and cybersecurity, and training the next generation of data scientists. Her work in plant sciences is focused on using statistical and AI techniques for global food security.
Select publications
- Gomes-Neto, J. C., Crook, A., Hestrin, R., Li, G., Liew, C.-S., Rosa, G., Singh, K., Tuggle, C. K., Summers, K. L., Valdes, C., Fahlgren, N., and Clarke, J. Challenges and Opportunities: Computational Biology and the Future of Agriculture. Bioinformatics Advances 2026, 6:1.https://doi.org/10.1093/bioadv/vbag003
- Tuggle, C.*, Clarke, J.*, Murdoch, B.*, Lyons, E.*, Scott, N.*, Benes, B., Campbell, J., Chung, H., Daigle, C., Das Choudhury, S., Dekkers, J., Dorea, J., Ertl, D., Feldman, M, Frangomeni, B., Fulton, J., Guadagno, C., Hagen, D., Hess, A., Kramer, L., Lawrence-Dill, C., Lipka, A., Lubberstedt, T., McCarthy, F., McKay, S., Murray, S., Riggs, P., Rowan, T., Sheehan, M., Steibel, J., Thompson, A., Thornton, K., VanTassell, C., and Schnable, P. Current challenges and future of agricultural genomes to phenomes in the U.S.. Genome Biology 2024, 25, 8 https://doi.org/10.1186/s13059-023-03155-w
- 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
- Dustin, D., Clarke, B., and Clarke, J. Predictive Criteria for Prior Selection Using Shrinkage in Linear Models. Computational Statistics 2023 https://doi.org/10.1007/s00180-023-01342-8
- Clarke, J., Qiu, Y., and Schnable, J. Experimental Design for Controlled Environment High Throughput Plant Phenotyping. In: High Throughput Plant Phenotyping: Methods and Protocols, In: Lorence, A., Medina Jimenez, K. (eds) High-Throughput Plant Phenotyping. Methods in Molecular Biology, vol 2539. July 2022. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2537-8_7
- Sun, Y., Clarke, J., Clarke, B., and Li, X. Predicting antibiotic resistance gene abundance in activated sludge using shotgun metagenomics and machine learning. Water Research 2021, 202: 117384. https://doi.org/10.1016/j.watres.2021.117384
- Amiri, S., Clarke, B., Clarke, J., and Koepke, H. A general hybrid clustering technique. Journal of Computational and Graphical Statistics, 2019, 28: 541-544. https://doi.10.1080/10618600.2018.1546593
- Clarke, B., and Clarke, J. Predictive Statistics, Cambridge University Press. A graduate level textbook on prediction in statistics. Publication date April 12, 2018. https://www.cambridge.org/core/books/predictivestatistics/875021D46B2B7FF26F62E1B072105C50#. Reviewed in Binder, H. Biometrical Journal, 2019, 61: 1600.
- Weh, K.M., Clarke, J., and Kresty, L.A. Cranberries and cancer: An update of preclinical studies evaluating the cancer inhibitory potential of cranberry and cranberry derived constituents. Antioxidants, 2016; 5(3), 27 doi:10.3390/antiox5030027
- Clarke, B., Valdes, C., Dobra, A., and {\bf Clarke, J.}. A Bayes testing approach to metagenomic profiling in bacteria, Statistics and Its Interface 2015, 8 (2): 173-185.