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Section: Publications & Resources
IEEE Fellow is the highest grade of membership of the IEEE. It honors members with an outstanding record of technical achievements, contributing importantly to the advancement or application of engineering, science and technology, and bringing significant value to society.
Postdoc in Signal Processing and Machine Learning for cyber security of sensor equipped connected vehicle networks
Outstanding candidate will pursue important new research results and document them in the top journals and conferences.
If interested, please send a vita along with your publications and three references. Theoretical research which is mathematically and statistics based is especially appreciated, but applicants should also have the ability to implement/test machine learning and signal processing algorithms.
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Section: Community & Involvement
Section: Publications & Resources
The IEEE Signal Processing Society Boston Chapter has been selected as the recipient of the 2022 Chapter of the Year Award!
Section: For Volunteers
Signal Processing Society Conflict of Interest Disclosure
Section: Publications & Resources
In the cognitive neurosciences and machine learning, we have formal ways of understanding and characterising perception and decision-making; however, the approaches appear very different: current formulations of perceptual synthesis call on theories like predictive coding and Bayesian brain hypothesis.
Section: Events
Date: 21 December 2022
Time: 8:00 AM (PST) | 5:00 PM (CET)
Title: Active Inference
Full webinar details
Section: Events
Date: 14 December 2022
Time: 3:00 PM (Paris Time)
Title: Subgraph-Based Networks for Expressive, Efficient, and Domain-Independent Graph Learning
Registration | Full webinar details
Section: Publications & Resources
While message-passing neural networks (MPNNs) are the most popular architectures for graph learning, their expressive power is inherently limited. In order to gain increased expressive power while retaining efficiency, several recent works apply MPNNs to subgraphs of the original graph.
