My current research focuses on understanding deep learning by studying the complex interactions between datasets, architectures and optimization. In my work, I am providing a novel framework to design better and more reliable neural networks that exploit prior knowledge about the world.
Before starting my PhD, I lived in The Netherlands for two years, where I worked on sampling theory for tensors and network data at TU Delft. In the past, I have also spent some great time in Germany at Philips Research in Hamburg doing research on self-supervised deep learning for medical imaging.
