Time Series Clustering for Monitoring Fueling Infrastructure Performance with Kalai Ramea

1024 1024 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we are joined by Kalai Ramea, Data Scientist at PARC, a Xerox Company.

With a background in transportation, energy efficiency, art, and machine learning, Kalai has been fortunate enough to follow her passions through her work. In this episode, we discuss her environmentally efficient pursuit that lead to the purchase of a hydrogen car and the subsequent journey and paper that followed assessing fueling stations. In her next paper, Kalai looked at fuel consumption at hydrogen stations and used temporal clustering to identify signatures of usage over time, grouping the stations into categories.

In a time when the government is urging people to use hydrogen vehicles, a major concern is the reliability of fueling stations. With the construction of fueling stations is planned to increase dramatically in the next 5 years, building reliability on their performance is crucial to overall adoption. Check out this episode to hear about her papers, PARC research, and a sneak peek into how Kalai incorporates her love of art into her work!

About Kalai

From the Interview

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“More On That Later” by Lee Rosevere licensed under CC By 4.0

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