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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 2021 program.
|PI||Area of research*|
|Plant-pathogen interactions; model system; transcriptomics; proteomics; metabolomics; molecular biology (not available 2021)|
|Behrenfeld||Microbe and global ecology; remote sensing; modeling|
|Busby||Plant-microbe interactions; model system; agricultural systems; microbial ecology; microbiomes (not available 2021)|
|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 (not available 2021)|
|Dung||Plant-microbe interactions; agricultural systems; modeling|
|Fowler||Plant reproduction and development; agricultural systems; plant genetics and breeding; genomics; computer vision|
|Frost||Plant-microbe interactions; agricultural systems; modeling|
|Goyer||Improving nutritional value of crops; agricultural systems; plant-pathogen interactions; genomics; transcriptomics|
|Grunwald||Plant-pathogen interactions; agricultural and forest systems; genomics; population genomics (not available 2021)|
|Jaiswal||Plant stress (environmental); agricultural systems; model systems; transcriptomics; data curation (not available 2021)|
|LeBoldus||Plant-pathogen interactions; forest systems; genomics; molecular biology|
|Leiboff||Plant development, visualization; transcriptomics|
|Liston||Plant evolutionary biology; plant ecology; natural systems; genomics; genetics; population genomics|
|Megraw||Plant gene expression; model systems; transcriptomics; bioinformatics (not available 2021)|
|Mundt||Plant-pathogen interactions; agricultural systems; epidemiology; plant molecular breeding; computational modeling (not available 2021)|
|Myrold||Soil microbial communities; ecosystems; nitrogen cycling; microbial ecology; microbiomes; genomics (not available 2021)|
|Nackley||Pest management, plant propagation, plant nutrition, soil science, ecology, economics, and policy (not available 2021)|
|Naithani||Biocuration, data management, FAIR data principles, big data analytics|
|Sharpton||Animal-microbe interactions; model systems; animal health; microbial ecology; microbiomes; genomics|
|Spatafora||Fungal evolutionary biology; fungal ecology; insect-fungal interactions; genomics (not available 2021)|
|Tyler||Plant-pathogen interactions; agricultural systems; evolution; genomics; transcriptomics (mentor is Dr. Kronmiller in 2021)|
|Uehling||Fungal evolutionary biology; fungal ecology; genomics|
|Vega-Thurber||Animal-microbe interactions; animal health; microbial ecology; microbiomes; genomics|
|Vining||Plant-microbe interactions; plant molecular breeding; genomics; transcriptomics; molecular biology (not available 2021)|
|Warren||(not available 2021)|
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.
The Anderson lab studies chemical signaling that occurs between plants and pathogenic bacteria. We are particularly interested in how pathogenic bacteria use plant-derived chemicals as cues to begin their infection process. We study signaling responses in both plant and bacteria using a combination of genetic, biochemical, metabolomic, transcriptomic and proteomic approaches.
Students will learn to process and analyze transcriptomic, proteomic or metabolomic datasets. This project may also include hands-on experience in collecting and preparing samples in the laboratory for data analysis.
Students will learn fundamental concepts in plant biotic interactions, genetics, biochemistry, cellular information flow, information flow and logic in data analysis pipelines, and statistics. Application of the scientific method to analyze data sets (i.e. hypothesis development and testing) will be emphasized throughout. Additional training in molecular biology and microbiology techniques may occur depending on the specific project.
The Busby lab studies plant microbiome ecology. Current research in the lab seeks to elucidate plant genetic and environmental factors structuring plant microbiome composition and function. In particular, we study how endophytes in the microbiome influence plant immunity. We strive to apply results of our research to the sustainable management of disease in agriculture.
Lab website: www.posybusby.com
Students will generate and analyze fungal and bacterial amplicon (short DNA sequences that can be used as barcodes to distinguish between organisms) sequence data representing plant-associated microbial communities (e.g., leaf, wood, seed microbiomes).
Students will learn to generate, manipulate, and analyze amplicon sequence data representing plant-associated microbial communities. Students will learn fundamental concepts in community ecology. Students may also contribute to data collection by participating in greenhouse and field experiments.
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. Two recent publication that exemplify the types of work the student will do are in Savory et al. (2017) and Davis et al. (2018) (view at Google Scholar).
