Projects
The research in our lab can be broadly categorized into five groups:
- Machine learning for graph-structured data
- Spatio-temporal forecasting
- Multi-label classification
- Bayesian graph neural network
- Particle flow
For a comprehensive list of current and previous projects, please choose the appropriate tab above.
- Multi-sensor data association in $\delta$-generalized labeled multi-Bernoulli filter
- Spatio-Functional segmentation for spectral images
- Routing in Opportunistic Networks
- Information Diffusion in Networks
- Random finite set (RFS) based filters for superpositional observation model
- Bearing-only Single Target Tracking on Spherical Surfaces
- Radio frequency (RF) tomography for multi-target tracking
- Convoy Detection in Networks of Short-Range Sensors
- Neural-network Modelling of Bayesian Learning and Inference
- Clustered Sparse Associative Memories
- Anomaly Detection using Proximity Graph and PageRank
- Distributed Tracking Using Selective Gossip
- Multiscale Gossip for Fast Averaging
- Weblog Analysis for Predicting Correlations in Stock Price Evolutions
- Green Analysis for Content-Centric Networking (CCN)
- Content Centric Networking
- Flow-based Bandwidth Estimation in Non-visible Links
- Cramér-Rao bound for Unmanned Aerial Vehicle (UAV)
- Clustering For Dynamic Networks
- Graph Spectral Compressed Sensing
- Daum-Huang exact flow particle filter
- Graph Clustering
- ChoPS