Announcements of Upcoming Summer Schools

For the 21st year, Penn State’s Center for Astrostatistics and Astroinformatics is offering its week-long Summer School in statistical methodology for astronomy. The 21st summer school will be held virtually via Zoom from June 1-5, 2026. It features

• Pre-recorded lectures & live Q&A sessions via Zoom.
• Computational lab tutorials as homework to complement each lesson.
• Opportunities to ask further questions to instructors, teaching assistants and other students via Slack.
• Parallel sessions for learning various data analytic methods.

The School provides an intensive program in statistical inference covering topics such as principles of probability and inference, regression and model selection, bootstrap resampling, supervised / unsupervised learning, Bayesian data analysis, Markov chain Monte Carlo (MCMC), nested sampling, time series analysis, spatial statistics, deep learning neural networks, Gaussian process, random forest, and machine learning for astronomical applications.

Lectures will be prepared by experienced instructors in astrostatistics and astroinformatics and will be available at least one day before the scheduled lecture time. Each day will feature a live Q&A session. Lab tutorials, provided as computational notebooks, will be assigned as homework to reinforce the learning experience, allowing participants to apply methods to astronomical datasets that illustrate realistic challenges in contemporary research.

We anticipate that attendees will be spread across multiple time zones. They are strongly encouraged to complete the tutorials either in the afternoon or evening following the lesson or in the morning before the next day’s live session begins. Additionally, attendees are encouraged to engage with faculty, teaching assistants, and fellow students via Slack while working on the lab tutorials. Questions about lectures and lab tutorials can be asked at any time via Slack.

The lecture recordings, materials, and Slack workspace will remain accessible for one week after the end of the summer school (until June 12, 2026) for those who miss lectures during the summer school week but would like to catch up afterward. 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 statistics.

The School assumes that participants have basic familiarity with programming, but does not assume that participants 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. It is highly recommended that all of the participants install R before the beginning of the summer school. Click here to download R on your local machine. Participants will also be provided instructions for installing R as a part of the first day tutorial, so they can complete notebook-based labs on their local system.

Serious participants should expect to engage full days during the week. In addition to the daily schedule, including lectures and live Q&A sessions, we recommend budgeting approximately 2 hours per day to work through the computational notebook tutorials as daily homework and to consult with instructors, teaching assistants, and other participants on Slack. While this may be a heavier expectation than is typical for many other meetings, experience from previous years shows that applying lessons learned in the lab tutorials is extremely valuable for developing intuition about the methods and improving data analysis skills. Course Information

A tentative schedule for the 2026 summer school will be available in March and will be finalized in May. The schedule for last year’s summer school is available here for reference.

List of instructors:

• Suzanne Aigrain (Oxford University) for Gaussian process
• Jogesh G. Babu (Penn State University) for model fitting, validation, and bootstrapping
• David Banks (Duke University) for deep learning neural network
• Eric Feigelson (Penn State University) for time series and R tutorials
• Peter Freeman (Carnegie Mellon University) for random forest
• Murali Haran (Penn State University) for Markov chain Monte Carlo and spatial statistics
• David Hunter (Penn State University) for probability
• Tom Loredo (Cornell University) for Bayesian inference
• Ashish Mahabal (California Institute of Technology) for machine learning applications in astronomy
• Joshua Speagle (University of Toronto) for nested sampling and machine learning applications in astronomy
• Hyungsuk Tak (Penn State University) for statistical inference
• Ashley Villar (Harvard University) for regression, supervised and unsupervised learning

Registration Information

Registration Deadline: May 8th, 2026.

Registration Fee: USD 120

Refunds and Cancellations:

Refunds will be issued for cancellation by the registrant if we receive your notification in writing by May 8th, 2026.

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.

For any questions about the virtual summer school, please feel free to reach out to the Penn State Center for Astrostatistics and Astroinformatics (cast[AT]psu[DOT]edu). For additional questions, please see the school website.