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The Midwest (formerly Michigan) Research Experience for Graduates (MREG) is a workshop designed to provide early graduate students in pure, applied, and interdisciplinary mathematics an opportunity to engage in research with their peers, and to network with early-career faculty and professionals working in these fields. At the same time, we aim to promote and help start conversations around various initiatives and issues centered on diversity, equity and inclusion (DEI).
The two-week program splits students into small groups who will work closely with a project mentor. During the first week, students will learn the background material as guided by their group's mentor. The second week will be focused on working on the project at hand. At the end of the workshop, groups will give presentations about their project and methodology. The projects will be in pure, applied, and interdisciplinary mathematics, and we have invited mentors working in commutative algebra, algebraic geometry, topology, dynamics, differential equations, probability and machine learning. We plan to have a graduate student or postdoc assistant in each group.
MREG 2023 will run from in-person July 24th through August 4th due to generous support from the NSF. We would also like to acknowledge additional financial support from the University of Michigan through MCAIM, the Rackham Allies program, and the Department of Mathematics.
Applications are due May 19th, 2023.
Research Experience for Early Graduate Students
There will be working groups of 3-5 graduate students led by early career mathematicians with some groups also having UM faculty as mentors. The participants will be working on accessible research problem(s) and are expected to commit to the project for the full two week period. The exact workload and the meeting times will vary for each project and will be flexible based on the preferences of the group members and the group leader.
The list of available projects is given below.
List of Research Projects
- Caleb Ashley - Geometric Structures & Curves on Surfaces
Home Institution: Boston College
Website: https://www.bc.edu/bc-web/schools/mcas/departments/math/people/faculty-directory/Caleb-Ashley.html
Project Description: Click here.
Prerequisites: N/A
Final Report: Here
- Keisha Cook - Utilizing Stochastic Modeling Techniques in Interdisciplinary Studies
Home Institution: Clemson University
Website: https://drcookdoesmath.weebly.com/
Project Description: Click here.
Prerequisites: See description
Final Report: Here
- Neel Patel - Using Analysis and PDE to Study Traffic Flow
Home Institution: University of Maine
Website: https://sites.google.com/view/neelpatelmath/home
Project Description: Tools from analysis and partial differential equations can lend insight into flows of various kinds: fluids, animal flocking, etc. In this project, we will apply these tools to study the dynamics of traffic flow. One of the key problems to analyze is the development of shock waves due to changes in speed limits or traffic density or a red light. We will begin by understanding the one-dimensional model (akin to a one lane road) from both the continuous density perspective and the discrete "car following" model perspective. With this understanding, we will develop and analyze differential equations modeling various real world traffic scenarios (red/yellow/green lights, flow source points from an exit/entrance, etc.) and explore how a multilane model would be developed.
Prerequisites: First course in graduate analysis
Final Report: Here
- Yutong Wang - Multiclass Perceptrons
Home Institution: University of Michigan
Website: https://web.eecs.umich.edu/~yutongw/
Project Description: Click here.
Prerequisites: Basic linear algebra and probability. Basic programming with Python will be helpful, but not necessary. No prior knowledge of machine learning is expected.
Final Report: Here
Organizers:
Reebhu Bhattacharyya
Schinella D'Souza
Oliver Knitter
Max Lahn
Ben Riley
Lukas Scheiwiller
Kashvi Srivastava
Chris Stith
MREG organizing committee email: mreg-committee@umich.edu
NSF Grant Number: 2317485
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