RTG Research Experience for Undergraduates

As a member of the RTG-REU, you will    

- work with RTG faculty, postdocs, and graduate students on data driven modeling and applications;

- learn mathematical ideas fundamental for data science;

- learn to use basic tools for data-intensive scientific computing;

- learn to communicate science effectively;

- learn about applying to graduate schools.                                            

Qualifications: We expect all REU students to have completed a standard calculus sequence (including vector calculus) and have had some exposure to linear algebra and differential equations at the time they start the REU; some of our projects may benefit from additional background knowledge. Experience with some form of programming, e.g., Matlab or Python, is helpful but not required. (If you're interested but unsure whether you qualify, just ask!)

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APPLICATION. We have two sources of funding:

• UA's Undergraduate Research Opportunities Consortium (UROC) supports students from groups traditionally underrepresented in graduate studies in STEM; and

• the National Science Foundation (NSF) provides support for US citizens and permanent residents.

Unfortunately, we can only support students who are eligible for at least one of the two funding sources above.  The two options differ mainly in funding source; all RTG research and training activities are open to both groups (UROC students may have additional activities), and both sources of funding are open to non-UA students.

• UROC application (deadline 2/1/23): apply to the Summer Research Insitute and state that you are interested in working with the Data Driven Discovery REU

• NSF application (rolling deadline) we will continue to review applications until all positions are filled

Note that while UROC does not require letters of recommendation, we do ask for 2 letters from all applicants for NSF funding. If you apply to UROC, you will also be considered for our NSF funding provided you arrange for 2 letters to be sent to us (not UROC!) at the same time.

Please email Prof. Laura Miller (lauram9@math.arizona.edu) if you have any questions.  Letters of recommendation may be emailed to rtg-admin@math.arizona.edu.

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MORE DETAILS ON THE PROGRAM

The Summer 2023 RTG-REU program will start in early to mid June, and end in early August. (We are well aware that some of you may have exams in early June, and will be happy to work around your schedule as needed.)

Full-time, 40 hour per week commitment required.

Modality: for Summer 2023, we will have a hybrid modality. You are welcome to participate remotely or join us in Tucson. Note, however, that you will need to make your own housing arrangements.

Eligible participants will receive a stipend so they can focus on their summer research.

Recent past projects include......       

• Automated segmentation of spine X-rays for scoliosis detection (Prof. Marek Rychlik)

• Stochastic Models of Thermostatically Controlled Multi-Unit Buildings (Prof. M. Chertkov) 

• Reconstruction of vector fields from weighted and unweighted vectorial Radon transforms (Prof. Leonid Kunyansky)

• Machine learning and medical imaging (Prof. Ali Bilgin)

• Optimal planning for self-driving cars (Dr. Christian Parkinson)

• Neural data analysis and network dynamics (Prof. Kevin Lin)

• Using 3D morphological data in multiphysics simulations (Prof. Laura Miller)

• Circadian rhythms and wearable data (Prof. David Glickenstein, Prof. Kevin Lin, and Dr. Avinash Karamchandani)         

Depending on interest and availability, we can also arrange projects with other RTG faculty, postdocs, and graduate students. Let us know your areas of interest and/or whom you'd like to work with in your application, and we'll do our best to match you with a suitable advisor and project!