Juliane Mueller

Juliane Mueller serves as Group Manager of the Artificial Intelligence, Learning, and Intelligent Systems (ALIS) group within the Computational Science Center at the National Renewable Energy Laboratory (NREL), leading optimization and machine learning initiatives since 2022. Previously, she held research scientist positions at Lawrence Berkeley National Laboratory (2014-2022) and conducted postdoctoral work at Cornell University.

She holds a Master's in Applied Mathematics from Freiberg University of Mining and Technology and a Ph.D. in Applied Mathematics from Tampere University of Technology.

Her research pioneers derivative-free optimization algorithms for black-box problems, specializing in surrogate modeling (Gaussian processes, radial basis functions), active learning, and machine learning applications. Current work focuses on tuning deep learning architectures for robust predictions in renewable energy systems, environmental science, and quantum computing, with emphasis on accelerating scientific discovery through computational innovation.

Analysis of her 2025 publications reveals a dominant trend toward AI-driven solutions for energy challenges, particularly in inverse design problems, explainable AI for renewable systems, and computational resource optimization. These works consistently integrate machine learning with domain-specific physics to enhance model fidelity while addressing scalability constraints.

• Luis W. Alvarez Postdoctoral Fellowship (Lawrence Berkeley National Laboratory)

As ALIS group leader, Mueller directs a multidisciplinary team developing optimization frameworks that enable cross-cutting scientific acceleration at NREL, with particular focus on creating reliable predictive models for Department of Energy mission-critical applications in biofuel engineering and photonic material design.