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VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Machine Learning Methods for Robust Uncertainty Quantification and
  Controller Approximation
DTSTART:20230511T170000
DTSTAMP:20260403T233236Z
UID:534fc2f3473cedebea6ca1344a0e1c494a900e0c55f577a57a3bab5c
CATEGORIES:Thesis defenses
DESCRIPTION:Emilio MADDALENA\nThesis Director: Prof. C. N. Jones\,\nElectr
 ical Engineering doctoral program\nThesis Nr. 9831\n\nTo take part in the
  public defense\, please contact directly the speaker
LOCATION:MED 0 1418 https://plan.epfl.ch/?room==MED%200%201418
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR