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Engineering | SENSIP

SenSIP Sensors and Machine Learning Workforce Development Program


NSF REU Award 1659871

NSF REU Award 1659871

     NSF IRES Award 1854273

               NSF RET Award 1953745

For Applications Visit REU, IRES Cyprus, IRES DCU AND RET


Four workforce development programs operate under the auspices of the Sensor Signal and Information Processing (SenSIP) center which is also an Industry-University Cooperative Research Center (I/UCRC) sponsored by the National Science Foundation (NSF) and I/UCRC industry members. The first program is an NSF Research Experiences for Undergraduates (REU)  site which has trained more than 37 students working on sensor hardware design and machine learning algorithm development. The second program is the NSF International Research Experiences for Students (IRES) site which is collaborative with the University of Cyprus and is focused on sensors and machine learning for energy systems.  The third program is the NSF International Research Experiences for Students (IRES) site which is collaborative with Dublin City University (DCU) on sensors and machine learning for health and wearable devices. And the fourth program is the NSF Research Experiences for Teachers (RET) program that started in June 2020. This program embeds teachers and community college faculty in SenSIP machine learning projects. Another state-funded program in which SenSIP is a partner is MedTech ventures. Our partner MedTech works on training medical technology students, entrepreneurs, and engineers to create smart medical solutions for preventive healthcare.

Relevant Publications

  1. S. Naik, N. Vaughn, A. Spanias, K. Jaskie,  Quantum classification for synthetic aperture radar, SPIE Defense and Commercial Sensing, National Harbor, Maryland, April 2024    (IRES 2023).
  2. L. Miller, G. Uehara, A. Spanias,  “Quantum Image Fusion Methods for Remote Sensing, ” IEEE Aerospace Conference, Big Sky, Montana, March 2024. (REU 2023).
  3. Billingsley, G., Dietlmeier, J., Narayanaswamy, V., Spanias, A., & O’Connor, N. E. (2023, October). AN L 2-Normalized Spatial Attention Network for Accurate and Fast Classification of Brain Tumors in 2D T1-Weighted CE-MRI Images. In 2023 IEEE International Conference on Image Processing (ICIP) (pp. 1895-1899). IEEE.  (IRES 2022).
  4. L. Miller, G. Uehara, A. Sharma, A. Spanias,  “Quantum Machine Learning for Optical and SAR Classification,” IEEE DSP Conference, Rhodes, June 2023   . (REU 2023).
  5. Glen Uehara, Jean Larson, Wendy Barnard, Michael Esposito, Filippo Posta, Maxwell Yarter, Aradhita Sharma, Niki Kyriacou, Matthew Dobson, Andreas Spanias, “Undergraduate Research and Education in Quantum Machine Learning,”  IEEE FIE 2022, Upsala, Oct. 2022   (REU, RET IRES 2022).
  6. M. Esposito, S. Rao, V. Narayanaswamy, A. Spanias,  “COVID-19 Detection using Audio Spectral Features and Machine Learning,” Asilomar Conference on Circuits, Systems and Computers, Monterey, Oct. 2021.
  7. A. Spanias, E. Forzani, G. Raupp, W. Barnard, M. O’Donnell, J. Larson, N. O’Connor, N. Dunne, S. Daniels, S. Little,  “The ASU-DCU International Research and Workforce Development Program on Sensors and Machine Learning,” 2022 IEEE 13th International Conference on Information, Intelligence, Systems & Applications (IISA), Hybrid Conference, July 2022.  (IRES 2022).
  8. M. Esposito, G. Uehara, A. Spanias, Quantum Machine Learning for Audio Classification with Applications to Healthcare, 2022 IEEE 13th International Conference on Information, Intelligence, Systems & Applications (IISA), Hybrid Conference, July 2022. (REU 2022).
  9. K. Jaskie, J. Larson,  S. Rao, J. Blain Cheristen, M. O’Donell, A. Spanias, “Research experiences for Teachers (RET)  in Machine Learning,” IEEE FIE 2021, Oct. 2021.
  10. 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.
  11. J. Martin, K. Jaskie, Y. Tofis, A. Spanias,”  PV Array Soiling Detection using Machine Learning Fault Detection,”    Proc. IEEE IISA 2021, July 2021.  (IRES Project)
  12. A. Spanias, Machine Learning Workforce Development Programs on Health and COVID-19, Proc. IEEE IISA 2020, Piraeus, Keynote Speech, July 2020.
  13. Jaskie, J. Martin, S. Rao, W. Barnard, P. Spanias, E. Kyriakides, Y.Tofis, L. Hadjidemetriou, M. Michael, T. Theocharides, S. Hadjistassou, and A. Spanias, IRES Program in Sensors and Machine Learning for Energy Systems, Proc. IEEE IISA 2020, Piraeus, July 2020.
  14. A.Spanias, J. Blain Christen, T. Thornton, K. Anderson, M. Goryll, H. Arafa, U. Shanthamallu, E. Forzani, H. Ross, W. Barnard, S. Ozev, “The Sensor Signal and Information Processing REU Site, ” Proc. 2018 ASEE Annual Conference, Salt Lake City, Utah, June 2018.
  15. A.Spanias and J. Blain Christen, “A STEM REU Site On The Integrated Design of Sensor Devices and Signal Processing Algorithms’,” Proc. IEEE ICASSP 2018, Calgary, April 2018.


Articles on SenSIP Workforce Programs


IRES Dublin




IRES Cyprus


The REU program is funded by NSF CISE award 2349567.

The IRES project by NSF Award 1854273.

The RET project is funded by NSF Award 1953745.