Host a session of the LSSTC Data Science Fellowship Program!

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The LSSTC Data Science Fellowship Program (LSSTC DSFP) is a two-year training program, designed to teach students in astronomy-related fields (e.g. astrophysics, cosmology, planetary science, etc.) essential skills for dealing with big data from LSST.

Want to give your graduate students the chance to become local experts in astronomical data science? Host institutions are given four student auditor spots for their graduate students; these students attend the session at their institution and become part of the DSFP online community, enhancing their professional network.

Want to spend a week with visiting experts at the cutting edge of data-intensive astronomy? The LSSTC DSFP brings domain experts to the host department, opening the possibility of additional talks, conversations, and opportunities for collaborations.

Learn more about the LSSTC DSFP here: astrodatascience.org

Institutions* may apply to host a session of the DSFP by completing the application below. Applicant review takes place on a rolling basis; we are currently accepting applications for 2023 and beyond.

* Institutional hosts are responsible only for providing a venue, and are not required to provide instructors or funding. We inquire about local expertise below, in the event local participation in teaching is relevant and/or of interest to the host institution. Institutional hosts do not need to be LSSTC Members to host.

Name of Institution(s) Applying *

Name and Email of Primary Contact for Application *

Available Facilities

The DSFP lessons consist of both lectures and hands-on, collaborative work, which necessitate different physical layouts in the teaching space. Available learning spaces must have capacity for ~45-50 persons at a time.

What facilities or working spaces does your department offer for these varying lesson styles? Please speak to capacity, available audiovisual equipment, ADA accessibility, and any other details you deem relevant. *

When would your institution be interested in hosting?

DSFP sessions take place over the course of ~5 days, every 2-3 months, with some flexibility with regard to dates. Please choose your first and second preferences for hosting season below.

First preference *

Second preference *

If you have specific dates in mind at this time, please let us know:

If there is a specific time when your host institution is unavailable to host, please let us know:

Topical Areas

The LSSTC DSFP covers a variety of topics, with each week-long session delving into a single topic in depth.

Which of the topical areas covered by the DSFP would your department be most interested in hosting? *

Software engineering: Building code repositories; object oriented programming; version control/GitHub; issue tracking; unit tests; continuous integration.

Statistics: Regression; frequentist vs. Bayesian methods; Gaussian processes; generative models; hierarchical models; missing information and selection effects.

Machine learning: Unsupervised methods; including density estimation, anomaly detection, feature extraction, and clustering techniques; supervised methods; end-to-end automated classification models; deep neural networks.

Scalable programming and data management: Parallel programming; databases; software profiling; cloud computing.

Time series analysis: Understanding variable sources with incomplete and noisy sampling; measures of periodicity; Gaussian processes; the LSST alert stream.

Image processing: Noisy astronomical detectors; processing pipelines; position, flux, and shape measurements; hands-on experience with the LSST image processing software stack.

Visualization: Visualization of large dimensional data sets; interactive visualization for exploration; visual hierarchies; the effective use of space, color, contrast, and textures.

Science communication: Understanding your audience; effective body language for communication; presentation design principles; using data to tell a story.

Required

Please indicate if your department has expertise in any of the following topics.

Software engineering: Building code repositories; object oriented programming; version control/GitHub; issue tracking; unit tests; continuous integration.

Statistics: Regression; frequentist vs. Bayesian methods; Gaussian processes; generative models; hierarchical models; missing information and selection effects.

Scalable programming and data management: Parallel programming; databases; software profiling; cloud computing.

Time series analysis: Understanding variable sources with incomplete and noisy sampling; measures of periodicity; Gaussian processes; the LSST alert stream.

Image processing: Noisy astronomical detectors; processing pipelines; position, flux, and shape measurements; hands-on experience with the LSST image processing software stack.

Visualization: Visualization of large dimensional data sets; interactive visualization for exploration; visual hierarchies; the effective use of space, color, contrast, and textures.

Science communication: Understanding your audience; effective body language for communication; presentation design principles; using data to tell a story.

If there are particular people in your department who might be interested in teaching a lesson during the session, please list them (and their expertise) below! (Note that these are just suggestions, so feel free to nominate whoever you feel would be appropriate.)

Local Logistics

Please tell us a bit about your location.

Are there lodging options within walking distance of your teaching facilities? Please provide specific details if possible (an estimated nightly cost for typical lodging is very helpful). *

Is there a major airport within easy access of your facilities? Please provide specific details if possible: *

Are there opportunities for social activities locally? Please provide specific details if possible: *

Anything else you'd like to add that we didn't ask about above?

Any questions?

Please contact Adam Miller (amiller AT northwestern DOT edu) or Bryan Scott (bryan.scott AT northwestern DOT edu) for clarification or additional information.

Thank you for your application!

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