juliane mueller

Juliane Mueller

Group Manager III-Computational Science


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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

Education

Ph.D., Applied Mathematics, Tampere University of Technology

M.S., Applied Mathematics, Freiberg University of Mining and Technology

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)

Associations and Memberships

Member, Society for Industrial and Applied Mathematics

Member, Institute for Operations Research and the Management Sciences

Member, Joint Committee on Women in the Mathematical Sciences

Representative, Joint Committee on Women in the Mathematical Sciences for Society for Industrial and Applied Mathematics Diversity Advisory Committee (2022-2025)

Vice Chair, Institute for Operations Research and the Management Sciences for Computational Optimization and Software (2022-2023)

Featured Work

Surrogate Optimization of Deep Neural Networks for Groundwater Predictions, Journal of Global Optimization (2021)

Classical Optimizers for Noisy Intermediate-Scale Quantum Devices, 2020 IEEE International Conference on Quantum Computing and Engineering (2020)

Surrogate Optimization of Computationally Expensive Black-Box Problems With Hidden Constraints, INFORMS Journal on Computing (2019)

SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems, INFORMS Journal on Computing (2017)

MISO: Mixed-Integer Surrogate Optimization Framework, Optimization and Engineering (2015)

Influence of Ensemble Surrogate Models and Sampling Strategy on the Solution Quality of Algorithms for Computationally Expensive Black-Box Global Optimization Problems, Journal of Global Optimization (2014)

SO-MI: A Surrogate Model Algorithm for Computationally Expensive Nonlinear Mixed-Integer Black-Box Global Optimization Problems, Computers & Operations Research (2013)


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