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Applications are closed for the 2020 Artificial Intelligence Summer Institute. Check back in the fall for the summer 2021 application!

The Artificial Intelligence Summer Institute (AISI) will collaborate with Oak Ridge National Laboratory (ORNL) scientists on solving problems of national and scientific interest, engage in educational and professional development opportunities, explore career opportunities at national laboratories, and interact with and present their research to ORNL’s scientists.  

As part of the summer institute, students will be organized into teams with diverse educational backgrounds and develop their skills to help solve scientific challenges using artificial intelligence, machine learning, and data science. They will learn from ORNL mentors who have expertise in artificial intelligence and machine learning and/or domain sciences such as physics, materials, or biology. Students will participate in an educational and professional development seminar series. They will also learn and participate in scientific communication exercises to prepare them for a career in scientific research, including oral and poster presentations and technical reports.


Students will participate in projects in one or more of the following research areas:

  • Fundamental AI/ML: The team for fundamental AI/ML research focuses on developing novel AI/ML methodologies in order to address grand mathematical challenges, e.g., high dimensionality, lack of robustness, uncertainty quantification, etc., arising from scientific applications at DOE experimental and high-performance computing facilities.
  • Scientific Imagery/Image Analytics: At ORNL we are tackling scientific deep learning applications in medical imaging, manufacturing, and geospatial AI. Our projects are pushing the state of the art in 2D and 3D image segmentation, classification, registration, and tomographic reconstruction leveraging ORNL's unique capabilities in high performance computing and AI.
  • Reinforcement Learning: The reinforcement learning team focuses on model-based reinforcement learning with Bayesian methodologies, which are particularly apt for scientific settings, which demand utmost data efficiency, as experiments are necessarily expensive.  Reinforcement learning methods are used in many scientific applications, such as in materials synthesis to provide automated guidance of synthesis trajectories towards desired material properties.
  • Scalability: The scalability efforts focus on developing novel scalable machine learning algorithms on leadership-class computing resources such as the world’s fastest supercomputer, Summit. A team of domain-specialists and computer scientists leverages the vast troves of data generated within the DOE complex and work together closely to push the performance boundaries of these high-performance machine learning methods by improving the training speed and inference quality in a variety of large-scale science and technology applications.

Program Dates

The program is scheduled to begin on June 1, 2020, and end on August 7, 2020.

Application Deadline

Applications must be submitted by January 31, 2020. To be considered for selection, applications must be fully completed and submitted and two recommendations must be received within the Zintellect system. Applicants will be notified when selections are completed. For best chance of selection, you must complete your application as soon as possible.


Image credit: Carlos Jones/ORNL


Eligibility Requirements

  • Be currently enrolled as a senior undergraduate (must be graduating prior to start date and provide proof of degree) at the time of application in a degree-seeking program at a regionally accredited U.S. college or university at the time of application 
    • Must provide proof of acceptance/enrollment for fall 2020 in a degree-seeking graduate program at a regionally accredited U.S. college or university (provide with application if available or by May 1, 2020, if selected)
  • Or have graduated with a bachelor's degree in the last six months (at the time of application) from a regionally accredited U.S. college or university
    • Must provide proof of acceptance/enrollment for fall 2020 in a graduate degree program at a regionally accredited U.S. college or university (provide with application if available or by May 1, 2020, if selected)
  • Or be currently enrolled in a master's degree-seeking program at a regionally accredited U.S. college or university at the time of application
  • Or have graduated with a master's degree in the last six months (at the time of application) from a regionally accredited U.S. college or university
  • Or be a current Ph.D. student or Ph.D. candidate at the time of application
  • Have a cumulative minimum GPA of 3.0 on a 4.0 scale
  • Be 18 years of age
  • Be a U.S. Citizen or Lawful Permanent Resident (LPR) at the time of application

Meet a Participant

  • "I would wholeheartedly recommend this program to others. It is an amazing opportunity to participate on an interesting project, learn from professionals in the field, gain experience both professionally and academically, and meet some awesome people from various disciplines and backgrounds." -Preston Robinette 

    Read more about Preston.

  • "The program has put me in a better position to achieve my future goals. I feel that the experience gained can definitely be leveraged in either an academic or industry setting, and I have a much clearer idea of the state of the field and how I can improve my skillset." -Daniel Elbrecht 

    Read more about Daniel.

  • "This program has given me valuable experiences in tackling machine learning problems and how a national lab operates in a team-based environment. Coming into the program I had no idea how work was done at national labs or how machine learning tied into science problems. After this program I feel that I am comfortable in collaborating with others coming from various disciplines and employing machine learning to solve the tough science questions that need to be addressed." -Daniel Schultz 

    Read more about Daniel.

  • "This experience has generally just broadened my perspective. I had a lot of material thrown at me over the course of the summer, so I had to learn a lot on the fly. It was really neat having the experience of teaming up with a group of people to tackle a really daunting project." -Austin Li 

    Read more about Austin.

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