ITC Pizza Lunch Spring 2016: Astrostatistics


Date Speaker Title
Wed 02/10/2016
  1. Douglas Finkbeiner (CfA)
  1. Introduction to Astrostatistics
Wed 02/17/2016
  1. Vinay Kashyap (CfA)
  2. Aneta Siemiginowska (CfA)
  1. Upper Limits, or How X-ray bright is Antares not
  2. Narrow Features in Noisy Data: Is there a line?
Wed 03/02/2016
  1. Alyssa Goodman (CfA)
  1. Humans-in-the-Loop: Visualization + Machine Learning
Wed 03/09/2016
  1. Kaisey Mandel (CfA)
  2. Ben Montet (CfA)
  1. Hierarchical Bayes, Huh? What is it good for? Absolutely Everything (including Supernova Cosmology)!
  2. Posterior Distributions of Transit Times in the Kepler Dataset
Wed 03/23/2016
  1. Lindy Blackburn (CfA)
  2. Michael Johnson (CfA)
  1. Statistics used in the detection of gravitational waves by LIGO
  2. Astrostatistics and Radio Interferometry: Using Information Theory, Digital Telescopes, and Stochastic Optics to Image a Black Hole
Wed 03/30/2016
  1. Hyungsuk Tak (Harvard Statistics)
  2. Jill Naiman (CfA)
  1. Bayesian and Profile Likelihood Strategies for Time Delay Estimation from Stochastic Time Series of Gravitationally Lensed Quasars.
  2. Visualization of Large Astrophysical Datasets: How do we know what we know about our data?
Wed 04/06/2016
  1. Greg Green (CfA)
  2. David Jones (Harvard Statistics)
  1. Mapping Dust in 3D with Stellar Photometry
  2. Disentangling overlapping astronomical sources using spatial and spectral data
Wed 04/13/2016
  1. Victor Pankratius (MIT)
  2. Erik Rosolowsky (University of Alberta)
  1. Computer-Aided Discovery in Astronomy: Leveraging Machine Intelligence for Scientific Insight Generation
  2. Comparing Simulations and Observations of Star Formation through Experimental Design
Wed 04/20/2016
  1. David A van Dyk (Imperial College London)
  1. Science­-Driven Models for Image Analysis in Astronomy and Solar Physics
Wed 04/27/2016
  1. Anna Barnacka
  1. Resolving the High Energy Universe with Strong Gravitational Lensing
Wed 05/04/2016
  1. Xiao-Li Meng (Harvard Statistics)
  1. Calibration with Multiplicative Means but Additive Errors: A Log Normal Approach