Research in Computational Science
The COMS-REU program provides prospective undergraduates with a 8-week program of study focused on developing practical research skills in the area of computational science. Students learn a blend of computational approaches from the fields of computer science and mathematics with a focus on solving problems in the applied sciences (in particular, biology and physics). Accepted applicants are provided on-campus housing for the duration of the 8-week program (during the summer) along with a $4K stipend.
Students develop skills in computer programming (Python, GitHub, JupyterLab), high-performance computing (Linux, SGE, MPI) and mathematical modeling (differential equations - numpy/scipy/COMSOL, machine learning - SciKitLearn/Keras, and model validation) during the first half of the program. During the second half of the program, students develop practical research skills including experiment/simulation design/testing and data generation/presentation by working closely with program faculty on applied research projects from scientific domains.
How to apply
Please use the link below to complete an REU application. After you click the link, you will need to create an account by selecting the "Create New Account" option displayed beneath the Log In button. Attention MTSU students: when creating the account, do NOT use your MTSU email account. You must use a non-MTSU (personal) email account.Click here to apply for Summer 2021 Click here for additional program information [PDF]
|Sunday, June 6||Orientation: Check in to dorm, connect to WiFi and learn about food options||Dr. Leander|
|Monday, June 7||Orientation: Meet with project mentors and other REU students, complete paperwork, and tour campus facilities||Faculty Mentors|
|Monday-Friday||Research with mentors||Student Teams and Faculty Mentors|
|Tuesdays & Thursdays||Workshops on research methods and professional development||Faculty Mentors|
|Wednesdays||Lunch with research groups||Research Groups|
|Fridays||Project team meeting (progress checks)||Faculty Mentors|
Middle Tennessee State University
The COMS-REU program takes place on the campus of Middle Tennessee State University in Murfreesboro, TN. Dormitory housing for students is provided on the campus, and the city bus system provides access to restaurants, shopping, etc. throughout the city of Murfreesboro. Some of these options are within walking distance of the campus. From the campus, it is a 45-minute drive to the capital city of Nashville and planned social events provide opportunity for students to visit Nashville while in the area.
Click below to obtain more information about our campus, city, and the surrounding region:MTSU Directions Interactive Map Murfreesboro Nashville State Parks
|January-March||Develop workshop materials, design surveys|
|March||Contact accepted applicants and arrange travel|
|April||Conduct pre-REU surveys|
|June 7||Student orientation|
|June 8 - June 11||Python bootcamp 9:00-11:00, 1:00-4:30 research meetings|
|June 8 - July 30||Research investigations|
|July 5||Conduct midpoint survey|
|July 30||Research celebration and conduct post-survey|
|August-||Prepare results for publication and present results at professional meetings|
|Python Boot Camp Part 1: Introduction to programming in Python||Dr. Phillips||Object-oriented programming, functional programming, logic, structuring code, readability, sharing code, plotting, Python libraries, Jupyter notebooks|
|Python Boot Camp Part 2: Direct optimization and analysis in Python||Dr. Robertson||Motion prediction using Euler and Euler-Cromer method, simulated annealing, neural networks, data classification|
|Modeling with differential equations||Dr. Ding||Interpreting ODE models, parameterization, model fitting, stability analysis, sensitivity analysis, model comparison, and numerical simulation|
|Modeling with stochastic differential equations||Dr. Leander||Stochastic integration, deriving an SDE from a discrete stochastic model, and computational methods for solving SDEs|
|Molecular modeling||Dr. Phillips||Molecular modeling and simulation (GROMACS, bash), post-simulation analysis with statistical and machine learning techniques (MDAnalysis, Keras/TensorFlow, and SciKitLearn), and visualization of molecular models and simulations (Pymol, VMD)|
|Documentation and reproducibility in computational research||Dr. Phillips||Collaborative coding, code versioning and code repositories|
|Tips for preparing a research article for publication||Dr. Brinthaupt (LT&ITC)||Finding the appropriate journals and writing the manuscript in a professional way|
|Tips for building and maintaining professional collaborations||Dr. Brinthaupt (LT&ITC)||Finding collaborators and resolving problems|
|Tips for getting the most from professional meetings||Dr. Brinthaupt (LT&ITC)||Creating a professional presentation, presenting and networking|
Dr. Wandi Ding
Mathematics (PI)Website Project 1 (Summer 2019)
Dr. Ding's research interests include mathematical biology, computational biology, mathematical modeling, ordinary and partial differential equations, difference equations and hybrid systems. Applications include population dynamics, disease modeling, natural resource management, systems biology and quantum biology.
Dr. Ding's research focuses on understanding the spatial and temporal patterns that arise in dynamic biological systems and, when possible, finding the best way to control the system.
Dr. Rachel Leander
Dr. Leander uses mathematical modeling and data analysis to study cellular processes. She is especially interested in the analysis of cellular decisions, including division and death. She collaborates with biologists to build detailed, mechanistic models of intracellular processes as well as top-down, qualitative models of processes occurring at the level of an individual cell or population. In coupling these models to biological data, she develops custom numerical methods.
Dr. Joshua L. Phillips
Computer Science (co-PI)Website Project 1 (Summer 2019) Project 2 (Summer 2019)
Dr. Phillips' research interests are in computational biophysics and cognitive science, but primarily on the development of novel computational methods for addressing existing scientific or engineering problems related to molecular or structural biology. His background is in machine learning and neural networks, so his work often employs or is inspired by algorithmic approaches from these fields.
Dr. William Robertson
Dr. Robertson's research interests cover a broad range of subjects in optics and acoustics explored both experimentally and through numerical simulation. Some key areas of current interest are acoustic and photonic band gap materials, acoustic metamaterials, fast and slow wave phenomena (including superluminal sound), surface electromagnetic excitations including surface plasmons and surface waves on photonic crystals, label-free biosensing, signal processing of speech and musical sound manipulation, and the design of innovative diffractive optics.