Graduate Mentor List

Mentors and projects!
Below is the list of graduate student mentors and a brief description of their research and opportunities to participate.  Please see the mentoring program page for details about the program format and goals.  This list is updated at the beginning of each semester (sometimes more frequently). If you are interested in a position, you can contact the graduate student directly, providing them with a brief introduction and a resume. You may also reach out to if you have any questions.
Graduate students: If you would like to update or add to this page, please contact Clare Rodenberg or Elise Heffernan at
Revisited Jan 2023

Who: Elise Heffernan,
1. This project is focused on primary research and seeks to complement my own boreal forest treeline research in the Arctic. A student would choose a topic (if possibly related to another major/minor, great!) and conduct primary research, create an annotated bibliography and write a report on the topic throughout the term. The goal is to get a broader, multidisciplinary understanding of my research area. The student will gain experience with primary source research and writing, and will have rather wide discretion in their chosen topics. I am particularly seeking students who are interested in social-environmental interactions.
2. This project focuses on running a boreal forest - tundra model across multiple sites in Alaska and Canada. Some coding experience would be beneficial (R or python) but we will teach you how to run the model so advanced students and those looking to increase their skills are welcome! We will work as a team and have many people running the model to get an array of simulations and outputs.
3. The project involves running an individual-based forest gap model for a site nearby in VA and investigating the use and accuracy of different tree allometries, including some terrestrial lidar scanner derived equations developed by a former UVA graduate student. The model will be run on Rivanna and we will be working together with a few other students to get things up and running in the beginning, especially. A strong coding background is not necessary but an interest in learning R and some python is essential. Even more importantly, some background coursework in terrestrial ecology or biology will be very helpful. This project will be advised by Amanda Armstrong, but direct emails to Elise Heffernan (eh9hg)
Openings: multiple

Who: Amanda Armstrong (
What: Allometry simulations using a Forest Model
The student will gain experience running a cutting edge forest model to simulate a forest in Virginia. We will compare how well the model does simulating trees with parameters measured in the field and taken from the literature as compared to parameters measured by ground-based lidar. The student will be running the model (python) and interpreting results (R). No coding experience necessary though at least exposure to coding preferred.
Forest Model Research
This research-based experience will involve a little bit of detective work. We will be developing a database of global forest model parameters, combing through literature and books to piece together in one place model parameterizations from around the globe. No modeling experience necessary.
Openings: 1-2 students
When: Spring 2023
How: Academic credit.

Who:  Mirella Shaban,
What:  Seeking a student with an adequate understanding of R to conduct weekly data visualizations and analysis of data from meteorological stations in Barrow, Alaska. Student will create figures and analyze data to catch anomalies or instrument errors and learn about micrometeorology in Arctic environments. Student can also pursue further knowledge in Arctic sciences if they please (please feel free to discuss your learning goals with me!). .
Openings: 1-2 students
When: Fall 2022 and Spring 2023
How: Academic credit and Volunteer

Who:  Kelsey Schoenemann,
What: I have LOTS of video data of bumble bee activity from hives that were located in different habitats. This project involves transcribing video data into spreadsheets and potentially using video analysis (i.e., scene change detection algorithms) to streamline the process. Students would work (for credit or volunteer) as a team with another student on the project (who is receiving academic credit).
Openings: 1-2 students
When: Fall 202 and Spring 2023
How: Academic credit and Volunteer

Who: Stephanie Petrovick (
What: One project involves germinating seeds in a greenhouse, with the seedlings needing to be checked with decreasing frequency over the year - depending on when the seedlings stop appearing, this could take 6-12 months. It mostly consists of watering the plants, checking for seedlings, and identifying the species of those seedlings.
Further work that I need help with includes sorting and weighing dried biomass samples and sieving and weighing soil samples. Biomass samples are sorted based on categories rather than individual plant species, and the soil samples are being weighed alongside any roots or rocks that were in the sample in order to measure bulk density.
When: Fall 2022 and Spring 2023
How: Academic credit

Who:  Mirella Shaban and MacKenzie Nelson
What: Hiring an undergraduate student with GIS experience to commit ~1-3 hours a week for sourcing data and mapping it in arcGIS. Research is centered around a remote Alaskan community. The student will be responsible for scouring the internet for GIS data and creating visualizations and analyses of the data with direction from the mentors. Students will mostly have creative freedom throughout this mentorship to explore relationships they are interested in within the data, with some guidance from the mentors when necessary.
Openings: 1 student
When: Fall 2022 (with possible extension into Spring 2023)
How: Academic credit and Volunteer

Who: Rong Li (
What: We are looking for undergraduate students to collect and/or process terrestrial LiDAR scanning data. The LiDAR data provides detailed 3D structure of trees where individual leaves can be clearly seen. Students will learn about tree structure and LiDAR, and can conduct research related to tree structure if interested. Data collection will ideally be conducted biweekly at a forest 35 min from campus, and the measurement will take ~1.5 hr. Data processing involves using a software to register scans (1 hr for each set of measurements), and may involve separating individual trees using software if students are interested in relevant research. No prior experience is required.
Openings: 1-2 Students
When: Spring 2023, Summer 2023
How: Academic Credit or Volunteer