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Ozan Öktem is a professor at KTH Royal Institute of Technology, specializing in the field of applied mathematics. His research focuses on developing theories and algorithms for solving inverse problems, which aim to recover hidden model parameters from indirect observations. Öktem's work involves simulating data in reverse, addressing the complexities of ill-posed problems where multiple solutions may be consistent with the data, and where small variations in the data can lead to significant changes in solutions. A core aspect of his research includes the ability to simulate noise-free data through forward operators and managing intrinsic instability necessitated by ill-posed challenges. He emphasizes the importance of controlling discretization errors and enforcing stability through regularization methods. His expertise further extends to the computational feasibility of large-scale inverse problems, with applications in critical areas such as medical imaging. His work is situated at the intersection of mathematical analysis, machine learning, statistics, and numerical analysis.
KTH Royal Institute of Technology • Stockholm, Sweden
Engaged in teaching and research primarily focused in applied mathematics and inverse problems.
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