2023 Faculty Mentors

Meet our faculty mentors

Here are our participating faculty mentors and their areas of research interest. Click their names to see more about their lab and project. Some faculty mentors are not available for the 2023 program.

PI Area of research*
Chang Plant-microbe interactions; agricultural systems; microbial evolution; microbial ecology; genomics; molecular biology
David Animal-microbe interactions; model systems; animal behavior; mental health; microbial ecology; microbiome; genomics
Deluc Plant stress (environmental and pathogen); genetic engineering; agricultural systems; genomics; molecular biology 
Fowler Plant reproduction and development; agricultural systems; plant genetics and breeding; genomics; computer vision
Frost Plant-microbe interactions; agricultural systems; modeling (not accepting applicants)
Goyer Improving nutritional value of crops; agricultural systems; plant-pathogen interactions; genomics; transcriptomics (tentatively accepting applicants)
Grunwald Plant-pathogen interactions; agricultural and forest systems; genomics; population genomics 
Jaiswal Plant stress (environmental); agricultural systems; model systems; transcriptomics; data curation 
Kronmiller Bioinformatics; data science; genomics
LeBoldus Plant-pathogen interactions; forest systems; genomics; molecular biology
Leiboff Plant development, visualization; transcriptomics
Liston/Mickley Plant evolutionary biology; plant ecology; natural systems; genomics; genetics; population genomics; biodiveristy informatics
Rivedal Plant-pathogen interactions; virology; genomics; epidemiology
Sharpton Animal-microbe interactions; model systems; animal health; microbial ecology; microbiomes; genomics (potential to accept up to two interns)
Spatafora Fungal evolutionary biology; fungal ecology; insect-fungal interactions; genomics 
Weisberg Plant-pathogen interactions; genomics; evolution; ecology


*Some groups focus on the host plant while others focus on the microbe.

Epidemiology: the study of the incidence and spread of disease.

Genomics: study of genomes (entire set of genes in an individual).

Metabolomics: study of the metabolites that are produced at a specific time, in a specific location, or in response to a specific signal.

Microbiomes: the study of all the microbial (fungal and/or bacterial) genomes within a community (interacting group of microbes). We use short molecular barcodes called “amplicons” or we use whole genome sequences.

Plant molecular breeding: the use of DNA sequences to accelerate breeding and development of new lines of crops with desirable characteristics.

Population genomics: study of the natural variation in genomes of a population.

Proteomics: study of all proteins that are expressed at a specific time, in a specific location, or in response to a specific signal.

Transcriptomics: study of subset of genes that are expressed as RNA at a specific time, in a specific location, or in response to a specific signal.

Jeff Chang, PI

The Chang lab studies the interactions between bacteria and plants. We focus on bacteria and study both mutualistic (beneficial) and pathogenic (detrimental) types of bacteria to understand their ecology and evolution, and the mechanisms by which they interact with plants. Our research relies heavily on generating, processing, and analyzing whole genome sequences to generate and test hypotheses. To date, we have sequenced more than 500 genomes from many taxa of bacteria.

Lab website: http://changlab.cgrb.oregonstate.edu/


Students will learn to process and analyze whole genome sequence data. Tasks may include assembling genome sequences, constructing phylogenies, identifying genes of interest, and/or using genomic data to generate hypotheses on transmission and evolution. 


Students will learn fundamental concepts in plant symbiotic interactions, information flow, and genomics. Students will use genomic data to learn the scientific process.



Maude David, PI

Dr. Maude David's laboratory studies the gut-brain axis, to understand how microbes can impact our behavior, specifically in Autism Spectrum Disorder and Anxiety. She uses a crowd-sourced approach to collect lifestyle type information, diet habits, and samples. Her team is also working on identifying bottlenecks in microbial ecology and bioinformatics, bringing novel solutions to unravel microbial molecular mechanisms by optimizing new molecular methods and improving massive sequencing data annotation.

Lab website: https://microbiology.science.oregonstate.edu/maude-david


Student will learn to process and analyze soil or gut microbiome (human or mice) sequencing data. Tasks may include 16S (short DNA sequences that can be used as barcodes to distinguish between organisms) data analysis (microbial structure) as well as genome sequence assembly, public datasets curation to apply machine learning, and/or multivariate analysis from the Human Microbiome Project, in the context of gut brain-axis.


Students will learn fundamental concepts in Soil and/or Human Gut Microbiome analysis. The laboratory mainly uses R, python and the student will help develop workflow in collaboration with other students to help the team implement new analysis and tools.



Laurent Deluc, PI

The Deluc Lab studies different aspects of plant crop development in relation with abiotic and biotic stresses. The major crop model that is studied is grapevine. Our research focuses on three main research themes; i) ripening, ii) long-distance communication between plant organs (shoot and roots), and iii), plant-pathogen interactions with a focus on Grape Leafroll Virus, a major worldwide pest in grape production. Using OMICS tools, we have gathered compelling evidence to link a series of gene(s) to economically important trait for grapevine production. Our next step is to validate the function of these genes using traditional genetic engineering and new breeding techniques (CRISPR/Cas9). To do so, we use the microvine (Chaïb et al., 2010), a grapevine model system amenable for genetic engineering because of its short-life cycle. We have sequenced the genome of microvine and are in the process of assembling it. Our next step is to create a series of mutant collections on targeted gene families known to be associated with economically important traits in grape production. We are currently developing a CRISPR interference and CRISPR activation pipeline to generate these mutants (Lowder et al., 2015).

Lab website: https://www.delucl.com


Students will learn to process and analyze whole genome sequence data. Tasks may include assembling genome sequences (microvine), manually curating the functional annotation of gene models, designing, synthesizing, and cloning for CRIPSR experiments.


Students will learn fundamentals in gene editing, assembly of genomes, and fundamentals of gene structure. The students will also learn how to handle and share big data in a computing structure.


Chaïb J, Torregrosa L, Mackenzie D, Corena P, Bouquet A, Thomas MR (2010) The grape microvine - a model system for rapid forward and reverse genetics of grapevines. Plant J 62: 1083–1092

Lowder LG, Zhang D, Baltes NJ, Paul JW III, Tang X, Zheng X, Voytas DF, Hsieh T-F, Zhang Y, Qi Y (2015) A CRISPR/Cas9 Toolbox for Multiplexed Plant Genome Editing and Transcriptional Regulation. Plant Physiol 169: 971–985



John Fowler, PI

The Fowler lab studies the molecular mechanisms that govern cellular morphogenesis and development in plants. Broadly, we are interested in how cellular processes – for example, exocytosis – are integrated into developmental systems at the organismal scale. The lab uses a variety of complementary techniques in genetics, genomics, transcriptomics, cell biology and (more recently) computational analysis of digital images to investigate these processes. The lab’s current focus is on the male gametophyte of Zea mays, including pollen and the growing pollen tube, structures required for crop plant reproduction and seed generation. Our current challenge is to utilize large scale datasets (e.g., from transcriptomics) to predict phenotypic outcomes (e.g., which genes exert the largest influence on reproductive success?).

Lab website: https://bpp.oregonstate.edu/people/fowler-john


Students will help choose from two possible projects: 1) developing approaches for automated analysis of digital images of phenotypes, i.e., ‘computer vision’; or 2) processing and analyzing whole genome sequence data to determine the genomic location of certain types of mutations that could influence plant development and reproduction. Tasks may include generation of training sets for machine learning and image analysis; genome and gene model sequence analysis; and/or using transcriptomic data to generate hypotheses regarding gene function. One recent publication from the lab exemplifies our genomic and transcriptomic work: Chettoor et al. 2014.


Students will learn fundamental concepts in plant genetics, molecular and cellular biology. Students will use genomic and/or high content digital imaging data to learn the scientific process.



Ken Frost, PI

A recent research focus of the Frost lab has been to investigate how the soil microbial community structure changes in response to different crop management practices. Since, soil microbial communities support a wide range of ecosystem services required for maintaining soil structure and fertility, supporting carbon, nitrogen, and phosphorus cycling, and removing soil contaminants, we are interested to learn how different crop management practices can influence soil bacterial and fungal community structure as well as plant health. Our research uses, culture-independent, high-throughput sequencing techniques to investigate changes in soil microbial community structure in response to commonly used crop production practices including crop rotation and pesticide application.

Extension website: Hermiston Agricultural Research and Extension Center


Students will learn to process and analyze amplicon sequence data to profile soil microbial communities in an agricultural field experiment or as a function of observed independent variables.


Students will learn fundamental concepts in experimental design, plant pathology, and microbial ecology. Students will learn about the capabilities and limitations of a technique used to study the soil microbiome.



Aymeric Goyer, PI

The Goyer lab has two main research fields of interest: (1) vitamin metabolism in food crops, and (2) interactions between potato virus Y and potato. Our research involves whole transcriptome and genome analyses to identify genes and gene networks that function in the aforementioned biological pathways.  

Lab website: http://blogs.oregonstate.edu/agoyer10162018/


Students will learn to process and analyze whole transcriptome and/or genome sequence data. Tasks will include mapping reads to reference genome, differential expression analysis, visualization of data, and identification of single nucleotide polymorphism. One publication that exemplify the types of work the student will do is in Goyer et al 2015 (see the link to the Goyer lab webpage above for a full copy of the paper).


Students will learn fundamental concepts in plant secondary metabolism and/or plant-pathogen interactions, transcriptomics and genomics.



Niklaus Grunwald, PI

The Grunwald lab is interested in the epidemiology, genetics and evolution of exotic and reemerging Phytophthora species. The genus Phytophthora contains some of the most destructive plant pathogens that affect agricultural and forest crops. Important examples include P. ramorum, a devastating exotic pathogen causing sudden oak death, and P. infestans, known as the cause of the Irish potato famine. Much of our work focuses on translational applications towards improving disease management in agriculture. Our team combines basic tools from genomics, epidemiology and bioinformatics, with translational research approaches to strategically address some of the fundamental challenges posed by plant diseases caused by the genus Phytophthora. We have and continue to sequence whole genomes of important Phytophthora pathogens.

Lab website: http://grunwaldlab.cgrb.oregonstate.edu/


Students will learn to process and analyze whole genome sequences and population genetic data. Tasks may include assembling genome sequences, calling variants in populations, identifying genes of interest, and/or using genomic data to generate hypotheses on pathogen emergence. Select publications exemplifying our research approaches include:


Students will learn fundamental concepts in characterizing genomes and populations using high throughput sequencing data and computational approaches. Students will use genomic data to learn the scientific process.



Pankaj Jaiswal, PI

The Jaiswal lab studies abiotic stress responses in international crops such as rice, wheat, sorghum and bioenergy feedstock plants poplar and Brachypodium. We focus on analyzing time-series dependent transcriptome analysis and genome annotations and profile the underlying genetic differences carried by various crop varieties showing contrasting stress response characteristics to understand the function of genes and their response towards stress tolerance.

Lab website: http://jaiswallab.cgrb.oregonstate.edu/


Students will learn to design an experiment on stress treatment of a crop plant, collect samples for transcriptome sequencing, generate, manage process and analyze the data. Students will also learn the Biocuration process to synthesize known prior biological knowledge about the genes, biochemical pathways and gene regulation which will help in data analysis and building hypothesis.


The students will learn data management, designing experiments, FAIR data principles, Biocuration and data analytics.



Brent Kronmiller, PI

The Center for Quantitative Life Sciences Bioinformatics and Data Science group conducts bioinformatics research and consulting across Oregon State University.  Eight bioinformatics analysts and trainers work on research projects such as RNAseq analysis, genome assembly, gene prediction, environmental sequence analysis, COVID research, and bioinformatics programming in a wide range of scientific domains.

Lab website: https://cqls.oregonstate.edu/bioinformatics


A variety of projects are available. Broadly, students can learn to use biological computing to test hypotheses, develop computational methods to process and/or manage large datasets, or test efficacy of computational methods that are currently under development.


Students will learn fundamental concepts in biological computing, best practices for working on shared, large computing infrastructure, and skills for collaborating within research teams. Application of the scientific method to analyze large data sets.



Sam Leiboff, PI

We use mutant plant varieties to understand the genes necessary to make a normal, healthy corn plant. We use 1) next generation sequencing, long-read Oxford Nanopore, and chromatin structural sequencing to identify the genetic changes that cause mutations while we 2) capture comprehensive image-based measurements and fit mathematical models to compare normal and mutant development. Our work is split 60/40 between indoor laboratory work and field-based sampling in our nearby corn nursery .

Lab website: Sam Leiboff


Students will use advanced genetic pedigrees to map and characterize mutant maize phenotypes. Mapping tasks will include experimental work to prepare DNA for sequencing and/or the bioinformatic analysis of the map data to identify potential causative mutations. Mutant characterization will include high-throughput genotyping with molecular markers, field-based measurements of plant growth, high-throughput imaging of live plant tissue followed by computational image processing and modeling to identify and model mutant vs normal plant tissue shape.


Students will design and execute hypothesis-driven research with a variety of techniques. Over the summer, students will learn and deploy basic to advanced concepts in genetics and plant development, including mutant analysis of organ production and mapping-by-sequencing.

  • Molecular training will include DNA extraction, PCR, restriction digest, Sanger sequencing, with potential preparation of Illumina sequencing libraries.
  • Computational training will include the design or deployment of analytical pipelines to align genomic DNA sequences to a refence genome, identify sequence variants, map mutations using variant allele frequency, automated image processing, and statistical shape description with R.
  • Field training will include pedigree management, sampling logistics, and fundamentals of maize propagation.



Aaron Liston, PI

The Liston lab applies genomic approaches to the study of plant evolution, using the strawberry genus as our model system. Our current research focuses on understanding the role of whole genome duplication in the evolution of sexual dimorphism in plants.

Lab website: http://blogs.oregonstate.edu/listonlab/


Students will learn to process and analyze genome sequence data. Research opportunities include genetic linkage mapping of sex determination, population genetic analyses, and phylogenetic analysis of interspecific hybridization.


Students will learn fundamental concepts in plant evolution and genomics. Students will use genomic data to learn the scientific process.



Dimitre Mollov, PI

The USDA Virology lab focuses on virus detection, virus epidemiology, virus co-infections, virus taxonomy, transmission, and disease development. We also focus on discovery and characterization of new and emerging viruses with relevance to agriculture.  Findings are used to inform on disease management strategies.

Lab website: https://www.nwsmallfruits.org/researchers/


Students will learn to process and analyze genomic data of viruses. Tasks may include assembling genome sequences, constructing phylogenies, identifying genes of interest, and using genomic data to generate hypotheses on transmission and evolution of viral pathogens.


Students will learn fundamental concepts in plant-viral interactions, virus evolution, virus taxonomy, and genomics. Students will learn how fundamental discovery can be translated to applications.





Hannah Rivedal, PI

The Rivedal lab studies, develops, and implements strategies to manage diseases that affect the forage seed, grass seed, and hemp industries. Important strategies include molecular diagnostics and use of high throughput sequencing to detect and identify pathogens.

Lab website: https://bpp.oregonstate.edu/users/hannah-rivedal


The intern will sequence the genomes of viruses of hemp.


The students will learn virology, plant-microbe interactions, agriculture, genome sequencing, annotation and analysis.



Thomas Sharpton, PI

The Sharpton Lab defines how the gut microbiome impacts vertebrate health, behavior, and evolution and ultimately aims to use this knowledge to design novel disease diagnostics and therapeutics. Our interdisciplinary research relies on microbiology, bioinformatic and systems biology techniques, and often involves developing novel computational and analytical methods to efficiently analyze massive data sets. We actively collaborate with other laboratories to strengthen and broaden our research, which frequently includes studying microbiomes in non-human organisms to improve our understanding of general microbiome properties.

Lab website: http://lab.sharpton.org/


Students will learn the interdisciplinary process of generating and analyzing gut microbiome data. Tasks may include conducting DNA extraction from samples collected from animals, sequencing DNA, using bioinformatic procedures to analyze DNA sequences, and applying statistical methods to generate hypotheses about how the microbiome’s composition relates to the health of its host. Two recent publications that exemplify the types of work the students will do are Conley et al. 2016 and Gaulke et al. 2018. These publications are linked via the Sharpton lab website under the publication tab.


Students will learn foundational concepts and methods in host-microbiome interactions and microbiome informatics. Students will use microbiome data to learn the scientific process.



Joey Spatafora, PI

Our research is focused on evolutionary biology of fungi with emphases in phylogenetics and comparative genomics across a diversity of taxonomic and ecological systems.  There are currently four main focus areas, all of which seek to use genome-scale data and phylogenetic methodology to address questions in fungal evolutionary biology. 1) The Zygomycetes Genealogy of Life (ZyGoLife) - the Conundrum of Kingdom Fungi seeks to understand numerous questions involving organismal and genomic evolution of fungi hypothesized to represent some of the earliest lineages to colonize land. 2) 1000 Fungal Genomes (1KFG) Project is designed to use genome data from across the Kingdom Fungi to address numerous questions regarding major fungal ecologies and nutritional modes. 3) Evolution of Insect Pathogenic Fungi has been a focus of our lab for more than 20 years.  Much of our recent research seeks to understand patterns and processes that have facilitated host jumps and evolution of novel ecologies. 4) Systematics and Population Biology/Ecology of Ectomycorrhizal Fungi is one of the more active areas of research in mycology. We are using genome scale data to understand patterns between Rhizopogon, a type of truffle, and its host trees. 

Lab website: https://joeyspataforalab.weebly.com/


Students will learn to process and analyze whole genome sequence data. Tasks may include assembling genome sequences, annotating genome sequences, constructing phylogenies, identifying genes of interest, and/or using genomic data to generate hypotheses on fungal organismal and genomic evolution.  Recent publication that exemplify the types of work the student will do are in Bushley et al. (2013), Quandt et al. (2015, 2018), Mujic et al. (2019) and Chang et al. (2019); these are provided in the Spatafora lab website under publications.


Students will learn fundamental concepts in fungal biology, information flow, and genomics. Students will use genomic data to learn the scientific process. Students will work closely with postdoctoral researchers and graduate students.



Alexandra Weisberg, PI

The Weisberg lab studies the interactions between bacteria and plants. We use whole genome sequencing of bacterial pathogens to study the evolution and ecological bases for plant-microbe interactions. We also study the "flexible" genome of pathogens; these are dynamic regions that can be swapped between very diverse bacteria and potentially add or alter traits.

Lab websitehttps://weisberglab.com/


Students will learn to process and analyze genomic data of plant pathogenic bacteria. Tasks may include assembling genome sequences, constructing phylogenies, identifying genes of interest, and using genomic data to generate hypotheses on the evolution of pathogens.


Students will learn fundamental concepts in bacterial evolution, plant-microbe interactions, and genomics.