Diana Marcela Escamilla Sanchez

Plant Breeding Data Analyst with expertise in genomic selection, quantitative genetics, and breeding across maize, soybeans, and cotton. Skilled in field research, statistical analysis, and data visualization to optimize plant breeding outcomes. Dedicated to advancing agricultural science through collaboration and cutting-edge data analysis techniques. Proactive and goal-oriented professional with excellent time management and problem-solving skills. Recognized for reliability, adaptability, and quick ability to learn and apply new skills. Committed to driving team success and contributing to organizational growth.

• Conflict Management Training, 2023, Iowa State University
• APSIM workshop, 06/01/23, Iowa State University
• Bayesian Statistics for Genetics, 2020, SISG. The University of Washington
• Mixed Models in Quantitative Genetics, 2020, SISG. The University of Washington
• SOYGEN2 Genomic Selection Workshop, 2020, Soybean Breeding Workshop
• Multivariate Analysis for Genetic Data, 2020, SISG. The University of Washington

• Dow AgroSciences Award, Purdue University, 2020-2021
• Summer Institute of Statistical Genetics (SISG) Scholarship, University of Washington, 2020
• Second place poster competition at the graduate research symposium, Virginia Tech, 2017, Poster: Enhancing Soybean sprout food safety through seed infection treatments.
• William T. Steele, Jr. Graduate scholarship, Virginia Tech, 2016-2017
• Honors Graduation Distinction, National University of Colombia, 2013
• The Adair Research Scholarship, University of Arkansas, 2012

• Genomic Selection: Essence, Applications, and Prospects, Escamilla, D. M., Li, D., Negus, K. L., Kappelmann, K. L., Kusmec, A., Vanous, A. E., Schnable, P. S.; Li, X.; Yu, J., 2024, Accepted at The Plant Genome
• Genome-wide association study reveals GmFulb as candidate gene for maturity time and reproductive length in soybeans (Glycine max), Escamilla, D. M., Dietz, N., Bilyeu, K., Hudson, K., Rainey, K. M., 2024, PLOS ONE, 19, 1, e0294123, https://doi.org/10.1371/JOURNAL.PONE.0294123
• Canopy coverage phenotyping and field spatial variability adjustment as an efficient selection tool in soybean breeding, Escamilla, D. M., Huang, M., McHale, L., Wang, D., Diers, B., Xavier, A., Rainey, K. M., 2023, Crop Science, 63, 6, 3277-3291, https://doi.org/10.1002/CSC2.21084
• Assessing the interactions among carbohydrate content and seed quality traits in soybean, Escamilla, D. M., Xavier, A., Vuong, T. D., Nguyen, H. T., Rainey, K. M., 2023, Theoretical and Applied Genetics, https://doi.org/10.1002/csc2.21027
• Identification and validation of major QTLs associated with low seed coat deficiency of natto soybean seeds (Glycine max L.), Qian Z., Escamilla, D. M., Wu, X., Song, Q., Li, S., Rosso, M.S., Lord, N., Xie, F., and Zhang, B., 2020, Theoretical and Applied Genetics, https://doi.org/10.1007/s00122-020-03662-5
• Improvement of soybean cultivars for natto production through the selection of seed morphological and physiological characteristics and seed compositions: A review, Escamilla, D. M., Rosso, M. L., Holshouser, D. L., Chen, P., and Zhang, B., 2019, Plant Breed., 10.1111/pbr.12678
• Identification of fungi associated with soybeans and effective seed disinfection treatments, Escamilla, D., Rosso, M. L., and Zhang, B., 2019, Food Sci. Nutr., 7, 3194-3205, 10.1002/fsn3.1166
• Evaluation of important seed and sprout traits as potential selection criteria in breeding varieties for sprout soybeans, Escamilla, D. M., Rosso, M. L., Strawn, L. K., and Zhang, B., 2017, Euphytica, 10.1007/s10681-017-2007-0

• Mentor/judge, Hackathon-Bayer (2024)
• Editorial Board Committee member, Maize GDB (2024)
• Congressional Visits for Germplasm Enhancement of Maize project (2023)
• Graduate representative, Agronomy Department, Purdue University (2020-2021)