Juliane Mueller


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Juliane “Juli” Mueller is the manager of the Artificial Intelligence, Learning, and Intelligent Systems (ALIS) group within the Computational Science Center at NREL. Juli’s background is in the development of numerical optimization algorithms for black-box and compute-intensive problems where analytic descriptions of objective and constraint functions are not available. Her algorithm developments include surrogate modeling and active learning. In the past, she has applied these optimization algorithms to a variety of U.S. Department of Energy -relevant problems, including environmental applications, fuel search, quantum computing, and high-energy physics. Most recently, Juli’s work as focused on tuning deep learning model architectures with the goal to find models that make robust and reliable predictions. As group leader of ALIS, it is her goal to develop optimization and machine learning capabilities that enable researchers across all NREL applications to accelerate their science. 


Research Interests

Derivative-free optimization algorithm development

Surrogate modeling (including Gaussian process models, radial basis functions, machine learning models)

Active learning and sampling methods

Machine learning

Professional Experience

ALIS Group Manager, NREL (2022–present)

Staff Scientist, Lawrence Berkeley National Laboratory (2021–2022)

Research Scientist, Lawrence Berkeley National Laboratory (2017–2021)

Luis W. Alvarez Postdoctoral Fellow in Computing Sciences, Lawrence Berkeley National Laboratory (2014–2017)

Postdoctoral Researcher, Cornell University (2013–2014)

Education/Academic Qualification

Master, Applied Mathematics, Freiberg University of Mining and Technology

PhD, Applied Mathematics, Tampere University of Technology


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