Michelle Lochner
Born in South Africa with a PhD from the University of Cape Town, Dr. Michelle Lochner is a Senior Lecturer with a joint position between the University of the Western Cape and the South African Radio Astronomy Observatory (formally SKA South Africa). Her focus is on cosmology and trying to get the best out of combining optical and radio telescopes like the Vera C. Rubin Observatory and the Square Kilometre Array. She works on developing new statistical techniques and using machine learning to tackle the masses of data we are dealing with in astronomy, currently focusing on the use of anomaly detection for scientific discovery. She is also the director of an international mentoring programme for women in physics called the Supernova Foundation (www.supernovafoundation.org).
Profile Picture – adass-xxxi-2021/question_uploads/profile_CWLjzra.jpg Affiliation –
University of the Western Cape / South African Radio Astronomy Observatory
Position –Senior Lecturer
Homepage – GitHub ID –MichelleLochner
Sessions
The next generation of telescopes such as the SKA and the Rubin Observatory will produce enormous data sets, far too large for traditional analysis techniques. Machine learning has proven invaluable in handling large data volumes and automating many tasks traditionally done by human scientists. In this talk, I will discuss how machine learning for anomaly detection can help automate the process of locating unusual astronomical objects in large datasets thus enabling new cosmic discoveries. The framework of using active learning to build a recommendation engine for interesting anomalies is general and could be applied in other fields.