• Machine Learning for Communications, Multi-agent Reinforcement Learning, Meta-learning
• Coded Distributed Computing, Wireless Caching Networks
• Statistical signal processing, Markov Chain Monte Carlo Detection
• Coding Theory, Iterative Decoding
• Underwater Acoustic Communication.
• Machine Learning for Medical Imaging, MRI Image Reconstruction

We are conducting research in the general areas of Massive MIMO, Ultra Reliable Low Latency Communications (URLLC), Filterbank Multicarrier Communications (FBMC), and High Frequency (HF) Communications.

Our research seeks to augment biological neural networks with artificial neural networks and bionic devices to treat neurological disorders and to further our understanding of neural processing. Working at the intersection of artificial intelligence, robotics, and neuroscience, we are developing biologically-inspired artificial intelligence and brain-machine interfaces to restore and/or enhance human function.

The Sensing, People, and Networks (SPAN) Lab conducts research on two topics: wireless networking, and the equity of engineered and automated sensing and decision systems. We have a history of augmenting the reliability, efficiency, and capabilities of networks using the radio as a sensing interface in addition to the communication interface. We also investigate how sensing and other “big data” systems and algorithms that affect the resources people are allocated can have both positive and negative effects on equity in society. Students in the SPAN Lab study wireless networking, statistical signal processing, and power and inequity, and these tools enable us to study the interaction of people, networks, and sensing systems in new ways that will lead to greater privacy, reliability, and equity of engineered systems.

We also actively aim to contribute to fundamental machine learning research on topics such as semi-supervised learning, domain adaptation and interpretable machine learning. A partial list of project descriptions can be found here . Our vision is to overcome the barriers of scarcity of annotated data and the lack of interpretability of AI models to facilitate their ubiquitous adoption in interdisciplinary research and every day life.