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Thomas Killestein is a Warwick Prize Fellow in the Astronomy and Astrophysics group at the University of Warwick. He primarily works as a part of the Gravitational-wave Optical Transient Observer (GOTO) team, focusing on developing high-performance deep learning classification algorithms for detecting transient candidates in GOTO data. His research involves autonomously identifying hosts and interesting objects to facilitate rapid follow-up observations. Thomas is also a co-leader of Kilonova Seekers, a citizen science project within the Zooniverse platform, which enables the public to participate in making significant scientific discoveries by inspecting data in near-real time. He leads the technical development of this project, including database and pipeline development to enhance citizen science's responsiveness to the fast pace of time-domain astronomy. His research interests encompass Machine Learning and Deep Learning applied to time-domain astronomy, as well as the empowering of the public through citizen science initiatives to drive meaningful scientific discoveries.
Includes General, Mechanical, Civil, Electrical, Biomedical, and Manufacturing Engineering. Most programs fall under English Band A.