Current CAM Research Projects

This page is a list of our summer 2018 student research projects. Contact CAM Director, Dr. Magda Stolarska at for more information.

Advisor: Misha Shvartsman

Abstract: The mission of the Freshwater Society is to “to promote the conservation, protection and restoration of all freshwater resources.” As part of their mission, the Freshwater Society conducts research on water quality to inform policy. The Freshwater Society would like to examine the potential effects of mining on groundwater in Dakota County. The 7,973 acres UMore property was deeded to the University of Minnesota in 1948 by the Federal Government. The University of Minnesota has assessed the northern 5,000 acres to see where potential for mining is greatest and has compiled a data set that characterizes the current movement of groundwater from the property to the Vermillion River because it defines the slope of the water table and gives detailed information on the permeability and porosity of the material. Mining will change these conditions by removing the granular material, exposing the water table to surface temperatures, and changing the slope of the water table.  The Freshwater Society would like students to explore the question: How will removal of the economic deposits of sand and gravel impact water reaching the Vermillion River, a nearby trout stream which depends on continuous cold-water discharge from groundwater?

Students working on this project will develop a mathematical model and a simulation tool based on the finite difference method.  The purpose of the tool will be to predict how changes to the groundwater temperature from mining and the driving forces that affect groundwater flow affect the Vermillion River.  

Background: Students must have successfully completed or be in the process of completing MATH 200 or MATH 210. Students should also be comfortable learning mathematical programming.

Student Researchers: Brad Walton, Phuong Ahn Vo, Erik Sundberg


Advisor: Rebecca GloverMolly Peterson

Abstract: Interested in math teaching or tutoring? This project will look at best tutoring practices, tutoring philosophies and how to best conduct peer tutor trainings. We will research best practices in peer tutoring and teaching mathematics and use this research to create tutoring videos, PowerPoints, and worksheets to be used in training future MaRC tutors.

Background: MATH 200 - Multivariable calculus. Preference will be given to Mathematics Education Majors.

Student Researchers: Rachel Reinecke, Hannah Rumon


Advisor: Kenichi Okamoto

Abstract: Conservation decisions rely on a risk-assessment tool known as population viability analysis. Briefly, this analysis aims to characterize the likelihood that a given species will go extinct in a certain number of years. These analyses were traditionally developed using queueing theory and Poisson process models to characterize births and deaths in a population. However, conservation biologists are increasingly looking to inject greater biological realism into these analyses. For example, biologists use deterministic mathematical models to characterize the growth of individual organisms. These physiologically-structured models allow us to link resource (e.g., food, nesting space, etc...) scarcity to weight gain, survival and reproduction in individual organisms. While deterministic versions of these models can be scaled to the population level using partial differential equations, scaling these models to populations in a stochastic fashion has proven challenging. Recently, I developed an open-source computational library ( that should let us surmount this difficulty. The student will look to apply this framework to develop and implement stochastic models linking individual growth to population persistence. We will be using a high-performance computing platform using graphics processing units (also used in cryptocurrency mining). We will consider as our case study Caiman crocodilus apaporiensis, a highly endangered subspecies of South American alligators.

Background: Probability and/or Statistics course, an interest in learning mathematical programming. This project will start in the summer, but preference will be given to students who are also willing to put in a few hours a week in the 2018-2019 academic year.

 Student Researchers: Alice Coffman, Erin Reysack

Advisor: Mike Axtell

Abstract: Summer research students will receive actual claims data from a local insurance company. Claims data will be analyzed in hopes of discovering exploitable patterns that will allow actuaries to predict which claims will prove most difficult to close. The research team will work with a retired consulting actuary and will present their results to upper-level management at the local insurance company.

Course Work Required: MATH 114 and some knowledge of Excel desired.

Student Researchers: Annika Iverson, Dominique Stewart, Joseph Bricher

Advisor: Christina Knudson

Abstract: R package glmm is available on CRAN, but software development is never finished. Potential projects involve adding parallel processing, profiling R code and improving its efficiency, tweaking the method to improve the importance sampling distribution, and writing vignettes to explain the package to users. The summer may also involve finishing other aspects (incorporating a new distribution for the response, adding weights). 

Course Work Required: B+ or higher in  Math 313, Stat 314, Stat 333, and at least one computer science course. 

Preferred: Experience writing functions in R, experience in C, experience using git and Github.

Student Researchers: Sydney Benson, Kristen Rutschke

Advisor: Cheri Shakiban

Abstract: In this project, the students will learn about the statistical method called Mahalanobis distance, which is a statistical metric used to classify different 3D objects.  In particular, we will look at 3D tumors to decide if they are cancerous or benign.  

Course Work Required: Math 200 - Multivariable calculus, some statistical knowledge and ability to write (or willing to learn) simple programs in Mathematica or Matlab. 

Student Researchers: George McGivern, Jordan Olson, Suzanna Truong, Riley O'Niell, Seth Glidewell, McKenna Mayne


Advisor: Paul Ohmann

Abstract: Building upon previous work on grazing herds and predation risk, this goal of this project is to model a Predator-Prey-Food ecosystem. In particular, we seek to explore the conditions and extent to which prey will risk themselves in an effort to acquire food.

Course Work Required: MATH 200-Multivariable calculus, some statistical knowledge and ability to write (or willing to learn) simple programs in Mathematica or Matlab.

Qualifications: Students should have completed PHYS 112 as well as CISC 130 (or have Matlab experience), along with a good GPA and an ability to work independently with faculty guidance.

Hours: up to 25 per week (flexible times)

Duties: Background reading and synthesis of relevant papers on this subject. For example: 

  1. Nicholas J. Ose & Paul R. Ohmann (2017), “The selfish herd: Noise effects in Local Crowded Horizon and Voronoi models”, Journal of Theoretical Biology 424:84-90
  2. Radka Sarova, (2010), “Graded leadership by dominant animals in a herd of female beef cattle on pasture”, Animal Behaviour 79:1037-1045.
  3. Construct Matlab predator-prey models, building upon previous work. We will need to define and keep track of relevant quantities, such as individual positions and food intake. We will need to carefully analyze these simulations in order to interpret the results.
  4. Write a final report summarizing the project, following CAM’s guidelines for final reports.
  5. Present your results at the Inquiry at UST Poster Session in September. 

Student Researchers: John Scheele 

Advisor: Eric Rawdon

Abstract: My group works with knots, usually in the context of real physical knots, like in proteins and DNA.  We are currently working on using neural networks to recognize knots, looking at knotting transitions that (are conjectured to) occur in subatomic particles, writing software to distinguish different types of links, and looking at random configurations that model biological chains with some
inherent thickness.  New students would work on extending these different studies.  Coding is required (usually we work with C/C++ and Perl in Linux).  One does not really need to know those particular languages, or maybe even any computer language, but it helps a lot to know some language or to have a heavy desire to learn to code. Generally, we generate a bunch of data on a subject and then try to make sense of what we see.

Student Researchers: Matt Ward, Brandon Tran, Zach Sorenson

Advisor: Magda Stolarska, Afshan Ismat

Abstract: The caudal visceral mesoderm (CVM) is a group of cells that migrate along the entire length of the Drosophila embryo.  These cells migrate as a loose collective of individual cells, making connections with both their extracellular environment and each other.  The migration of these cells will be examined through fluorescence microscopy and mathematical modeling and numerical after genetically altering the extracellular environment in various ways.

Background: BIOL 208, MATH 113, willingness to learn some mathematical programming.

Student Researchers: William Hamilton, Dillon Kough

Abstract: Our understanding of the composition of astronomical objects ultimately derives from our ability to measure the spectral properties of elements in the lab.  In order to improve this understanding, astronomical studies require laboratory data of increasing quantity and quality.  To help address this requirement, this study aims to improve the automation of spectral line analysis.

Time Frame: Summer 2018

Faculty Advisor: Mike Wood

Qualifications: Students should have completed Phys 112 as well as CISC 130/131 (or have prior Matlab or Python experience), along with a good GPA and an ability to work independently with faculty guidance.

Hours: up to 25 per week (flexible times)


  1. Background reading and synthesis of papers on this subject.  For example, past research on automation techniques and the application of atomic spectral data:
    1. Lindner, R. P. et al. 2015, “Autonomous Gaussian Decomposition”, Astronomical Journal, 149 138
    2. Chartrand, R. 2011, “Numerical Differentiation of Noisy, Nonsmooth Data”, ISRN Applied Mathematics, 164564
    3. Wood, M. P. et al. 2014, “A Laboratory log(gf) Measurement of the Ti ii 15873.84 Å H-band Line in Support of SDSS-III APOGEE”, Astrophysical Journal Letters, 787, L16
    4. Build spectral line analysis code in Matlab/Python.  We will work to incorporate mathematical and computational techniques to perform line parameter estimation, non-linear least squares fitting of line profiles, and error calculations.  We will need to work with multiple, large spectral datasets and keep track of resulting line strength measurements and corresponding uncertainties.  We will also benchmark the analysis code through comparisons to earlier results.
    5. Write a final report summarizing the project, following CAM’s guidelines for final reports.
    6. Present your results at the Inquiry at UST Poster Session in September.

Student Researchers: Robert Klemm

Abstract: Coming soon

Faculty Advisor: Arkady Shemyakin

Student Researchers: Sydney Benson, Regina Burroughs, Jessica Mohr, Thomas Vlasak