Rate My Professor Madeline Wade

Wade's research interests center on gravitational-wave physics, astrophysics, and data analysis, with recent efforts focused on developing low-latency calibration pipelines for Advanced LIGO, using machine learning algorithms to enhance LIGO data quality, predict glitches, and estimate neutron star equations of state from binary mergers. She has co-authored key publications including "Observation of Gravitational Waves from a Binary Black Hole Merger" (Phys. Rev. Lett. 116, 061102, 2016), "GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral" (Phys. Rev. Lett. 119, 161101, 2017), "GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo during the Second Part of the Third Observing Run" (Phys. Rev. X 13, 041039, 2023), and "Searching for Asymmetric and Heavily Precessing Binary Black Holes in the Gravitational Wave Data from the LIGO and Virgo Third Observing Run" (Phys. Rev. Lett. 133, 201401, 2024). Wade received a prestigious NSF CAREER grant to support her integrated research and education program aiding the LIGO Scientific Collaboration in unlocking spacetime mysteries. She mentors undergraduate students on projects involving LIGO calibration, gravitational-wave glints, pulsar candidate ranking via machine learning, and NANOGrav collaboration efforts. Wade delivers public lectures, such as "How AI Can Help Us Understand the Densest Objects in the Universe," and teaches courses including PHYS 105: Frontiers of Astrophysics.