NIST researchers lead a hackathon addressing challenges in data analytics

NIST researchers lead a hackathon addressing challenges in data analytics

Credit: Wikimedia Commons

In August, 38 students, researchers, and industry professionals came together to participate in the 2021 ASME-CIE Hackathon. The theme of this year’s hackathon was Explore the Power of Data and Cybersecurity for Mechanical Engineering. Students and researchers from Georgia Tech, Purdue University, and the University of Arkansas won first place for their solutions to the three problems that the hackathon addressed. The events were all organized by the ASME CIE-Systems Engineering and Information Knowledge Management (SEIKM) technical committee, led by researchers from NIST’s Systems Integration Division (SID) and the University of Texas Austin. Representatives from industry, government labs, and universities volunteered and judged the event.

A hackathon is an event where computer programmers come together for a marathon software-building session. The 2021 ASME-CIE Hackathon brought together teams of 2-3 people to address three problems that are important to members of ASME-CIE. This event had two goals: to work on these problems and to train students in the data analytics related to the problems. The event happened August 14-15, and the winners presented at the end of the 24-hours period. ASME awarded $7,500 in total prize money to the winners.

The first problem that the participants addressed was around cybersecurity in digital manufacturing systems. “Data confidentiality is a growing concern” said Dr. Yan Lu, a NIST organizer for the event. The second problem was related to production scheduling. Production scheduling--or the process of assigning resources and systems to different products--itself is hardly a new practice in manufacturing, but the recent influx in data related to manufacturing makes the field of data analytics related to production scheduling a new and exciting field. Hackathon participants worked with datasets to design software that improved automated testing in production scheduling. The final problem addressed additive manufacturing. Additive manufact ..

Support the originator by clicking the read the rest link below.