Agenda

Signal Processing Seminar

Semi-supervised learning for likelihood-based classifiers

Marco Loog
Bioinformatis/Pattern Recognition group

Bio: Marco Loog received an M.Sc. degree in mathematics from Utrecht University and in 2004 a Ph.D. degree from the Image Sciences Institute for the development and improvement of contextual statistical pattern recognition methods and their use in the processing and analysis of images. After this joyful event, he moved to Copenhagen where he acted as assistant and, eventually, associate professor next to which he worked as a research scientist at Nordic Bioscience. In 2008, after several splendid years in Denmark, Marco moved to Delft University of Technology where he now works as an assistant professor in the Pattern Recognition Laboratory. He currently is associate editor of Pattern Recognition and honorary full professor in pattern recognition at the University of Copenhagen. Marco's research interests primarily include all types of variations to supervised learning.

Overview of Signal Processing Seminar

Agenda

Microelectronics Colloquium

Sten Vollebregt, Massimo Mastrangeli, Daniele Cavallo

Tenure track colloquium

Daniele Cavallo (TS group); wideband phased arrays for future wireless communication terminals, Massimo Mastrangeli (ECTM Group); Towards smart organs-on-chip, Sten Vollebregt (ECTM group) Emerging electronic materials: from lab to fab

Signal Processing Seminar

Krishnaprasad Nambur Ramamohan

Signal processing algorithms for acoustic vector sensors

Symposium MRI for Low-Resource Setting

Steven Schiff, Johnes Obungoloch

Sustainable Low-Field MRI Technology for Point of Care Diagnostics in Low-Income Countries

Kick-off meeting of the project "A sustainable MRI system to diagnose hydrocephalus in Uganda"

Signal Processing Seminar

Peter Gerstoft

Machine learning in physical sciences

Machine learning (ML) is booming thanks to efforts promoted by Google. However, ML also has use in physical sciences. I start with a general overview of ML for supervised/unsupervised learning. Then I will focus on my applications of ML in array processing in seismology and ocean acoustics. This will include source localization using neural networks or graph processing. Final example is using ML-based tomography to obtain high-resolution subsurface geophysical structure in Long Beach, CA, from seismic noise recorded on a 5200-element array. This method exploits the dense sampling obtained by ambient noise processing on large arrays by learning a dictionary of local, or small-scale, geophysical features directly from the data.

Signal Processing Seminar

Aydin Rajabzadeh

manufacturing defect detection