Date: 12th May
Hour: 2 p.m.
Venue: Room EA2, North Tower, Alameda Campus
«The Instituto de Telecomunicações is pleased to announce the talk “Deep Learning for Inverse Problems: Theoretical Perspectives, Algorithms, and Applications”, by Prof. Miguel Rodrigues.
Recent years have witnessed a surge of interest in deep learning methods to tackle inverse problems arising in various domains such as medical imaging, remote sensing, and the arts and humanities.
This talk offers an overview of recent advances in the foundations and applications of deep learning for inverse problems, with a focus on model-based deep learning methods.
Concretely, this talk will introduce theoretical advances in the area of model-based learning, including learning guarantees; it will also introduce algorithmic advances in model-based learning; and, finally, it will showcase a portfolio of emerging signal & image processing challenges that benefit from model-based learning.
Free event.
Bio:
Miguel Rodrigues is a Professor of Information Theory and Processing at University College London; he leads the Information, Inference, and Machine Learning Lab at UCL, and he has also been the founder and director of the master’s program in Integrated Machine Learning Systems at UCL. He is also currently the UCL Turing University Lead and a Turing Fellow with the Alan Turing Institute — the UK National Institute of Data Science and Artificial Intelligence.
He held various appointments with various institutions worldwide including Cambridge University, Princeton University, Duke University, and the University of Porto, Portugal. He obtained an undergraduate degree in Electrical and Computer Engineering from the Faculty of Engineering of the University of Porto, Portugal, and a PhD degree in Electronic and Electrical Engineering from University College London.
Miguel Rodrigues’s research lies in the general areas of information theory, information processing, and machine learning. His most relevant contributions have ranged from the information-theoretic analysis and design of communications systems, information-theoretic security, information-theoretic analysis and design of sensing systems, and the information-theoretic foundations of machine learning. He serves or has served as Editor of IEEE BITS, Editor of the IEEE Transactions on Information Theory, and Lead Guest Editor of the Special Issue on “Information-Theoretic Methods in Data Acquisition, Analysis, and Processing” of the IEEE Journal on Selected Topics in Signal Processing. He was the recipient of the IEEE Communications and Information Theory Societies Joint Paper Award 2011 and is also an IEEE Fellow.»
