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BEGIN:VEVENT
SUMMARY:Gravitational data analysis with machine learning and deep learnin
 g
DTSTART:20250918T143000Z
DTEND:20250918T160000Z
DTSTAMP:20260418T202214Z
UID:0f21baa0-99d2-4d7a-8373-1eb0f7da7de6
SEQUENCE:1
CREATED:20250915T133011Z
DESCRIPTION:  Gravitational-wave astronomy enables precision studies of co
 mpact binary systems and provides powerful tests of General Relativity. Bl
 ack-hole spectroscopy—the detection of gravitational-wave emission spect
 ra from black-hole ringdowns—offers a particularly clean framework for t
 esting gravity theories against well-defined predictions. However\, accura
 te waveform modeling and efficient parameter estimation are essential to e
 xtract both astrophysical and fundamental physics insights from the data. 
 In this talk\, I will present my recent works in gravitational waveform mo
 deling and inference\, with a focus on black hole ringdowns\, and highligh
 ting the role of machine learning and deep learning techniques. In particu
 lar\, I will discuss the promises and challenges of using Gaussian Process
  Regression for waveform modeling and hierarchical inference\, and simulat
 ion-based inference for parameter estimation. 
LAST-MODIFIED:20250915T133011Z
LOCATION:DF Seminar Room (2-8.3)\, 2nd floor of Physics Building
URL:http://df.vps.tecnico.ulisboa.pt/en/events/gravitational-data-analysis
 -with-machine-learning-and-deep-learning/
X-ALT-DESC;FMTTYPE=text/html:<p data-block-key="4sama">  Gravitational-wav
 e astronomy enables precision studies of compact binary systems and provid
 es powerful tests of General Relativity. Black-hole spectroscopy—the det
 ection of gravitational-wave emission spectra from black-hole ringdowns—
 offers a particularly clean framework for testing gravity theories against
  well-defined predictions. <br/><br/>However\, accurate waveform modeling 
 and efficient parameter estimation are essential to extract both astrophys
 ical and fundamental physics insights from the data. In this talk\, I will
  present my recent works in gravitational waveform modeling and inference\
 , with a focus on black hole ringdowns\, and highlighting the role of mach
 ine learning and deep learning techniques. <br/><br/>In particular\, I wil
 l discuss the promises and challenges of using Gaussian Process Regression
  for waveform modeling and hierarchical inference\, and simulation-based i
 nference for parameter estimation. </p>
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