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Focus Areas

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My current research focus spans the following three areas:

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​Trustworthy & Tiny Artificial Intelligence for 6G

• Efficient and Accelerated AI​

• Foundation Models for 6G

• Generalizable and Data & Label Efficient AI 

• Safe & Generalizable Reinforcement Learning for Dynamic Environments
   

Joint Sensing & Communications (JSAC)

• Multimodal and Distributed Low-Cost JSAC

• AI for Data & Label-Efficient JSAC 

• WiFi Sensing for Human Vital Signs

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Applications: Immersive Communications & BCIs

• Efficient Dynamic Neural Radiance Fields (NeRF)

• Compression for Stereoscopic 8K Virtual Reality Streaming

• Transfer learning to improve Pediatric Brain-Computer Interfaces for Children

• Generative AI and Foundation Models for BCI 

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Projects

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1. Trustworthy AI for Sensing in 6G Applications (Hiring)
    

2. Autonomous Low-Power SDR Sensing and Communications (Hiring)
    

3. AI-Powered Beam Management for 6G Wireless Communications (Hiring) 
    

4. Extended Reality (XR) Applications and QoE over 5G Networks (Hiring)
    

5. Brain Sensing and Communications: Transfer Learning for Pediatric BCI, Tiny BCI.
   A collaboration with the BCI4Kids Team at the Cumming School of Medicine to build performant and efficient pediatric BCI.
    

6. AI-Powered Traffic Flow Characterization, Monitoring, and Prediction 
    

7. Robust Intelligence for Beyond-5G Networks and Applications
   Tiny and efficient AI/machine learning for wireless communications. Safe reinforcement learning, transfer learning in RL for wireless communications. Research at the intersection of wireless networks and communication of extended reality and digital twins across networks.
    

8. AI-Augmented Intelligent RFICs for Transmitter Predistortion for 5G and 6G Wireless and Space Communication Applications
    

9. Cognitive Machine Type Communications for Massive IoT Applications (complete)
   Adaptive configured grant scheduling for factory automation, joint communication and path planning of UAVs.
    

10. 5G-Assisted Autonomous Vehicles (complete)
    Anticipatory scheduling for autonomous vehicles, microscopic vehicle mobility and maneuver prediction.
     

11. Machine Intelligence and Big Data Analytics for 5G Networks and Beyond (complete)                   
    Network traffic prediction using real network data, hybrid deep learning architectures for spatiotemporal mobility modeling, anticipatory network slicing using reinforcement learning, predictive mobility-aware hand-overs.