Machine Learning Workforce Development Programs on Health and COVID-19 Research

Spanias, Andreas

doi:10.1109/iisa50023.2020.9284402

“…Typically, all projects in a program follow a particular topic, allowing for narrow standardization of a skill curriculum. Several REU programs have already focused on ML and AI topics [17]- [19]. Course-based Undergraduate Research Experiences (CUREs) are another method aiming to integrate research and teaching [20], [21].…”

Section: A Related Workmentioning

confidence: 99%

An Immersive Curriculum to Develop Computational Science and Research Skills in a Cohort-Based Internship Program

Johnson,

Villafañe-Delgado,

Symonette

et al. 2024

Preprint

0

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Workforce education is a key challenge as computational science, including data science and machine learning, increasingly influences critical application spaces such as public health and medicine, space exploration, national security, autonomous systems and cybersecurity. Developing core software development, analysis, and machine learning skills will enable workers to have impact across a range of spaces. These skills are in high demand in industrial research and development, but we do not believe that traditional recruiting and training models in industry (e.g., internships, continuing education) are serving the needs of the diverse populations of students who will be required to revolutionize these fields. To accelerate workforce development in these key areas, we have designed and executed a machine learning and research skills training curriculum for our cohort-based research internship program, the Cohort-based Integrated Research Community for Undergraduate Innovation and Trailblazing (CIRCUIT). The program targets trailblazing, high-achieving students who face barriers in achieving their goals, and the training program is aimed at accelerating their growth as leaders in data science, machine learning, and artificial intelligence research. The training curricula and support structure is designed to be flexible to student and research project needs during a research internship. Utilizing both existing online material and custom workshops, this model consists of a compressed data science and machine learning curriculum, a series of professional development training workshops, and team-based challenges. Strategies allow for customization of these training efforts for individual students and projects. Over four cohorts, this training curricula has helped students achieve mastery of data science and machine learning concepts, produce key demonstrations and work products in their cohort research projects, and progress to further internships, graduate school, and employment.

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IRES Program in Sensors and Machine Learning for Energy Systems

Jaskie

¹

,

Martin

²

,

Rao

³

et al. 2020

2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA

3

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An Immersive Curriculum to Develop Computational Science and Research Skills in a Cohort-Based Internship Program

Johnson,

Villafañe-Delgado,

Symonette

et al. 2023

2023 IEEE Integrated STEM Education Conference (ISEC)

1

0

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Workforce education is a key challenge as computational science, including data science and machine learning, increasingly influences critical application spaces such as public health and medicine, space exploration, national security, autonomous systems and cybersecurity. Developing core software development, analysis, and machine learning skills will enable workers to have impact across a range of spaces. These skills are in high demand in industrial research and development, but we do not believe that traditional recruiting and training models in industry (e.g., internships, continuing education) are serving the needs of the diverse populations of students who will be required to revolutionize these fields. To accelerate workforce development in these key areas, we have designed and executed a machine learning and research skills training curriculum for our cohort-based research internship program, the Cohort-based Integrated Research Community for Undergraduate Innovation and Trailblazing (CIRCUIT). The program targets trailblazing, high-achieving students who face barriers in achieving their goals, and the training program is aimed at accelerating their growth as leaders in data science, machine learning, and artificial intelligence research. The training curricula and support structure is designed to be flexible to student and research project needs during a research internship. Utilizing both existing online material and custom workshops, this model consists of a compressed data science and machine learning curriculum, a series of professional development training workshops, and team-based challenges. Strategies allow for customization of these training efforts for individual students and projects. Over four cohorts, this training curricula has helped students achieve mastery of data science and machine learning concepts, produce key demonstrations and work products in their cohort research projects, and progress to further internships, graduate school, and employment.

show abstract

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Cited by 4 publications

References 52 publications

An Immersive Curriculum to Develop Computational Science and Research Skills in a Cohort-Based Internship Program

An Immersive Curriculum to Develop Computational Science and Research Skills in a Cohort-Based Internship Program

IRES Program in Sensors and Machine Learning for Energy Systems

An Immersive Curriculum to Develop Computational Science and Research Skills in a Cohort-Based Internship Program

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