There is national recognition for the importance of artificial intelligence (AI) and machine learning, especially with regard to United States economic and national security. In fact, the White House issued an Executive Order on American Leadership in Artificial Intelligence last month, and the FY 2020 President’s Budget Request for the Department of Energy includes $119 million for AI.
AI is undoubtedly a hot topic, and for good reason. With artificial intelligence and its cousin machine learning, scientists take on complex data challenges, match it with the power of supercomputing and smart algorithms, and get world-changing results.
Learn about AI Alongside Expert Scientists & Powerful Supercomputing
If you’re an undergraduate, graduate student or recent graduate, you may be looking for ways to get in on the action. The U.S. Department of Energy has an opportunity for you this summer at Oak Ridge National Laboratory’s (ORNL) Artificial Intelligence Summer Institute (AISI), a brand new program that allows students and graduates to participate directly with the teams currently using AI and machine learning to tackle some of the nation’s greatest scientific challenges.
From June 10 to August 16, students and recent graduates will join one of two teams at ORNL who have just begun their projects. One dives into nuclear safety issues and will aim to use AI to reduce the need for human inspections of hazardous material storage by automating the detection of any pits or cracks on nuclear fuel canister surfaces.
The second project team is researching human health, using AI to study gut microbiomes to understand how they interact, grow, and reproduce. If the behavior can be modeled through AI and machine learning, this research could have significant health care impacts.
Many types of current student or graduate scientists could be right for this educational program. The teams need a mix of research interests from machine learning, programming and code, statistics, supercomputing, mathematics, and more to best come up with creative approaches for their summer projects.
Dr. Catherine “Katie” Schuman, research scientist at ORNL and director of the summer institute, designed the experience to give students and recent graduates “a view of what it is really like to collaborate in diverse teams, learn from domain scientists on exciting and challenging problems while bringing their own skills and experience… and see a project as a whole come to fruition.” Dr. Schuman earned her doctorate in computer science from the University of Tennessee in 2015 and her dissertation studied models and algorithms for neuromorphic computing as well as other topics in AI and machine learning. By joining the 10-week program, Dr. Schuman stated “you can see what it is really like to be a part of AI at the lab.”
Once students and graduates apply for the AISI program, laboratory researchers will evaluate the applications and make selections. AISI is a new pilot program and will have a limited number of slots (10-15) for participants this summer, with an eye for future expansion. In cases where applicants appear to fit better in other ORNL research participation programs, applications will be shared with other potential mentors outside of the AISI program.
“What we do is really show participants what they can do with their career,” said Leslie Fox, a section manager for the Oak Ridge Institute for Science and Education, which administers research participation programs like AISI for the U.S. Department of Energy. “We are extremely excited to bring them in, meet them, and have the opportunity to collaborate with them in the future.”
Throughout the summer participants will attend professional development workshops and seminars about the lab’s research, hearing from outside speakers and lab staff about how they apply AI and machine learning to particular challenges. Beyond being part of the AI research at the lab, the participants in the pilot program will develop a network of peers and mentors with lasting connections and gain an understanding of the AI research conducted at ORNL.
To apply, interested candidates will need to share basic education and experience information, their goals, research interest areas, and expertise, an unofficial academic record or official transcript, resume, and contact information for recommenders. See more details and the application here.