NSF-RET-1953745-Comprehensive-Activity-Report-2020-26

The SenSIP RET program allows local, STEM-focused K-12 teachers and community college faculty to participate in cutting-edge sensor and machine learning research.   Teachers engage in curriculum development and design and implement lesson plans on project research focusing on areas of machine learning for several applications including health, sustainability, and energy.

RET Activities 2025 – 2026

Program Overview

The RET program is hosted by the SenSIP center which is an industry-university cooperative research center (I/UCRC) sponsored by the National Science Foundation (NSF) and its industry members. SenSIP’s mission is to develop sensor and machine learning innovations for a variety of applications including health, security, and sustainability. Specific applications include:

• Machine Learning for Energy Studies and Solar Arrays
• Signal processing and AI algorithms for Health Diagnostics
• Machine Learning and DSP for Communications
• Imaging and Vision Applications to Smart Cameras
• Machine Learning for Flexible Sensors
• Quantum Machine Learning

We are seeking K-12 and community college teachers that are interested in engaging with the SenSIP Center in these areas through a program supported by NSF that provides stipends for teacher training.  The summer Research Experience for Teachers (RET) program in the SenSIP laboratories at the ASU campus will provide an opportunity for teachers to directly participate in research as well as develop a related lesson plan that they will take back and incorporate into their own classroom in the coming academic year.

Program Benefits

• $8000 ($6k +$2k) stipend. The additional $2k requires lesson implementation and research report submission.
• Teachers/Instructors work on projects with ASU Professors and Graduate student researchers on cutting edge topics.
• Teachers/Instructors learn how to program machine learning algorithms for analytics.
• Teachers/Instructors learn to work sensor circuits for health related and other applications.
• Teachers/Instructors form lesson plans on machine algorithms and implement in their classes.

Program Eligibility

• Open to U.S. citizens or U.S. permanent residents.
• High school teachers or community college faculty in STEM areas (Computer Science, Engineering, Physics, Chem, Bio, Math).
• Ability to work 40 hours per week for the entire 5-week program (NSF requires 100% engagement in June)
• Teachers (or those serving students) from diverse groups and underrepresented STEM programs are encouraged to apply.

Program Application

Applications and will require the following:

1. A completed online application.
2. A brief resume/CV.
3. A professional letter of recommendation (department chair, principal, colleague, etc.).

How to Apply

Complete the online application form.

As part of the application process, be prepared to upload the following in PDF format:

• A brief resume/CV
• A professional letter of recommendation(department chair, principal, colleague, etc.)

Additional Program Information

Publications

Larson, J.S., Blain, J., Gulick, D., Uehara, G.S. and Spanias, A., 2026, July. Educator Research Immersion in Sensors and Machine Learning for AI Workforce Development. In 2026 17th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-6). IEEE.

Jean Larson, Tanay Patel, Glen Uehara, Niraj Babar, Nandika Goyal, Deep Pujara, and Andreas Spanias., Integrated REU-RET Workforce Training Model in Signal Processing, AI and Quantum Computing, IEEE Asilomar Conference on Signals and Computers, Monterey, Oct. 2026

Larson, Jean S., Anna M. Haywood, Frank S. Marfai, Milton E. Johnson, Niraj Anil Babar, Glen Uehara, Judith Klein-Seetharaman, Daniel Gulick, Jennifer Blain Christen, and Andreas Spanias. “WIP: Bringing Classical and Quantum Machine Learning in Biomedical and Environmental Applications to the Community College Setting.” In 2025 IEEE Frontiers in Education Conference (FIE), pp. 1-5. IEEE, 2025.

Larson, Jean S., Megan O’Donnell, Kristi Lynn Eustice, Carolyn Aitken Nichol, Kristen Jaskie, Andreas S. Spanias, Kimberly Farnsworth, Jennifer M. Blain Christen, and Mi Yeon Lee. “Lessons Learned from Evaluating Three Virtual Research Experiences for Teachers (RET) Programs Using Common Instruments and Protocols (Evaluation).” In 2021 ASEE Virtual Annual Conference Content Access. 2021.

Jaskie, K., Larson, J., Johnson, M., Turner, K., O’Donnell, M., Christen, J.B., Rao, S. and Spanias, A., 2021, October. Research experiences for teachers in machine learning. In 2021 IEEE Frontiers in Education Conference (FIE) (pp. 1-5). IEEE.

Spanias, A., 2020, July. Machine learning workforce development programs on health and COVID-19 research. In 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA (pp. 1-4). IEEE.