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Our guest today is Jan Drgona, who joins us today from Johns Hopkins University.
Jan is an associate professor in the department of civil and systems engineering, and is also at the Ralph S O’Connor Sustainable Energy Institute.
Jan’s research focuses on energy management in buildings and he’s working on developing scientific machine learning methods to model energy management which turns out is very complex. In this mini episode, I ask Jan about what makes a building complicated to heat and cool, and describes the various factors that make temperature control a challenge, and hints at how physics and machine learning can help. Tune in next week for the full-length interview when Jan talks about making energy use in buildings sustainable and how scientific machine learning and problem solving with an engineering approach and mindset can help.
Hosted on Acast. See acast.com/privacy for more information.
By Susan Keatley5
1111 ratings
Please click below to fill out the survey for this episode:
Science Fare Podcast Feedback Form
Science Fare Podcast website
Our guest today is Jan Drgona, who joins us today from Johns Hopkins University.
Jan is an associate professor in the department of civil and systems engineering, and is also at the Ralph S O’Connor Sustainable Energy Institute.
Jan’s research focuses on energy management in buildings and he’s working on developing scientific machine learning methods to model energy management which turns out is very complex. In this mini episode, I ask Jan about what makes a building complicated to heat and cool, and describes the various factors that make temperature control a challenge, and hints at how physics and machine learning can help. Tune in next week for the full-length interview when Jan talks about making energy use in buildings sustainable and how scientific machine learning and problem solving with an engineering approach and mindset can help.
Hosted on Acast. See acast.com/privacy for more information.