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Artificial Intelligence Summer Institute (AISI)
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.
Selections will be made on a rolling basis with the first round of selections starting January 1, 2020 and selections continuing until approximately ten applicants have been chosen. Applications must be submitted by January 31, 2020.
Areas of Interest
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.
Be currently enrolled as a senior undergraduate (must be graduating prior to start date and provide proof of degree) at time of application in a degree-seeking program at a regionally accredited U.S. college or university at 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 (6) 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 time of application
Or have graduated with a master's degree in the last six (6) 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 time of application