Summer School in Statistics for Astronomers XVII

For the 17th year, Penn State’s Center for Astrostatistics is offering its week-long virtual Summer School in statistical methodology for astronomy.

The School provides an intensive program in statistical inference covering topics such as principles of probability and inference, regression and model selection, bootstrap resampling, multivariate clustering and classification, Bayesian data analysis, Markov chain Monte Carlo (MCMC), time series analysis, spatial statistics, deep learning neural networks, and machine learning with random forest.  

Lectures will be presented by experienced instructors in astrostatistics. Lab tutorials in the form of computational notebooks will reinforce the learning experience, encouraging  participants to exercise the methods with astronomical datasets illustrating realistic challenges faced in contemporary research.  Lectures and tutorials will be enhanced via virtual interactions among instructors, teaching assistants, and participants, including Zoom Q&A sessions followed by lectures and Slack channels to handle extra questions and to support students working through lab tutorials. 

Live content will be delivered 10am-4pm EDT (UTC-4 hours).  Live lectures and Q&As will be recorded and made available to participants to accommodate participants in different time zones.  Slack channels will be active before and after live content thanks to participation from around the globe.

For participants’ interest, the School is followed by the 2nd Summer School in Astroinformatics from June 13-17, which emphasizes computationally intensive astronomical data analyses via high performance computing (https://sites.psu.edu/astrostatistics/astroinfo-su22/).

Intended Audience & Preparation 

The School is designed for graduate students and postdocs in Astronomy & Astrophysics.  Advanced undergraduate students and more senior researchers are also welcome to join.  The Summer School in Statistics for Astronomers assumes working mathematical knowledge at the level of undergraduate physics or astronomy major, but does not assume that students have had any prior formal training in statististics.

The School assumes that participants have basic familiarity with programming, but does not assume that students are familiar with the syntax of any particular language.  The School will provide training in using R, an open-source statistical software environment widely used among statisticians.  Students will be provided instructions for installing R, so they can complete notebook-based labs on their local system.

It is highly recommended that all of the participants study the following introductory materials before the beginning of the summer school; a Jupyter notebook (here) and a 45-minute lecture (here), prepared by Dr. Eric Feigelson. It is important for the first-time R users to review these materials. Dr. Eric Feigelson will give a brief overview on R during the first-day tutorial, and these introductory materials will be given for your self-study (homework) on Moday.

Serious participants should expect to engage for full-days during the week.  In addition to live lessons, we recommend budgeting ~2 hours per day to work through the computational notebook tutorials, and consulting with instructors, teaching assistants & other participants on Slack.  Depending on a student’s time zone and other commitments, students may choose to work on lab tutorials in the afternoon/evening after the live lessons or the morning before the next set of lessons.   While this may be a heavier expectation than is common for many online meetings, previous years’ experience teaches us that applying lessons learned in lab tutorials is extremely valuable for participants to gain intuition for methods and improve their data analysis skills.

Refunds and Cancellations:
Refunds will be issued for cancellation by the registrant if we receive your notification in writing by May 15, 2022.

A $50 administrative fee will be charged for all cancellations. Refund requests made after that time will not be honored.

The University may cancel or postpone any course or activity because of insufficient enrollment or other unforeseen circumstances. If a program is canceled or postponed, the University will refund the full registration fees but cannot be held responsible for any other related costs, charges, or expenses.