My research mainly involves studying dust in galaxies. Central to my PhD thesis is the use of machine learning techniques to circumvent a lack of data. Galaxies that have been observed through a broad range of wavelengths (and hence have accurately determined properties) are used as training examples. The machine learning pipeline learns to predict accurate properties, even for galaxies with less observational constraints.
You might know me from one of the following conferences:
| name | year | where | talk? | topic |
|---|---|---|---|---|
| Nederlandse Astronomen Conventie (NAC) | 2018 | Groningen, The Netherlands | poster | global FIR predictions |
| Machine learning applications for astronomy workshop | 2018 | Nottingham, UK | talk | morphological stellar mass |
| IAU Symposium 341: PanModel2018 | 2018 | Osaka, Japan | talk | global FIR predictions |
| EWASS | 2019 | Lyon, France | talk | morphological stellar mass |
| The art of measuring galaxy physical properties | 2019 | Milano, Italy | talk | global FIR predictions |