Portrait of Mark Coates

Mark Coates

Associate Academic Member
Associate Professor, McGill University, Department of Electrical and Computer Engineering
Research Topics
Dynamical Systems
Graph Neural Networks
Learning on Graphs
Recommender Systems
Representation Learning
Google Scholar

Biography

Mark Coates is a professor in the Department of Electrical and Computer Engineering at McGill University, which he joined in 2002. He received his Bachelor of Engineering degree in computer systems engineering from the University of Adelaide, Australia, in 1995 and his PhD degree in information engineering from the University of Cambridge, U.K., in 1999. Coates was formerly a research associate and lecturer at Rice University, Texas (1999–2001) and a senior scientist at Winton Capital Management, Oxford, U.K. (2012–2013).

He has assumed multiple editorial roles, including senior area editor of IEEE Signal Processing Letters, associate editor of IEEE Transactions on Signal Processing, and associate editor of IEEE Transactions on Signal and Information Processing over Networks. His research interests include machine learning and statistical signal processing, Bayesian and Monte Carlo inference, and learning on graphs and networks. His most influential and widely cited contributions have been on the topics of network tomography and distributed particle filtering.

Current Students

Joseph Cotnareanu
PhD - McGill University
Theodore Glavas
Master's Research - McGill University
Google Scholar
Liheng Ma
PhD - McGill University
liheng.ma@mail.mcgill.ca
Website
Github
Google Scholar
Julien Nicolas
PhD - McGill University
regol Regol
PhD
Website
Github
Google Scholar
Antonios Valkanas
PhD - McGill University
Website
Google Scholar
Yitian Zhang
PhD
Github

Publications

Greedy Gossip With Eavesdropping
Deniz Ustebay
Boris Oreshkin
Mark Coates
Michael Rabbat
This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average c… (see more)onsensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper, we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.
2008-05-07
ArXiv (preprint)
doi.org
arxiv.org