I am a postdoctoral researcher at the Scientific Computing and Optimization group at the Interdisciplinary Center for Scientific Computing of Heidelberg University since September 2022.
Furthermore, I am a GAMM Junior and treasurer of EMYA.
You can find my CV here.
Projects
- Operator Learning for Optimal Control: Approximation and Statistical Theory (with Sven Wang and Jakob Zech) in DFG SPP 2298 Foundations of Deep Learning
Research Interests
My research mainly focuses on
- Mathematical Machine Learning
- Optimal Control with Partial Differential Equations
- Adaptive Algorithms
- Variational Discretization
Recent teaching
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2024 SSNonlinear Optimization (Lecture)
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2023 WSGrundlagen der Optimierung (Lecture)
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2023 SSMathematical Machine Learning (Seminar)
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2023 SSNonlinear Optimization (Lecture)
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2022 WSMathematical Machine Learning (Seminar)
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2022 WSWeiterführende Themen der Numerik (Seminar)
Recent events organized
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2025-09-01 -- 2025-09-05 European Conference on Numerical Mathematics and Advanced Applications (ENUMATH)
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2023-09-25 -- 2023-09-27 European Conference on Computational Optimization (EUCCO)
Currently supervising
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M.Sc. Thesis of
Optimization Methods using Operator Learning and Applications in Optimal ControlM.Sc. Scientific Computing, Heidelberg UniversitySupervision: Evelyn Herberg and Roland Herzog
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B.Sc. Thesis of
Invex Optimization ProblemsB.Sc. MathematikSupervision: Roland Herzog and Evelyn Herberg
Latest publications
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An inexact semismooth Newton method with application to adaptive randomized sketching for dynamic optimizationFinite Elements in Analysis and Design 228, 2024
bibtex
@ARTICLE{AlshehriAntilHerbergKouri:2024:1, AUTHOR = {Alshehri, Mohammed and Antil, Harbir and Herberg, Evelyn and Kouri, Drew P.}, PUBLISHER = {Elsevier BV}, DATE = {2024-01}, DOI = {10.1016/j.finel.2023.104052}, JOURNALTITLE = {Finite Elements in Analysis and Design}, PAGES = {104052}, TITLE = {An inexact semismooth Newton method with application to adaptive randomized sketching for dynamic optimization}, VOLUME = {228}, }
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Sensitivity-based layer insertion for residual and feedforward neural networks,
2023
bibtex
@ONLINE{HerbergHerzogKoehneKreisSchiela:2023:1, AUTHOR = {Herberg, Evelyn and Herzog, Roland and Köhne, Frederik and Kreis, Leonie and Schiela, Anton}, DATE = {2023-11}, EPRINT = {2311.15995}, EPRINTTYPE = {arXiv}, TITLE = {Sensitivity-based layer insertion for residual and feedforward neural networks}, }
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Variationelle Diskretisierung für Optimale Steuerung mit MaßkontrollenMitteilungen der Deutschen Mathematiker-Vereinigung 31(3), p.156-159, 2023
bibtex
@ARTICLE{Herberg:2023:2, AUTHOR = {Herberg, Evelyn}, PUBLISHER = {Walter de Gruyter}, DATE = {2023-09}, DOI = {10.1515/dmvm-2023-0053}, JOURNALTITLE = {Mitteilungen der Deutschen Mathematiker-Vereinigung}, NUMBER = {3}, PAGES = {156--159}, TITLE = {Variationelle Diskretisierung für Optimale Steuerung mit Maßkontrollen}, VOLUME = {31}, }
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Adaptive randomized sketching for dynamic nonsmooth optimizationModel Validation and Uncertainty Quantification 3, p.107-116, 2023
bibtex
@INCOLLECTION{BaraldiHerbergKouriAntil:2023:1, AUTHOR = {Baraldi, Robert J. and Herberg, Evelyn and Kouri, Drew P. and Antil, Harbir}, PUBLISHER = {Springer Nature Switzerland}, BOOKTITLE = {Model Validation and Uncertainty Quantification}, DATE = {2023-06}, DOI = {10.1007/978-3-031-37003-8_17}, PAGES = {107--116}, TITLE = {Adaptive randomized sketching for dynamic nonsmooth optimization}, VOLUME = {3}, }
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Lecture Notes: Neural Network Architectures,
2023
bibtex
@ONLINE{Herberg:2023:1, AUTHOR = {Herberg, Evelyn}, DATE = {2023-04}, EPRINT = {2304.05133}, EPRINTTYPE = {arXiv}, TITLE = {Lecture Notes: Neural Network Architectures}, }
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An optimal time variable learning framework for deep neural networksAnnals of Mathematical Sciences and Applications 8(3), p.501-543, 2023
bibtex
@ARTICLE{AntilDiazHerberg:2023:1, AUTHOR = {Antil, Harbir and Dı́az, Hugo and Herberg, Evelyn}, PUBLISHER = {International Press of Boston}, DATE = {2023}, DOI = {10.4310/amsa.2023.v8.n3.a4}, EPRINT = {2204.08528}, JOURNALTITLE = {Annals of Mathematical Sciences and Applications}, NUMBER = {3}, PAGES = {501--543}, TITLE = {An optimal time variable learning framework for deep neural networks}, VOLUME = {8}, }
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Time regularization in optimal time variable learningProceedings in Applied Mathematics and Mechanics, 2023
bibtex
@ARTICLE{HerbergHerzogKoehne:2023:2, AUTHOR = {Herberg, Evelyn and Herzog, Roland and Köhne, Frederik}, DATE = {2023}, DOI = {10.1002/pamm.202300299}, EPRINT = {2306.16111}, EPRINTTYPE = {arXiv}, JOURNALTITLE = {Proceedings in Applied Mathematics and Mechanics}, TITLE = {Time regularization in optimal time variable learning}, }
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Variational discretization approach applied to an optimal control problem with bounded measure controlsOptimization and Control for Partial Differential Equations, p.113-136, 2022
bibtex
@INCOLLECTION{HerbergHinze:2022:2, AUTHOR = {Herberg, Evelyn and Hinze, Michael}, PUBLISHER = {De Gruyter}, BOOKTITLE = {Optimization and Control for Partial Differential Equations}, DATE = {2022-03}, DOI = {10.1515/9783110695984-006}, PAGES = {113--136}, TITLE = {Variational discretization approach applied to an optimal control problem with bounded measure controls}, }