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We develop theory and invent technologies for intelligent wireless communications. Recent research interests:

• Machine Learning for Intelligent Communications
• Optimization of (massive) MIMO interference networks;
• Information theory and coding;
• Synchronization algorithms;
• Machine learning aided wireless communication;
• Software defined radio (SDR) to transfer our research to real world systems;
• Characterization of random networks using stochastic geometry;
• Performance analysis of communication systems with feedback.

Peter Mathys and his group work at the intersection of communication and coding theory and practical implementations using software-defined radio (SDR), digital signal processing (DSP), and machine learning (ML). Of particular interest is the combination of SDRs and ML to create intelligent radio networks that are capable of sharing the radio frequency (RF) spectrum, which is a finite natural resource, in novel and more efficient ways. Another more interdisciplinary application of wireless communications, SDR and DSP for medical purposes is the design and implementation of passive implants that are powered by RF energy while at the same time receiving and transmitting bio-sensing data wirelessly.

Francois Meyer and his group work on the development of mathematical algorithms and computational methods for the analysis of observational high-dimensional dataset with applications to biology, neuroscience (fMRI, EEG), geoscience, etc.

Mahesh Varanasi and his group work in the areas of information theory, wireless communications and detection/estimation theory. Their focus at this time is on developing fundamental understanding of, and methods for, efficient and reliable transmission of data over networks. Of particular interest are networks that incorporate protocols to enable advanced features such as multiple-antenna terminals, cooperation and relays, cognition, spectrum sensing, simultaneous multiple groupcasting, security, privacy and feedback. Network topologies include small-to-large-scale single-cell, multi-cell, interference and relay networks, multihop-multiflow networks and cache networks. Information theoretic limits of such networks as well as the development of optimal and near-optimal methods are of interest as is the study of combinatorial and other structure of solutions and algorithms for their efficient computation.