Bellairs workshop 2019
2019 Bellairs Workshop on Machine Learning and Statistical Signal Processing for Data on Graphs
This workshop brought together leading international researchers who are developing and using machine learning and statistical signal processing methods to analyze data recorded on graphs and networks. The workshop consisted of research presentations and several tutorials. We discussed open problems and forged research collaborations, identifying promising future directions for graph data analysis. The event was held at McGill's beautiful Bellairs Research Institute in Holetown, Saint James Parish, Barbados.
Tutorials:
- Georgios Giannakis (U. Minnesota, USA): "Learning Nonlinear and Dynamic Connectivity and Processes over Graphs"
- Gonzalo Mateos (U. Rochester, USA): "Connecting the Dots: Identifying Network Structure of Complex Data via Graph Signal Processing"
Full presentations
- Waheed Bajwa (Rutgers University, USA): "Principal Component Analysis from Fast Streaming Data"
- Mark Coates (McGill, Canada): “Bayesian Graph Convolutional Neural Networks"
- Petar Djuric (Stonybrook, USA): “Deep Gaussian Processes: Theory and Applications"
- Georgios Giannakis (U. Minnesota, USA): "Online Scalable Learning Adaptive to Unknown Dynamics and Graphs”
- Gonzalo Mateos (Rochester, USA): "Digraph Fourier transform via spectral dispersion minimization”
- Arash Mohammadi (Concordia, Canada): "Distributed-Graph-Based Statistical Approach for Intrusion Detection in Cyber-Physical Systems"
- Farnoosh Naderkani (Concordia, Canada)
Short presentations (accepted abstracts):
- Muhammad Asad Lodhi (Rutgers, USA): “Union of subspaces signal detection"
- Rishabh Dixit (Rutgers, USA): “Decentralized dictionary learning in networks"
- Florence Robert-Regol (McGill, Canada): “Active learning for graph signals - Sampling the initial set"
- Arpita Gang (Rutgers, USA): “Resilient distributed learning algorithms"
- Soumyasundar Pal (McGill, Canada): “Scalable MCMC in degree corrected stochastic block model"
- Juliette Valenchon (McGill, Canada): "Disease Outcome Prediction Using Graph Auto-encoders”