TY - GEN
T1 - Machine Learning Workforce Development Programs on Health and COVID-19 Research
AU - Spanias, Andreas
N1 - Funding Information:
I. INTRODUCTION The Sensor Signal and Information Processing (SenSIP) center initiated several workforce research training programs in sensors and machine learning supported by a series of grants and supplements from the National Science Foundation (NSF). SenSIP is an NSF supported Industry-University Cooperative Research Center (I/UCRC) with several industry members which is on its 11th year. Industry members in the last 10 years included Freescale, Intel, LG, Lockheed Martin, National Instruments, NXP, ON Semi, Qualcomm, and Sprint. A series of small (SBIR size) companies also supported the center as associate or in kind members. The center received a grant from NSF, titled Research Experiences for Undergraduates (REU) [1], which in the last three years trained more than 30 undergraduate students (Fig. 1) from several universities in sensor devices and machine learning (ML) algorithms. The students produced sensor designs and developed algorithms for health and other applications. In 2019, the center received a second program titled International Research Experiences for Students (IRES) [2] that embeds graduate and undergraduate student researchers at the University of Cyprus (UCy) labs. This three year program trained six students in 2019 and 2020 that were co-advised by ASU and UCy faculty. The most recent program, titled Research Experiences for Teachers (RET) [3], was awarded by NSF in 2020 and will train approximately 30 teachers and community college faculty in ML. SenSIP also received a series of REU supplements for research on ML including studies on the identification of COVID-19 hotspots using networking theory and also detection of COVID-19 coughing patterns. In addition, to the above programs, SenSIP is partner in the MedTech ventures program [4] which has been funded by the Maricopa county. MedTech works on training medical technology students, entrepreneurs and engineers to create smart medical solutions for preventive healthcare. In this program, SenSIP has launched a series of training lectures in ML for health related applications.
Funding Information:
Abstract—This paper accompanies the keynote speech at IISA-2020 and describes federally funded workforce development research grants and supplements in the area of sensors and machine learning. These 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 REU site which has trained more than 30 students working on sensor hardware design and machine learning algorithm development. The second program is the NSF IRES site which is collaborative with the University of Cyprus and is focused on sensors and machine learning for energy systems. The most recent program funded by NSF is a 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. SenSIP also received NSF supplements to train students in using machine learning for COVID-19 detection.
Funding Information:
ACKNOWLEDGMENT We acknowledge the support of NSF through the REU award 1659871, RET award 1953745, IRES award 1854273 and also supplements through NSF awards 1540040. We also acknowledge the partnership with MedTech Ventures.
Funding Information:
VIII. CONCLUSION This paper is associated with the keynote speech at IISA 2020 and describes research programs that have been supported by workforce development grants. All programs train students in machine learning for sensor and health related applications. Several of the participants in the workforce development programs were able to publish some of their results and two participants have submitted patent pre-disclosures. The programs were assessed in terms of: a) enablement of the participants to produce new research results, b) the skill-building components in terms of ML and its applications, c) inclusiveness and diversity. Communications with most program participants have been maintained through emails, LinkedIn and social media. The most recent programs, supported through NSF supplements, are on COVID-19 research addressing networks and on sound analysis.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/7/15
Y1 - 2020/7/15
N2 - This paper accompanies the keynote speech at IISA2020 and describes federally funded workforce development research grants and supplements in the area of sensors and machine learning. These 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 REU site which has trained more than 30 students working on sensor hardware design and machine learning algorithm development. The second program is the NSF IRES site which is collaborative with the University of Cyprus and is focused on sensors and machine learning for energy systems. The most recent program funded by NSF is a 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. SenSIP also received NSF supplements to train students in using machine learning for COVID-19 detection.
AB - This paper accompanies the keynote speech at IISA2020 and describes federally funded workforce development research grants and supplements in the area of sensors and machine learning. These 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 REU site which has trained more than 30 students working on sensor hardware design and machine learning algorithm development. The second program is the NSF IRES site which is collaborative with the University of Cyprus and is focused on sensors and machine learning for energy systems. The most recent program funded by NSF is a 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. SenSIP also received NSF supplements to train students in using machine learning for COVID-19 detection.
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U2 - 10.1109/IISA50023.2020.9284402
DO - 10.1109/IISA50023.2020.9284402
M3 - Conference contribution
AN - SCOPUS:85099224231
T3 - 11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020
BT - 11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020
Y2 - 15 July 2020 through 17 July 2020
ER -