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Echolocation Episode: Talking about STEM with Austin Park

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Manage episode 501637761 series 3359529
Content provided by controldesign. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by controldesign or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In 2021, Control Intelligence spoke with Austin Park, winner of the first ever Acopian Power Supply Scholarship in 2017.

Park had completed his master's degree in energy resources engineering, Cleantech, machine learning and decarbonization at Stanford University and was working as a machine learning engineer at Gridmatic, where he's now been for almost six years. At Gridmatic, located in the San Francisco Bay area, Austin's responsibilities include designing, extending and improving wind, load and solar models, primarily focused on deep generative spatiotemporal sequence settings; building infrastructure to support model viz and evaluation; fetching and transforming new features; and leading quantification of Gridmatic's carbon impact.

When speaking with Park four years ago, he talked about his education at UCLA and Stanford, how his scholarship benefitted him and his goals for his career in engineering.

  continue reading

117 episodes

Artwork
iconShare
 
Manage episode 501637761 series 3359529
Content provided by controldesign. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by controldesign or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In 2021, Control Intelligence spoke with Austin Park, winner of the first ever Acopian Power Supply Scholarship in 2017.

Park had completed his master's degree in energy resources engineering, Cleantech, machine learning and decarbonization at Stanford University and was working as a machine learning engineer at Gridmatic, where he's now been for almost six years. At Gridmatic, located in the San Francisco Bay area, Austin's responsibilities include designing, extending and improving wind, load and solar models, primarily focused on deep generative spatiotemporal sequence settings; building infrastructure to support model viz and evaluation; fetching and transforming new features; and leading quantification of Gridmatic's carbon impact.

When speaking with Park four years ago, he talked about his education at UCLA and Stanford, how his scholarship benefitted him and his goals for his career in engineering.

  continue reading

117 episodes

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