Aleksandar Doknic joined the research network as a PhD student in January 2021 and received his master's degree in Computer Science at the University of Vienna in 2020. He works on the topic of explainable models. His research focuses on analyzing and understanding neural network models for scientific applications by using interactive visualization techniques. One interesting approach is the exploration of high-dimensional parameter spaces and loss landscapes with sampling methods. He is particularly interested in interdisciplinary research and machine learning applications in natural sciences.

We are very happy to have Aleksandar at our research network!