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
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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
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Liheng Ma
PhD - McGill University
liheng.ma@mail.mcgill.ca
Website
Github
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Julien Nicolas
PhD - McGill University
regol Regol
PhD
Website
Github
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Antonios Valkanas
PhD - McGill University
Website
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Yitian Zhang
PhD
Github

Publications

Distributed Average Consensus With Dithered Quantization
T. C. Aysal
Mark Coates
Michael Rabbat
In this paper, we develop algorithms for distributed computation of averages of the node data over networks with bandwidth/power constraints… (see more) or large volumes of data. Distributed averaging algorithms fail to achieve consensus when deterministic uniform quantization is adopted. We propose a distributed algorithm in which the nodes utilize probabilistically quantized information, i.e., dithered quantization, to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus at one of the quantization values almost surely. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We derive an upper bound on the mean-square-error performance of the probabilistically quantized distributed averaging (PQDA). Moreover, we show that the convergence of the PQDA is monotonic by studying the evolution of the minimum-length interval containing the node values. We reveal that the length of this interval is a monotonically nonincreasing function with limit zero. We also demonstrate that all the node values, in the worst case, converge to the final two quantization bins at the same rate as standard unquantized consensus. Finally, we report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.
2008-10-01
IEEE Transactions on Signal Processing (published)
doi.org
Distributed Average Consensus With Dithered Quantization
Tuncer Can Aysal
Mark Coates
Michael Rabbat
In this paper, we develop algorithms for distributed computation of averages of the node data over networks with bandwidth/power constraints… (see more) or large volumes of data. Distributed averaging algorithms fail to achieve consensus when deterministic uniform quantization is adopted. We propose a distributed algorithm in which the nodes utilize probabilistically quantized information, i.e., dithered quantization, to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus at one of the quantization values almost surely. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We derive an upper bound on the mean-square-error performance of the probabilistically quantized distributed averaging (PQDA). Moreover, we show that the convergence of the PQDA is monotonic by studying the evolution of the minimum-length interval containing the node values. We reveal that the length of this interval is a monotonically nonincreasing function with limit zero. We also demonstrate that all the node values, in the worst case, converge to the final two quantization bins at the same rate as standard unquantized consensus. Finally, we report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.
2008-10-01
IEEE Transactions on Signal Processing (published)
doi.org
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