Matthias Chung

Matthias Chung
Associate Professor

Department of Mathematics
Emory University

Address

Mathematics & Science Center

400 Dowman Drive

Office: E410

matthias.chung@emory

<codes> Research Group

Office hours: by appointment

Research keywords

inverse problems, scientific machine learning, computational biology & medicine, numerical analysis, optimization, optimal experimental design, scientific computing, applied linear algebra, dynamical systems, uncertainty quantification.

Publications

see Google Scholar

News

Gene Golub SIAM Summer School 2024 in SIAM News
Workshop on Big Data Inverse Problems

ICMS, Bayes Centre · Edinburgh, UKMay 20-24, 2024organized with Matthias Ehrhardt, University of Bath; and Carola Bibiane Schönlieb, University of Cambridge

2024 Gene Golub SIAM Summer School on

Iterative and Randomized Methods for Large-Scale Inverse ProblemsEscuela Politécnica Nacional, Quito, EcuadorJuly 22- August 2, 2024organized with Juan Carlos de los Reyes, Escuela Politécnica Nacional; Petros Drineas, Purdue University; Rosemary Renaut, Arizona State University; and Alex Townsend, Cornell University

Research

“Seht ihr den Mond dort stehen? Er ist nur halb zu sehen, Und ist doch rund und schön! So sind wohl manche Sachen, Die wir getrost belachen, Weil unsre Augen sie nicht sehn.” Matthias Claudius (Abendlied, um 1778) The rapidly evolving field of data science recognizes the urgent need for novel computational methods to overcome parameter inference and uncertainty quantification challenges to ultimately make informed decisions. Emerging fields such as machine learning and uncertainty quantification heavily rely on efficient computational methods for inverse problems.My research lies within this cross-disciplinary field of inverse problems, which aims at inferring information from the physical model given observations. Techniques developed in this field are of increasing interest to communities such as system biology, systems engineering, and medical and geophysical imaging, to name a few.The main challenges toward obtaining meaningful real-time solutions to large, data-intensive inverse problems are the ill-posedness of the problem, large parameter dimensions, and/or complex model constraints. My research addresses these mathematical, computational, and statistical challenges by exploiting a combination of tools from optimization, dynamical systems, parameter estimation, and optimization, and applied linear algebra. It is my comprehensive yet broad range of specialties that has enabled me to make significant impacts in the development, analysis, implementation, and validation of new numerical methods and tools for solving inverse problems.Many of my research projects are crossdisciplinary and driven by applications, with the ultimate goal being a direct transformational impact on fields such as computational biology, geophysical inversion, machine learning, computational ecology, and medical imaging.

Professional Societies

Current Teaching

Fall Semester 2024

MATH 347, Nonlinear Optimization

MW: 8:30 am- 9:45 am (Math & Science Center - E301)

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