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 2018 Summer School | Travel & Visa | Lodging | Registration | Program

Summer School in Statistics for Astronomers XIV (May 29 - June 2, 2018)

Summer School in Astroinformatics (June 4 - 8, 2018)

  Tentative schedule

Week 1: Summer School in Statistics for Astronomers XIV

    Tuesday, May 29
    • 8:30 a.m. Registration (30 min)
    • Astrostatistics: Past, Present & Future (Eric Feigelson 0:40)
    • Probability (David Hunter 3:00)
        Afternoon
    • Inference I (Bing Li 1:30)
    • Introduction to R (Eric Feigelson 1:45)
    Wednesday, May 30
    • Inference II (Bing Li 1:30)
    • Model Fitting, Bootstrap & Model Selection (G. Jogesh Babu 1:45)
        Afternoon
    • Introduction to Regression (Chad Schafer 1:45)
    • R session for Astronomy II (Eric Feigelson 1:30)
    Thursday, May 31
    • Multivariate Analysis, Clustering & Classification (Chad Schafer 2:00)
    • Spatial Models: A Quick Overview (Murali Haran 1:30)
        Afternoon
    • Introduction to Bayesian Analysis I (Thomas Loredo 1:30)
    • R session for Astronomy III (Eric Feigelson 2:00)
    Friday, June 1
    • Introduction to Bayesian Analysis II (Thomas Loredo 1:30)
    • Markov Chain Monte Carlo (MCMC) (David Hunter 1:45)
        Afternoon
    • R session for Astronomy IV (Eric Feigelson 1:45)
    Saturday, June 2
    • Time Series Analysis (Eric Feigelson 1:30)
    • Convergence Diagnostics for MCMC (Eric Ford 1:45)

- 14th Summer School concludes at 1pm -

Week 2: Summer School in Astroinformatics

    Monday, June 4
    • Welcome/Overview (30 min)
    • Fundamentals of Scientific Computing (2:30; Adam Lavely, Chuck Pavloski, Justin Petucci)
        Afternoon
    • High-Performance Computing for Astroinformatics (3:00; Adam Lavely, Chuck Pavloski, Justin Petucci)
    Tuesday, June 5
    • Data Mining (3:00; Tamas Budavari)
        Afternoon
    • Machine Learning including algorithms for Clustering & Classification (3:00; Vasant Honavar, w/ Ari Silburt helping to adapt examples for astronomers)
    Wednesday, June 6
    • PCA, Machine Learning (classification, random forest) including algorithms for Dimension Reduction (3:00; Le Bao & Jia Li)
        Afternoon
    • Bayesian Computing (Murali Haran; 3:00)
    Thursday, June 7
    • Modeling Astronomical Populations (Heirarchical Modeling Theory; Angie Wolfgang & Jessi Cisewski-Kehe; 3:00)
        Afternoon
    • Optimizing models over Big Data (Ethan Fang; 1:30)
    • Gaussian Process Regression (Murali Haran; 1:30)
    Friday, June 8
    • Neural Networks & Deep Learning (3:00; Lee Giles & Lingzhou Xue)
    • Conclusion and Program Evaluations (0:30)

- Summer School concludes at 1pm -

    For most days the schedule will be:
    9-10:30 First half of morning session
    10:30-11 Break
    11-12:30 Second half of morning session
    12:30-2 Break before Afternoon session starts
    2-3:30 First half of afternoon lesson
    3:30-4 Break
    4-5:30 Second half of afternoon lesson
Department of StatisticsEberly College of ScienceDepartment of Astronomy and Astrophysics