Dr. Amir Weiss
Welcome to my homepage!
I am a faculty member at the Faculty of Engineering at Bar-Ilan University.
My research focuses on what I like to call “machine-learning-enhanced statistical signal processing”—an emerging interdisciplinary area at the intersection of signal processing, machine learning, estimation theory, and elements of information theory.
In my research group, we combine the development of new mathematical methods with the solution of practical problems across a variety of domains, including:
• Digital communication systems
• Localization and positioning in electromagnetic and acoustic environments
• Algorithms for distributed sensing systems
• Innovative compression methods
Our goal is to develop algorithmic and theoretical tools that push the boundaries of scientific knowledge, while simultaneously enabling practical solutions to contemporary and future technological challenges.
Selected Research Areas
Joint Compression and Estimation
Goal-oriented compression for estimation (e.g., localization) in distributed systems under communication constraints, including theoretical analysis and practical solution schemes.
RF Signal Separation Using Machine Learning Techniques
Deep learning-based algorithms for RF signal separation, with applications in interference mitigation for advanced communication systems
Underwater Acoustic Localization
Underwater acoustic localization algorithms with environmental uncertainty, multipath propagation, and absence of line-of-sight, including performance bounds across a range of scenarios.
Selected Publications
Joint Data Compression and Time-Delay Estimation for Distributed Systems via Extremum Encoding
RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge
Blind Modulo Analog-to-Digital Conversion
A Semi-Blind Method for Localization of Underwater Acoustic Sources
RF Signal Separation Using Artificial Intelligence
RF Signal Separation Using Artificial Intelligence
In December 2024, I spoke at "AI Week", one of Israel’s leading AI events, presenting some of our latest and most exciting results on applying AI to radio signal processing, with a focus on improved signal separation and interference suppression.