Clemens Heitzinger

Prof. Clemens Heitzinger is an Associate Professor at TU Wien, affiliated with the Forschungsbereich Machine Learning (E194-06). His research focuses on interdisciplinary applications of machine learning, computational methods, and nanotechnology. Key areas include reinforcement learning for healthcare optimization, stochastic modeling of PDE systems, and Bayesian inversion in sensor design. He leads the project PDE models for nanotechnology under the Scientific Computing and Modelling department (E101-03).

His work spans medical imaging (e.g., electrical impedance tomography), nanopore sequencing, and nanoscale sensor development. Notable contributions include algorithms for corticosteroid therapy optimization in sepsis and superhuman performance in sepsis prediction via distributional reinforcement learning. He has advised over 13 PhD and Master's students, including Tobias Kietreiber, Sebastian Bittner, and Leila Taghizadeh.

His research methodologies integrate machine learning with numerical analysis, emphasizing uncertainty quantification and PDE modeling. Recent trends in his publications highlight applications in personalized healthcare, industrial anomaly detection, and computational fluid dynamics in nanoscale systems.

While no scientific awards are explicitly mentioned, his work has been published in high-impact journals and conference proceedings, reflecting his contributions to computational science and engineering.