We are engaged in the development and use of data science algorithms and architectures in diverse applications such as connected health, solar energy generation and distribution, online video classification, augmented and virtual reality, and massive MIMO systems. Research includes both deep learning techniques suitable for the applications, as well as new hybrid cloud and edge architectures to ensure scalable and real-time solutions with high efficiency in computation and data communication.
- Wireless AR/VR with Predictive AI and Edge Computing
- Building Scalable Recommendation Systems for Enterprise Knowledge Workers
- Towards Enabling Personalized Video Content and Services
- Efficiently Addressing Annotation Sparsity in Online Content
- Forecasting of Solar PV Generation using Artificial Intelligence
- Personalized Hypertension Care using Wearables and Machine Learning
- Learning-based Task Recommendation System for Treatment of Patients with Parkinson’s Disease in a Physical Therapy Setting
- Using Machine Learning based Adaptive Hybrid Beamforming for Energy Efficient Massive MIMO
- User Controlled Personalized Content