John Wu is an Assistant Astronomer at STScI and an Associate Research Scientist at JHU. Dr. Wu's research focuses on studying galaxies with multi-wavelength observations and machine learning methods. He has developed and employed machine learning algorithms that process astronomical imaging data at the pixel scale in order to understand how galaxy appearances are governed by their growth and evolution. Dr. Wu has shown that convolutional neural networks (CNNs) can reliably predict the optical spectra of low-redshift galaxies purely from Pan-STARRS image cutouts. Recently, he and his collaborators have used CNNs to identify >100,000 low-redshift galaxy candidates, and >20,000 probable satellites around known massive host galaxies. He is also using a similar method to determine DESI spectroscopic fiber targets in order to survey a complete population of z < 0.03 galaxies (LOWZ survey).
Dr. Wu began his tenure-track Assistant Astronomer position at STScI in 2022. He first joined STScI as a postdoc (2020), prior to which he was a postdoc at JHU (2019). He previously earned his PhD in Physics and Astronomy at Rutgers, The State University of New Jersey (2019) and BSc in Physics/Astrophysics at Carnegie Mellon University (2013).
PhD in Physics and Astronomy, Rutgers, The State University of New Jersey
BSc in Physics and Astrophysics, Carnegie Mellon University
Science Interests and Research:
- Galaxy Formation and Evolution
- Interstellar and Circumgalactic Medium
- Machine Learning and Statistical Methods
ORCID ID: 0000-0002-5077-881X