I am an Assistant Professor in the Mathematics of Imaging & AI group (DAMUT-EEMCS) at the University of Twente. My research interests broadly span different aspects of geometry in computer vision and machine learning. I am particularly interested in the applications of spectral and metric geometry for shape analysis, unsupervised learning, and image processing. 

The guiding theme of my research is the interplay between Geometry, Numerical Algorithms, and Machine Learning. I develop solutions for computational problems from different geometric modalities: images, graphs, and discrete representations of geometric shapes like 3D meshes and point clouds. Recent advances in deep learning provide compelling tools with neural networks to generate state-of-the-art performance for multiple challenges in image and geometry processing. However, despite this progress, they are generally considered to lack mathematical intuition and often demand significant amounts of clean, annotated training data for learning acceptable models. My research aspires to address these shortcomings by establishing a more meaningful merger between mathematically precise algorithms on one end, and data-intensive learning methods on the other. Therefore, besides improving performance, I aspire to improve mathematical interpretability and data efficiency in computational models. You can find some topics of my current interests in an Overview and, my papers with links to all details in Publications