Josh is an astronomy professor at the University of California in Berkeley, where he teaches high-energy astrophysics and the use of Python for data-driven science. Josh works at the intersection of astronomy, statistics and computer science, and he is always searching for fruitful collaborations between disciplines. Josh’s work focuses on discovery of astronomical transients events and telescope/insight automation.
In bringing advanced machine learning tools to bear within data-rich scientific fields, Josh hopes to uncover new phenomena that have been observed (captured with instruments) but not surfaced to the level of discovery (recognition of novelty). The systematic and robust automation of complex components of the “scientific inference stack” is designed to accelerate our understanding of the universe. It also frees scientists from repetitive knowledge work, allowing them to focus on what machines cannot do: formulate and test complex hypotheses about the world around us.
Josh has authored over 300 referred articles, garnering more than 30,000 citations. He is an inventor, holding patents in hyperspectral imaging and disaster early warning. He received the Alfred P. Sloan Research Fellowship and the Hertz Foundation Fellowship; in 2009, he was awarded the Pierce Prize from the American Astronomical Society. He is co-founder of Wise.io, a high-performance machine learning applications company. Josh holds a PhD in Astrophysics from the California Institute of Technology, and also trained at Harvard University and Cambridge University.
Google Scholar Profile
Python for Data Science Course Website
Maching Learning for Time Series Project
University of California, Berkeley Department of Astronomy