Machine Learning

The research of Rudolf Mößbauer Tenure Track Professor Reinhard Heckel lies in the intersection of machine learning and signal/information processing, with a focus on the following areas:

• 1) Deep networks for imaging. Learning-based methods, and in particular deep neural networks, are starting to be used for important imaging applications, such as the newest generation of medical imaging systems and smartphones. We develop algorithms that yield higher-resolution images at shorter scan times than conventional methods, and we study their performance and limitations.
• 2) The foundations of machine learning and information processing. We develop learning algorithms and study their theoretical foundations. We are particularly interested in learning from few and noisy examples since, even in the age of big data, training data can often be surprisingly scarce and noisy.
• 3) DNA data storage. Because of its longevity and enormous information density, DNA is a promising storage medium for digital data. We co-developed the first robust DNA storage system, and we are conducting ongoing research toward future DNA storage systems. We used our technology for the first commercial application of DNA data storage in 2020, and we are now exploring the role DNA can play in information technologies more broadly.