Students will learn fundamental concepts in plant symbiotic interactions, information flow, and genomics. Students will use genomic data to learn the scientific process.
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.
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.
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
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.
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.
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.
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.
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.
The LeBoldus lab studies forest pathology and the interactions between fungal and oomycete pathogens with their plant hosts. We focus on dissecting the virulence mechanisms used by these pathogens. Our research relies heavily on generating, processing, and analyzing whole genome sequences of both the host and the pathogen to generate and test hypotheses related to population genomics, epidemiology, and evolution.
Lab website: https://bpp.oregonstate.edu/people/leboldus-jared
Students will learn to process and analyze whole genome sequence data. Tasks may include calling variants, constructing phylogenies, identifying genes of interest, and/or using a variety of population genomic measures to generate and test hypotheses with respect to evolution. Two recent publication that exemplify the types of work the student will do are in Muchero et al. (2018) and Brar et al. (2018).
Students will learn fundamental concepts in population genomics host-parasite interactions, information flow, and genomics. Students will use genomic data to learn the scientific process.
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.
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.
The Megraw lab is focused on understanding the transcriptional regulation of protein coding genes and microRNAs in plants. More specifically, we are investigating how certain combinations of DNA elements known as “cis-regulatory elements” control the production of RNA transcripts that are generated by RNA Polymerase II. We predominantly use the model plant Arabidopsis thaliana for this research, though we have investigations into the genomes of tomato and the medicinal plant Catharanthus roseus (C. roseus) underway as well. Computational biology plays a central role in all of our projects; we use data mining techniques including pattern recognition methods in order to carry out our studies.
Lab website: http://megraw.cgrb.oregonstate.edu/
Students will learn to process and analyze transcriptomic datasets. Tasks may include use of straightforward data mining and visualization methods to examine gene sets of interest.
Students will learn fundamental concepts in plant genetics, genomics, and data analysis. Use of a linux-based computing infrastructure and computational project pipelines will also play a central role in project training.
The Mundt lab studies epidemiology, pathogen population biology, and the genetics and durability of host plant resistance to disease.
A major, current project uses wheat stripe rust as a model for studying invasive plant, animal, and human pathogens that spread via long-distance dispersal. Questions being addressed include issues of spatial scaling, effects of landscape heterogeneity, and influence of initial epidemic conditions on disease spread. This project involves large-scale field experiments and a variety of computational approaches.
A second major project is in collaboration with cereal breeding colleagues. This project seeks to use quantitative analyses to elucidate genetics of resistance to several diseases of complex inheritance, and to develop molecular breeding strategies for cereal diseases of importance in the Pacific Northwest.
Lab website: https://bpp.oregonstate.edu/people/mundt-chris
Students can choose one of two tracks. One track would involve the use of a computer simulation model to study factors that influence the spread of disease in space and time, and how disease control practices might limit this spread. A second track would involve genetic analysis of field data collected from molecular mapping populations exposed to plant diseases.
Students will learn fundamental concepts in plant disease epidemiology or genetics of host plant resistance to disease. They will gain experience in using simulation models or in quantitative genetic analysis.
My research group studies the structure and function of microbial communities in soil. We have a strong focus on nitrogen cycling and the role of microbial communities in soil health.
Students would learn about diversity and function of soil microbial communities by analyzing bacterial and fungal amplicon (short DNA sequences that can be used as barcodes to distinguish between organisms) sequences, and shotgun metagenome (the collection of all genomes with a community of microorganism) sequences.
The Nackley lab promotes sustainable horticulture. Research projects reflect an integration of skills and knowledge in pest management, plant propagation, plant nutrition, soil science, ecology, economics, and policy. We develop opportunities for mechanization and technology to offset decreased available labor and increased labor costs.
Lab website: https://agsci.oregonstate.edu/users/lloyd-nackley
The intern will learn to how integrate sensing technologies to optimize plant production. Sensors will include ground-based measurements as well as aerial (drone) based sensors. The sensors will be used to quantify how changes in management practices, such as increasing or decreasing irrigation and fertilizers, will affect plant growth and performance.
Students will learn how to develop a hypothesis, design an experiment, collect and analyze data. There will be Extension opportunities to learn how to communicate data to local stakeholders and community members.
The Naithani lab is interested in exploring the underlying principles of systems-level gene-networks in plants and developing biological pathway models supported by high-quality biocuration, gene-orthology based predictions, and analysis of high-throughput genomic data.
Lab website: https://agsci.oregonstate.edu/users/sushma-naithani
The intern will utilize a data set available from Plant Reactome database that contains data for ~300 pathways from 79 species to analyze evolution of metabolic pathways across the broad spectrum of plants and photoautotrophs; identify unique pathways associated with different plant families and set of universally conserved pathways. The student will also learn the biocuration of genes and pathways which will help in data analysis.
The students will learn biocuration, data management, FAIR data principles, Big data analytics.
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.
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.
The Tyler lab studies the interactions between fungus-like oomycete pathogens and plants (primarily soybean). We use genome sequencing and mRNA sequencing extensively to reveal new insights into how oomycetes overcome plant immunity. Oomycetes use huge numbers of genes to blanket plant defenses with numerous virulence proteins. We are interested in which genes are most important for pathogen virulence. We are also interested in how pathogen switch up which virulence genes they are using in order to confuse plant defenses.
Lab website: http://bpp.oregonstate.edu/people/tyler-brett
Students will learn to process and analyze whole genome sequence data and mRNA sequencing data. Tasks may include identifying genes of interest, checking that the computer software has correctly identified the genes, constructing phylogenies, checking when and how actively the pathogens are using the genes, and predicting the functions of genes.
Students will learn fundamental concepts in plant pathogen interactions, genomics, and transcriptional regulation. Students will use genomic data to learn the scientific process.
The Uehling lab studies evolution of fungal interactions with other organisms, or symbioses. We focus on plant-root associated mutualistic (beneficial) fungi called mycorrhizae, human pathogenic (detrimental) fungi such as those associated with the disease Valley Fever, and bacteria that live inside of fungal cells, or endosymbionts. Our research relies heavily on generating, processing, and analyzing whole genome sequences and other genomic data to generate and test hypotheses.
Lab website: Lab website
Students will learn to process and analyze whole genome sequence data. Tasks may include: isolating and sequencing fungal DNAs; assembling, and analyzing fungal genomes and transcriptomes; identifying genetic variation; phylogenomic analyses; assessing population structure and diversity; and evaluating evolutionary hypotheses using population diversity metrics from whole genome sequencing data.
Students will learn fundamental concepts in fungal symbiotic interactions, information flow, and genomics. Students will use fundamentals of computer science to leverage genomics data to ask and answer biological research questions.
The Vining lab applies the tools of ‘omics and bioinformatics to specialty crops in order to breed plant varieties that are resistant to diseases and pests. Crops we work with include mint, hazelnut, potato, and black raspberry. We study and utilize natural genetic variation in plant collections and in breeding populations. Methods we use include whole-genome sequencing and assembly, genome annotation, gene expression analysis, and molecular marker discovery/development.
Students will learn to process and analyze whole genome sequence data. Tasks may include assembling genome sequences and identifying genes of interest. Students may also engage in work to study gene expression and validating markers for breeding purposes.
My research group studies insect physiology and behavior, with a particular focus on long-distance dispersal in flying fruit flies. The tiny fruit fly, though not typically heralded as a navigator, has an extraordinary capacity to maintain a constant heading for tens of kilometers in an extended flight. This long-distance dispersal is of enormous agricultural concern, both in the Pacific Northwest and worldwide, as some fruit fly species are potent crop pests. My group seeks to understand how fruit flies choose a flight heading, the sensory cues they use to maintain their course, and what factors influence variability across individuals. This work will provide a basis for effective pest management as well as fundamental knowledge about animal navigation. Our approach involves both laboratory experiments using custom flight simulators that enable real-time tracking as well as field experiments that study dispersal at a landscape scale.
Website: Google Scholar profile
The REEU student will conduct analysis of behavioral data of continuous flight orientation of the spotted-wing fruit fly, an agricultural pest. The student will develop a capacity for Python programming, as well as time-series analysis. Furthermore, the student will learn to query and manipulate large data sets via Unix shell commands. There may be additional opportunities to conduct laboratory experiments with flying flies. These experiments would provide students the opportunity to use Arduino devices and the ROS programming environment for real-time experimental control.