Week 1 conference

KSPA 2019 Conference: Machine Learning in the Era of Large Astronomical Surveys

Santa Cruz, July 8th - July  12th 2019.

A conference is hosted in the first week of the Kavli Summer Program, and features invited pedagogical lectures in the morning, with afternoon contributed talks presenting state-of-the-art research on the program topic. Participation to the conference is open to everyone who wishes to attend, please register on this Google Form by June 15th.

The morning lectures are video-recorded, and meant for a broad audience. The afternoon talks present the latest developments in the field. All presentations will be published online a few weeks after the conference.

More detail about the upcoming conference will be posted as the organization of the program proceeds.

Scientific Program

Sunday 7/7:

  • 4:00 pm -6:30 pm: Welcome Reception, TBD

 

All the talks will be hosted in the SOE Simularium (E2-180) at the bottom of the E2 building (the nice shiny one) of the Baskin School of Engineering

Monday 7/8: 

  • 8:45 am Arrive at the Simularium
  • 9:00 am - 9:05 am: Welcome to UCSC by Dean Koch
  • 9:05 am - 9:30 am: Welcome to KSPA by the organizers
  • 9:30 am - 1:00 pm: Lectures
  • 1:00 pm - 2:30pm: Lunch break
  • 2:30 pm - 3:30pm: Orientation; Visit of campus;
  • 3:30 pm - 4:00 pm: Afternoon Break
  • 4:00 pm - 5:30 pm: Project presentations (Long-term participants and fellows only)

Tuesday 7/9:

  • 9:00 am - 12:30 pm: Lectures
  • 12:30 pm - 2:00 pm: Lunch break, project discussion time
  • 2:00 pm - 3:45 pm: Contributed talks
    • Marc Huertas-Company: "Anomaly detection in astronomical images"
    • Ryan Hausen: "Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data"
    • Helena Domínguez Sánchez: "Clasification of galaxy images with deep learning"
    • Shirley Ho: "Deep Learning as the New Simulator"
  • 3:45 pm - 4:30 pm: Afternoon Break
  • 4:30 pm - 6:00 pm: Project discussion time.

Wednesday 7/10:

  • 9:00 am - 12:30 pm: Lectures
  • 12:30 pm - 2:00 pm: Lunch break, project discussion time
  • 2:00 pm - 3:45 pm: Contributed talks
    • Michelle Lochner: "Human-in-the-loop learning for anomaly detection"
    • Joseph Burchett: "The Cosmic Web: Slimed, Visualized, and Analyzed"
    • Nesar Ramachandra: "Denerative models, Image emulation"
    • Sultan Hassan: "Constraining the astrophysics and cosmology from 21cm tomography with deep learning"
  • 3:45 pm - 4:30 pm: Afternoon Break
  • 4:30 pm - 6:00 pm: Project discussion time.

Thursday 7/11:

Friday 7/12:

  • 9:00 am - 12:30 pm: Lectures
  • 12:30 pm - 2:00 pm: Lunch break, project discussion time
  • 2:00 pm - 3:30 pm: Contributed talks
    • Sara Jamal: "Application of deep learning techniques for variables stars classification"
    • Avishai Dekel: "Confronting Theory with Observations using Deep Learning"
    • Nicholas Ross: "Are IR Changing-Look AGN preferential homes for LIGO events?"
    • Yuan-Sen Ting: "Milky Way, Machine Learning, Big Data"
  • 3:30pm - 4:15 pm: Afternoon Break
  • 4:15 pm - 6:00 pm: Project discussion time.

 

X. Prochaska: "Machine Learning in Astronomy"

    • Part 1

    • Part 2

S. Ho:

  • Lecture 1: Introduction to Deep Learning

  • Lecture 2: Sequence Modeling: Recurrent and recursive nets
    • Part 1

    • Part 2

  • Lecture 3: Graph neural networks

B. Menard:

  • Lecture 1: Data, Representations, and Information

  • Lecture 2: Mathematical concepts in neural-based machine learning
    • Part 1

    • Part 2

D. Kirkby:

  • Lecture 1: Practical Bayes, MCMC and variational inferences

  • Lecture 2: Unsupervised learning with variational autoencoders and GANs

  • Lecture 3: Probabilistic Deep Learning