CCSE

Juliane Mueller

Staff Scientist

Phone: +1 510 495 2872

Web: https://ccse.lbl.gov/people/julianem

Juliane Mueller joined LBL in 2014 as a Luis Alvarez Postdoctoral Fellow and is now a Staff Scientist in the Computational Research Division. She develops algorithms for black-box optimization problems whose objective and constraint function evaluations are computed from time-consuming computer simulations.

Research Interests Related to Simulation Optimization

derivative-free global optimization using surrogate models (response surface models)
continuous, integer, and mixed-integer optimization
multi-objective, multi-level, multi-fidelity optimization
non-linear, black-box constrained optimization
optimization under uncertainty
decision support
application areas: combustion, cosmology, event generator tuning, groundwater management, climate models, environmental engineering, fuel and engine design

Google Scholar profile

Journal Articles

O Karslıoğlu, M Gehlmann, J Müller, S Nemšák, JA Sethian, A Kaduwela, H Bluhm, C Fadley, "An Efficient Algorithm for Automatic Structure Optimization in X-ray Standing-Wave Experiments", Journal of Electron Spectroscopy and Related Phenomena, January 1, 2019,

J. Müller, M. Day, "Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints", INFORMS Journal on Computing, 2019, 31:633-845,

W. Langhans, J. Mueller, W.D. Collins, "Optimization of the Eddy-Diffusivity/Mass-Flux shallow cumulus and boundary-layer parametrization using surrogate models", Journal of Advances in Modeling Earth Systems (JAMES), Vol 11, Issue 2,, 2019,

G Conti, S Nemšák, C-T Kuo, M Gehlmann, C Conlon, A Keqi, A Rattanachata, O Karslıoğlu, J Mueller, J Sethian, H Bluhm, JE Rault, JP Rueff, H Fang, A Javey, CS Fadley, "Characterization of free-standing InAs quantum membranes by standing wave hard x-ray photoemission spectroscopy", APL Materials, May 1, 2018,

J Muller, "SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems", INFORMS Journal on Computing, July 31, 2017, 29:581-596,

J Müller, JD Woodbury, "GOSAC: global optimization with surrogate approximation of constraints", Journal of Global Optimization, 2017, 69:117--136, doi: 10.1007/s10898-017-0496-y

J Müller, "MISO: mixed-integer surrogate optimization framework", Optimization and Engineering, January 2016, 17:177--203, doi: 10.1007/s11081-015-9281-2

J Müller, R Paudel, CA Shoemaker, J Woodbury, Y Wang, N Mahowald, "CH4 parameter estimation in CLM4.5bgc using surrogate global optimization", Geoscientific Model Development, January 2015, 8:3285--3310, doi: 10.5194/gmd-8-3285-2015

J Müller, CA Shoemaker, R Piché, "SO-I: A surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications", Journal of Global Optimization, 2014, 59:865--889, doi: 10.1007/s10898-013-0101-y

J Müller, CA Shoemaker, "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, 60:123--144, doi: 10.1007/s10898-014-0184-0

J Müller, CA Shoemaker, R Piché, "SO-MI: A surrogate model algorithm for computationally expensive nonlinear mixed-integer black-box global optimization problems", Computers and Operations Research, 2013, 40:1383--1400, doi: 10.1016/j.cor.2012.08.022

J Mueller, R Piche, "Mixture surrogate models based on Dempster-Shafer theory for global optimization problems", Journal of Global Optimization, September 1, 2011, 51,

Conference Papers

V. Dumont, C. Garner, A. Trivedi, C. Jones, V. Ganapati, J. Mueller, T. Perciano, M. Kiran, and M. Day, "HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization", 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), November 15, 2021,

Erich Lohrmann, Zarija Lukic, Dmitriy Morozov, Juliane Mueller, "Programmable In Situ System for Iterative Workflows", Lecture Notes in Computer Science, Cham, Springer International Publishing, January 1, 2018, 10773:122--131, doi: 10.1007/978-3-319-77398-8\_7

J Müller, T Krityakierne, CA Shoemaker, "SO-MODS: Optimization for high dimensional computationally expensive multi-modal functions with surrogate search", Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, 2014, 1092--1099, doi: 10.1109/CEC.2014.6900599

Others

J Müller, B Faybishenko, D Agarwal, S Bailey, C Jiang, Y Ryu, C Tull, L Ramakrishnan, Assessing data change in scientific datasets, Concurrency and Computation: Practice and Experience, 2021, doi: 10.1002/cpe.6245