UC Riverside joins LLNL for a virtual Data Science Challenge

August 30 – September 17, 2021

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Meet your mentors >>


Vagelis Papalexakis, UC Riverside | epapalex [at] cs.ucr.edu

Brian Gallagher, LLNL | gallagher23 [at] llnl.gov

Jennifer Bellig, LLNL admin | bellig1 [at] llnl.gov

You could help advance world-class science this summer!

We’re building multidisciplinary teams to tackle real-world data science challenges. And we need 20 of UC Riverside’s finest scholars to make it happen.

Apply by May 23, 2021 (midnight PDT): llnl-data-science-challenge [at] llnl.gov

Your application must include:

  • Statement of interest (1 page)
  • Resume
  • 1 letter of recommendation by a UC Riverside faculty member sent on your behalf to the e-mail address above (subject line: "Recommendation Letter <Your Last Name>")
  • Graduate students: Include example(s) of your leadership and research experience in your statement of interest. The recommending faculty member should be your advisor.
  • Send your all your application materials to the email address listed above.

What it takes

  • Undergraduates interested in data science or related disciplines
  • Graduate students experienced in research or applying skills to a research environment
  • Students actively pursuing a degree in mathematics, computer science, engineering, science, or other relevant fields
  • Students with computational experience

What to expect

Don’t miss this unique opportunity! The experience will be unlike any other. This intensive virtual training program provides challenging exercises and assignments, virtual tours, and seminars. For 3 weeks, you’ll work on an important data science problem while learning from experts, networking with peers, and developing skills for future internships. You’ll also get a taste of day-to-day life at LLNL, where we have a passion for national service. Read about the 2020 UC Merced Challenge.

You’ll solve an exciting problem in astronomy for planetary defense. Near Earth Objects (NEOs) include potentially hazardous asteroids that pose a large or even existential threat to humanity. At the same time, NEOs offer clues about the origin of our Solar System. Modern astronomical sky surveys are collecting substantial data on NEOs, but it is difficult to distinguish objects of interest from more distant asteroids and stars in optical telescope images. In this challenge problem, you will apply machine learning methods to recent time-domain optical astronomy data to detect, distinguish, and characterize asteroids that may pass near Earth in the future. Read about your LLNL mentors.

*compensation will be provided

Undergraduate students will:

  • Work with scientists, engineers, and technical staff to support to LLNL projects
  • Participate in a real-world space science data problem
  • Gather and analyze data in support of scientific research
  • Participate in research and challenge problem evaluation discussions
  • Present results to scientists, engineers, and technical staff during final student briefing

Graduate students will:

  • Serve as team lead, guiding the research direction for 4 undergraduate students
  • Provide advanced technical support to LLNL scientists, engineers, and technical staff
  • Demonstrate work via presentations