Marina Kiseleva is a PhD student in University of Arizona’s Department of Computer Science, advised by Professor Kobus Barnard. Marina researches machine learning applications in astronomy. Before joining Arizona, Marina worked as a software engineer consultant for two years, designing software systems for financial and governmental clients. She earned her Bachelor’s of Science in Computer Science from Virginia Tech in 2016.
I am interested in machine learning applications in domain sciences, particularly astronomy. I am currently using machine learning techniques to classify astronomical transient events.
What long-term project do you want to work on?
I would like to work on image-based classification problems that can be applied to the Large Synoptic Survey Telescope (LSST).
What do you enjoy most about your work?
I enjoy contributing to our collective understanding of the universe. Advances in astronomy inform us about physical concepts that affect us here on Earth as well as reveal the mysteries of the universe around us. I also enjoy the challenge of working with complex datasets and designing programs that can use data in new and powerful ways.
What are your career goals?
I would like to work as a researcher in a lab focused on progressing our understanding of the universe.
Tell us something interesting about yourself!
In my spare time I like to take advantage of clear dark nights to observe stars and deep sky objects. I am a member of the Tucson Amateur Astronomy Association (TAAA).