Signal Processing and Machine Learning

The field of Signal Processing includes the theory, algorithms, and applications related to processing information contained in data measured from natural phenomena as well as engineered systems. The main goal of signal processing is to generate, transform, transmit and learn from said data, hallmarked by the state-of-the-art communication technology, image, video and speech processing systems. The ever-growing dimensions of modern-day data, however, have created critical challenges ranging from storage and privacy considerations to data mining and computational bottlenecks, and have initiated a paradigm shift towards automated data-centric solutions, often referred to as Machine Learning. In particular, signal processing approaches play a central role in this new paradigm, due to their ability to harness the sheer dimensionality of data, and have proven successful in numerous applications such as medical imaging, healthcare, computer vision, and neural sciences and engineering. Our faculty in the area of Signal Processing and Machine Learning lead a wide range of high-quality research programs in key areas such as computer vision, information forensics, multimedia signal processing, wireless sensing and communication, adaptive and statistical signal processing, systems neuroscience, neuroimaging, and speech processing.

Faculty in this area of research include: