IACS seminars are free and open to the public; no registration required. Lunch will not be provided.
ABSTRACT: While “off-the-shelf” ML has become pervasively used throughout astronomy inference workflows, there is an exciting new space emerging where novel learning algorithms and computational approaches are demanded and developed to address specific domain questions. After describing such efforts—in the search for Planet 9 and new classes of variable sources—Dr. Bloom will turn his attention to new practical implementations and uses for generative models in astronomy. One application arises in the need to optimize telescope observing cadences, requiring the generation of physically plausible astronomical time-series. He will present his research group's approach to this using semi-supervised variational autoencoders where physical inputs are mapped to the (generative) latent space. Finally, Dr. Bloom will present his group's recent work on a successful fast imaging artifact (cosmic rays) discovery and inpainting framework.
BIO: Bloom is an astronomy professor at the University of California, Berkeley where he teaches radiative processes, high-energy astrophysics, and a graduate-level "Python for Data Science" course. He has published over 300 refereed articles largely on time-domain transients events, artificial intelligence, and telescope/insight automation. His book on gamma-ray bursts, a technical introduction for physical scientists, was published by Princeton University Press. Josh has been awarded the Data-Driven Discovery prize from the Gordon and Betty Moore Foundation and the Pierce Prize from the American Astronomical Society; he is also a former Sloan Fellow, Junior Fellow at the Harvard Society, and Hertz Foundation Fellow. He holds a PhD from Caltech and degrees from Harvard College (AB) and Cambridge University (MPhil). He was co-founder and CTO of Wise.io, an AI application startup, acquired by GE in 2016.