Bert de Vries
Contactbert.de.vries@ tue.nl Flux 7.101
Bert de Vries is Part-time Professor in the Signal Processing Systems group. His research focuses on the development of intelligent autonomous agents that learn from in-situ interactions with their environment. And on using these agents to automate the development of novel signal processing algorithms, see biaslab.org. Our research draws inspiration from diverse fields including computational neuroscience, Bayesian machine learning and signal processing systems. A current major application area concerns personalization of medical signal processing systems such as hearing aid algorithms. In the past, De Vries contributed to research projects over a wide range of signal and image processing topics, such as word spotting, financial market prediction and breast cancer detection from mammograms.
I believe that development of signal processing systems will in the future be largely automated by autonomously operating agents that learn purposeful (signal processing) behavior from situated environmental interactions.
Bert de Vries received M.Sc. (1986) and Ph.D. (1991) degrees in Electrical Engineering from Eindhoven University of Technology (TU/e) and the University of Florida, respectively. From 1992 to 1999, he worked as a research scientist at Sarnoff Research Center in Princeton (NJ, USA). Since 1999, he has been employed in the hearing aids industry, both in engineering and managerial positions. De Vries was appointed part-time professor in the Signal Processing Systems Group at TU/e in 2012.
A factor graph description of deep temporal active inferenceFrontiers in Computational Neuroscience (2017)
The graphical brain: belief propagation and active inferenceNetwork Neuroscience (2017)
A probabilistic modeling approach to hearing loss compensationIEEE Transactions on Audio, Speech, and Language Processing (2016)
An adaptive Kalman filter for ECG signal enhancementIEEE Transactions on Biomedical Engineering (2011)
The gamma model : a new neural network for temporal processingNeural Networks (1992)
- Bayesian Machine Learning and Information Processing
- Adaptive information processing
No ancillary activities