Associate Vice Chancellor Emerita
Contact Information
- Office Address: Min H. Kao Building, Room 617
- E-mail: leparker@utk.edu
Education
- PhD in Computer Science, Massachusetts Institute of Technology, 1994
- MS in Computer Science, University of Tennessee, Knoxville, 1988
- BS in Computer Science, Tennessee Technological University, 1983
Biography
Associate Vice Chancellor Emerita Lynne E. Parker is a globally recognized leader in artificial intelligence (AI) and robotics, known for pioneering research in multi-robot systems and influential contributions to US AI policy. Her career spans academia, national laboratory, and senior leadership roles in the federal government.
Parker is Associate Vice Chancellor Emerita at the University of Tennessee, Knoxville (UT), and Founding Director of the AI Tennessee Initiative, helping position both the university and the state of Tennessee as leaders in the data-intensive knowledge economy.
At various times from 2018 through 2025, Parker served in the White House Office of Science and Technology Policy (OSTP), where she held multiple senior roles, including Principal Deputy Director, Deputy Chief Technology Officer of the United States, Assistant Director for AI, and Founding Director of the National AI Initiative Office. Earlier in her public service career, Parker served as Division Director for Information and Intelligent Systems at the National Science Foundation, managing the AI research portfolio and co-leading development of the 2016 National AI Research and Development Strategic Plan.
Across four US presidential administrations, she led the development of landmark national AI policies spanning research, governance, education, workforce development, international collaboration, and federal AI adoption. She also co-chaired the congressionally mandated National AI Research Resource Task Force, focused on expanding access to AI infrastructure and resources.
Parker began her research career at Oak Ridge National Laboratory, where she was a Distinguished Research and Development Staff Member and Group Leader. Her dissertation, “ALLIANCE,” a seminal distributed architecture for multi-robot cooperation, remains a cornerstone of multi-robot systems research. She joined the UT faculty in 2002, founding the Distributed Intelligence Laboratory and advancing research in distributed robotics, machine learning, sensor networks, and human-robot interaction. The work of Parker and her students led to some of the earliest research advances in multi-robot tracking of multiple moving objects, multi-robot motion planning, formation keeping, fault detection and diagnosis, and multi-robot object handling. Additional contributions include automatic synthesis of sensor-sharing in multi-robot coalitions, machine learning algorithms for anomaly detection in wireless sensor networks and in multi-robot teams, and peer-to-peer human-robot teaming.
She also held leadership roles including Interim Dean (2018) and Associate Dean for Faculty Affairs and Engagement (2017–2018) in UT’s Tickle College of Engineering.
Parker’s honors include the Presidential Early Career Award for Scientists and Engineers (PECASE), the IEEE Robotics and Automation Society’s George Saridis Leadership Award, the Computing Research Association’s Distinguished Service Award, the UT Distinguished Alumni Award, and the Nathan W. Dougherty Award. She is a Fellow of the Institute of Electrical and Electronics Engineers, the Association for the Advancement of Artificial Intelligence, and the American Association for the Advancement of Science, and a Distinguished Member of the Association for Computing Machinery. In 2024, she was appointed by Tennessee Governor Bill Lee to the state’s AI Advisory Council.
A Knoxville native, Parker earned a BS from Tennessee Technological University, an MS from UT, and a PhD from the Massachusetts Institute of Technology (MIT), all in computer science.
Research
- Distributed mobile robotics
- Human-robot interaction
- Distributed intelligence
- Sensor networks
- Artificial intelligence
- Machine learning
- Embedded systems
- Multi-agent systems