Difference between revisions of "Sarah Dean, 11-12 Feb 2020"

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(Created page with "Sarah Dean, a PhD student working with Ben Recht, will visit Caltech on 11-12 Feb 2020. If you would like to meet with her, please sign up for a slot below (using your IMSS...")
 
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=== Schedule ===
=== Schedule ===


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Tuesday (11 Feb)
Tuesday (11 Feb)
* 11:40 am arrival in BUR
* 11:40 am arrival in BUR
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* 5:00 pm: Open
* 5:00 pm: Open
* 6:00 pm: Dinner with Richard + grad students, postdocs
* 6:00 pm: Dinner with Richard + grad students, postdocs
 
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Wednesday (12 Feb)
Wednesday (12 Feb)
* 8:45 am: Open
* 8:45 am: Open
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* 4:45 pm: Depart campus
* 4:45 pm: Depart campus
* 6:40 pm departure from BUR
* 6:40 pm departure from BUR
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=== Seminar ===
=== Seminar ===

Revision as of 06:48, 5 February 2020

Sarah Dean, a PhD student working with Ben Recht, will visit Caltech on 11-12 Feb 2020. If you would like to meet with her, please sign up for a slot below (using your IMSS credentials to log in). Please make sure to put the location where she should meet you.

Schedule

Tuesday (11 Feb)

  • 11:40 am arrival in BUR
  • ~12:30 pm: arrival on campus
  • 12:30 pm: lunch with John Doyle (if available)
  • 1:30 pm: Richard Murray
  • 2:00 pm: Open
  • 2:45 pm: Open
  • 3:30 pm: Open (30 min)
  • 4:00 pm: Seminar
  • 5:00 pm: Open
  • 6:00 pm: Dinner with Richard + grad students, postdocs

Wednesday (12 Feb)

  • 8:45 am: Open
  • 9:30 am: Open
  • 10:15 am: Open
  • 11:00 am: Ludwig Schmidt seminar
  • 12:00 pm: Lunch with faculty or grad students
  • 1:15 pm: Open
  • 2:00 pm: Open (30 min)
  • 2:30 pm: Open (30 min)
  • 3:00 pm: CDS tea
  • 3:30 pm: Open (30 min)
  • 4;00 pm: Open (30 min)
  • 4:45 pm: Depart campus
  • 6:40 pm departure from BUR

Seminar

Safe and Robust Perception-Based Control
Sarah Dean, UC Berkeley

Tue, 11 February, 4 pm
105 Annenberg

Machine learning provides a promising path to distill information from high dimensional sensors like cameras -- a fact that often serves as motivation for merging learning with control. This talk aims to provide rigorous guarantees for systems with such learned perception components in closed-loop. Our approach is comprised of characterizing uncertainty in perception and then designing a robust controller to account for these errors. We use a framework which handles uncertainties in an explicit way, allowing us to provide performance guarantees and illustrate how trade-offs arise from limitations of the training data. Throughout, I will motivate this work with the example of autonomous vehicles, including both simulated experiments and an implementation on a 1/10 scale autonomous car. Joint work with Aurelia Guy, Nikolai Matni, Ben Recht, Rohan Sinha, and Vickie Ye.