Group Alumni

Reka Howard's primary research interests are in the application of statistical methodology in plant sciences, quantitative genetics, plant breeding education and curriculum development. For her dissertation research she evaluated 10 parametric and 4 nonparametric statistical methods for prediction purposes in Plant Breeding using simulated data. The results of this project shed light on the conditions under which the nonparametric methods outperform the parametric methods for phenotype prediction. Reka also investigated factor combinations that optimize the performance of the nonparametric methods for phenotype prediction purposes in Plant Breeding. For this purpose, she used Response Surface Methodology. Most recently, she is working on evaluating prediction methods where she is predicting the phenotype for the subsequent generation. Her other major research project involves statistical software development and evaluation in R for molecular data analysis for a structured population of soybeans. The two main components of the research are the use of statistical methods for QTL detection and phenotype prediction. Although her primary area of research is statistical methodology and data analysis in plant sciences, she has additional experience and interest in curriculum development. Reka is involved in in the development of MSc curriculum in Cultivar Development for Africa supported by the Bill and Melinda Gates Foundation. Her role is course content development and review in terms of statistical and quantitative genetics theory. She is also interested in exploring innovative uses of technology in plant breeding education to support learning and teaching, and the influence of different teaching strategies in Plant Breeding across different contexts of learning (for example, classroom versus online learning).

John Cameron received a B.S. in botany from the University of Wisconsin-Madison in 2008. After graduating he worked as an analytical chemist before joining the Peace Corps, where he assisted villages in southern Malawi with agricultural development projects. His PhD research is focused on the optimization of breeding strategies.


Shreyartha Mukherjee is a Ph.D. student in Bioinformatics and Computational Biology from India. He has a B.TECH degree in Information Technology from the Institute of Engineering and Management, Kolkata, India. Currently he is studying Nitrogen use efficiency in terms of genetic mapping. In this project, Shrey wishes to discover specific genes associated with Nitrogen uptake and assimilation in maize by integrating genetic mapping of quantitative trait loci (QTL) controlling maize NUE, mRNA expression profiling, homology modeling and protein structural information. A fundamental and unsolved problem in computational molecular biology is to predict the structure of a protein, given its sequence information. Several protein structure prediction algorithms have been in use in the commercial and the academic world trying to picture biologically feasible conformation of proteins. But none of these algorithms have been so far able to predict structures with satisfactory accuracy. The discriminatory functions of these algorithms often confer high scores to incorrect conformations, thus it is imperative to improve these discriminatory functions to advance the accuracy of protein structure prediction. One method to test these algorithms would be to generate incorrect conformations, very close to the native protein structure and then let the discriminatory function distinguish between correct and incorrect conformations. These structures, better known as protein decoys would help develop better protein modeling tools and high resolution decoys can help us solve structures of binding sites, interaction sites etc. A reduced representation protein modeling tool CABS is extensively used in this project to generate the decoys. The scope of this approach goes beyond generating high-resolution decoys. The applications of this method include ab-initio structure prediction, comparative modeling and loop modeling.


Dawn Gibson completed her Master’s degree in plant breeding in 2014. She worked on Nested Association Mapping in soybeans. Her objective was a joint effort with several institutions to map QTL that control agronomic traits in soybean germplasm. She completed her B.S. in Agronomy from the University of Florida in 2003.


Kendra Meade completed her PhD in plant breeding in 2012 and was a postdoc with the group until accepting a position with Syngenta as a Genetic Project Lead. Her PhD research focused on the genetic analysis of maize kernel traits using canonical models of growth and development.


Mark Newell was in the GFS Population Genetics Lab from 2008 – 2011 during his PhD at Iowa State. His PhD research was primarily genome-wide selection and genome-wide association studies for improved grain quality in oats. After graduation he was an assistant professor for two years at The Samuel Roberts Noble Foundation where he was the small grains breeder with primary focus on wheat and rye products for the Southern Plains. He is now the spring wheat breeder with Monsanto developing improved genetic products for about 13 million acres across the US.


Franco Asoro completed his Ph.D. in plant breeding in May of 2012. He completed his B.S. in Agriculture and M.S. in Plant Breeding at the University of the Philippines Los Banos. Franco now works as a plant breeder for Monsanto. The focus of his Ph.D. research was the use of molecular markers for association mapping and genomic selection. Parts of this project were the following:  association analysis for beta-glucan content in elite North American oats;  assessment of accuracy of genomic selection; and empirical comparison of phenotypic, marker-assisted selection and genomic selection methods.

Jenna Woody

Jenna Woody completed her Ph.D. in Genetics with an emphasis in statistics with Dr. Bill Beavis and Dr. Randy Shoemaker in May 2012. She completed her B.A. in English from the University of Iowa in 2007. She is now a research scientist at DuPont Pioneer. Her Ph.D. research focused on the computational side of primarily soybean research. She focused on the evolutionary demands and interactions between expression characteristics and structural genetics. Her first project focused on the relationship between expression breadth and level and the size of the individual gene. Her Ph.D. project involved characterizing soybean LHGRs (isochore and isochore-like regions) with the plan to extend the methods created while characterizing soybeans into other plant species.

Brandon Miller is a sophomore in agronomy – agroecology, as well as horticulture working on an honors project. His research project focuses on the selection of self-compatible lines in Brassica rapa using conventional and molecular plant breeding techniques. Brandon's responsibilities include working to create a successful number of genetically pure inbreds of Brassica rapa, as well as helping the lab group with an assortment of other projects. Brandon plans to go on to further his education by working on a M.S. degree in agronomy or horticulture.


Michael Paulsmeyer is an junior in the Department of Agronomy. He is interested in the genetics of double-haploids and is working with Danielle Dykema to optimize the growth of Arabidopsis thaliana haploid inducer lines from callus tissue. Michael plans to attend graduate school with a focus in plant genetics after completing his undergraduate degree.



Chris Meyer completed his B.S. in Agronomy - Plant Breeding in December 2012. Chris is currently working on his Master's degree in Weed Science. He worked with Dr. Beavis to maintain and care for Brassica rapa and Mimulus plants. Chris’s duties included working to create a successful number of genetically pure inbreds of both Brassica rapa and Mimulus to begin experimentation.


Lynn Veenstra finished her B.S. Agronomy and Genetics in May 2012. Lynn is now a graduate student in Genetics at Cornell. She worked on her honors research project with Dr. Beavis. Lynn’s project focused on the trends of Beta-Glucan levels in crosses of wild and adapted oats.

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