HDSI Workshop | Data Science in the Physical Sciences

More details coming soon.

Instructor:

• Matthew Schwartz, Professor of Physics, Department of Physics, Harvard University

Speakers:

• Carlos Arguelles Delgado, Assistant Professor of Physics, Harvard University; IAIFI
  • Using Machine Learning to Unveil the Invisible Universe
    • The IceCube Neutrino Observatory, the largest currently operating neutrino telescope today, has opened a new window of the Universe by detecting high-energy astrophysical neutrinos. These neutrinos are produced in some of the most violent regions of the Universe. In this contribution, I will summarize current efforts to use Machine Learning to improve the identification and reconstruction of high-energy neutrino events. i will highlight the main challenges in this experiment and future prospects.
• Lina Necib, Assistant Professor of Theoretical Astrophysics, MIT
  • (Machine) Learning the Genealogy of the Milky Way
    • Galaxies form and grow by merging with other galaxies, making the formation history of a galaxy resemble that of a family tree. Our galaxy, the Milky Way, is no exception, and with recent telescopes like Gaia, we are able to build the Milky Way’s family tree. In this talk, I will discuss how we can use machine learning techniques to unveil the secrets of the merger history of our Galaxy. 
• Na Li (“Lina”), Winokur Family Professor of Electrical Engineering and Applied Mathematics, Harvard John A. Paulson School of Engineering and Applied Sciences; IAIFI; NSF AI institute
  • Closing the Loop: From Data to Action in Complex Systems