Michelle Lochner


Email: dr.michelle.lochner at gmail.com

With the next generation of telescopes almost upon us, such as the radio telescope the Square Kilometre Array (www.skatelescope.org) and the optical Rubin Observatory Legacy Survey of Space and Time (www.lsst.org), my research focus is on rethinking how to do scientific analysis in the era of massive datasets. This largely involves developing new machine learning and statistical tools to best leverage new astronomical data. As one of the South African LSST Principle Investigators, I’m working to develop machine learning classification algorithms to handle the billions of new astrophysical transients that LSST will detect. I’m also heavily involved in efforts to optimise LSST’s observing strategy, given it’s many and ambitious science goals. On the radio side I’m interested in a new approach to source-finding and how best to combine optical and radio data, particularly for HI applications such as the LADUMA survey on the SKA pathfinder MeerKAT (https://www.sarao.ac.za/science-engineering/meerkat/about-meerkat/). I’m also particularly interested in the exciting question of scientific discovery in the era where most data cannot be monitored by human eyes and am working to develop general-purpose anomaly detection techniques for use with LSST, SKA and other telescopes. My position is joint between UWC and the South African Radio Astronomy Observatory.