The RET Site Computational Mathematics for Data Science within the Department of Mathematics at Emory University has several funded research opportunities for in-service high-school teachers. The program will be held in conjunction with the Emory Research Experience for Undergraduates that provides additional funding for current students.

Our site’s core activity is a six-week on-site research and training experience at Emory University in Atlanta, GA. This activity is preceded by a three-week virtual pre-REU/RET phase that focuses on team building, on-demand teaching of advanced mathematical concepts, and finalizing the design of the research projects. During the on-campus phase, the teams will work on their research project, engage in weekly seminars, and receive mentorship from faculty, postdocs, and graduate students. The pre-REU/RET phase will start on May 23 and the on-campus phase will begin on June 13 (subject to covid restrictions).

We particularly encourage women and teachers from underrepresented groups to apply.

Supported participants will receive a stipend of $6,000.

**Eligibility**

- Teachers must be currently teaching K-12 at a US institution and have US citizenship or permanent residency.

- Participants need to be able to commit some time during the pre-REU/RET phase between May 23 and June 12 (no more than one-hour synchronous activity per week) and must be available full-time between June 13 and July 22.

** Application **

All applications received by April 10 will receive full consideration. Application materials must include:

- Resume / Curriculum Vitae (including a list of relevant coursework and experiences)

- Statement of Interest (discussing experiences and skills related to the program, motivation for the program,...)

- Two Reference Letters, one of which should be from your school principal or supervisor (to be submitted by the reference writers at this site

** 2022 Theme: Models meet Data **

Computational and data-enabled science has become the third pillar of science, completing theory and experimentation. One simplified way to organize the vast number of approaches in this area is by the specificity of the model and the amounts of data required. This idea is often used to categorize approaches as model-based and data-driven approaches. Model-based approaches calibrate unknown parameters in integral or differential equations derived from first principles using measurement data; a process known as data assimilation. Advances in theory and computation have impacted many applications, e.g., numerical weather prediction, medical applications, and storm surge modeling. Due to the high specificity of the modeling, one often needs only a few measurements to predict reliably. By contrast, data-driven approaches use a fairly generic model (e.g., a neural network) that is trained with larger amounts of data. Their use has been successful in applications in which the relevant first principles are ill-defined, or a consistent model is mathematically and computationally intractable for real problems. Despite achieving impressive results, with some notable exceptions, these models are data greedy and often lack understanding and interpretability. In our projects, the teams will apply, compare, and combine data-driven and model-based approaches to solve real problems.

For additional information about the theme and projects visit http://www.math.emory.edu/site/cmds-reuret/