Matthias Chung
Associate Professor
Department of Mathematics
Emory University
Address
Mathematics & Science Center
400 Dowman Drive
Office: E410
matthias.chung@emory
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.Research keywords
News
News
ICMS, Bayes Centre · Edinburgh, UKMay 20-24, 2024organized with Matthias Ehrhardt, University of Bath; and Carola Bibiane Schönlieb, University of CambridgeIterative 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
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.
Current Teaching
Current Teaching
Fall Semester 2023
MATH 571, Numerical Optimization
TTh: 8:30 am- 9:45 am (Math & Science Center - E408)