Michael Coughlin

Michael Coughlin is an Assistant Professor of Physics at the University of Minnesota, affiliated with the School of Physics and Astronomy. He specializes in multi-messenger astronomy, combining gravitational wave and electromagnetic observations to study cosmic events like neutron star and black hole mergers. His work involves collaborations with major projects including LIGO, Zwicky Transient Facility (ZTF), and LSST.

• Education
  • PhD in Physics, Harvard University (2016)
  • AM in Physics, Harvard University (2015)
  • MPhil in Astronomy, Cambridge University (2013)
  • BA in Physics, Astronomy & Math, Carleton College (2012)

His research focuses on gravitational wave detection, seismic noise mitigation, and machine learning applications in astronomy. He has developed novel algorithms for LIGO data analysis and contributed to global telescope networks like GRANDMA and GROWTH to optimize transient event follow-up. His work also explores the stochastic gravitational-wave background and kilonova detection limits using optical telescopes.

Michael actively participates in public outreach, including the Galtier Elementary Outreach Program through the Accelerated AI Algorithms Institute (a3ai3). He has presented at international conferences on topics ranging from seismic noise effects to LISA mission planning. His recent publications analyze gravitational wave detection challenges and calibration strategies for next-generation observatories.