Ozan Öktem is a Professor at KTH Royal Institute of Technology , specializing in applied mathematics with a focus on inverse problems, machine learning, and numerical analysis. He works in the Division of Numerical Analysis, Optimization and Systems Theory and develops theory and algorithms for solving inverse problems, particularly in medical imaging and cryogenic electron microscopy (Cryo-EM). His research integrates mathematical analysis, machine learning, and numerical methods to address challenges in recovering hidden model parameters from indirect observations. He emphasizes regularization techniques to stabilize ill-posed problems and computational feasibility for large-scale applications. Key areas include tomographic reconstruction, deep learning-based methods, and applications in biomedical imaging. Recent publications highlight his work on learned primal-dual architectures for CT, Riemannian geometry in protein dynamics analysis, and regularization strategies for Cryo-EM. Collaborations span computational biology, medical imaging, and optimization. He serves as course responsible for advanced courses in differential equations, inverse problems, and scientific computing.
