Machine learning has revolutionized the way we do data science. In particular, deep neural networks have closed the performance gap between humans and machines in disciplines such as vision, image processing, audio processing, and natural language processing. Nonetheless, learning from unstructured high-dimensional empirical observations (for example gene measurements) is usually impossible for humans and may be difficult for machines. In particular, machine learning becomes extremely challenging since empirical measurements are often unlabeled, noisy, sparse, imbalanced, and high dimensional.

My main research goal is to develop automatic methods that would lead to novel scientific findings (in biology, physics, medicine, etc.). To achieve this goal I am currently developing deep learning methodologies that tackle the following tasks: