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
Arcos Bravo David Gamaliel
Master's Research - McGill University
Github
LinkedIn
Google Scholar
Theodore Glavas
Master's Research - McGill University
Google Scholar
Liheng Ma
PhD - McGill University
Website
Github
Google Scholar
Julien Nicolas
PhD - McGill University
regol Regol
PhD
Website
Github
Google Scholar
Chiara Roverato
PhD - McGill University
Co-supervisor :
Pablo Piantanida
Antonios Valkanas
PhD - McGill University
Website
Google Scholar
Yitian Zhang
PhD
Github

Publications

SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting
Yitian Zhang
Liheng Ma
Antonios Valkanas
Boris N. Oreshkin
Mark Coates
Koopman operator theory provides a framework for nonlinear dynamical system analysis and time-series forecasting by mapping dynamics to a sp… (see more)ace of real-valued measurement functions, enabling a linear operator representation. Despite the advantage of linearity, the operator is generally infinite-dimensional. Therefore, the objective is to learn measurement functions that yield a tractable finite-dimensional Koopman operator approximation. In this work, we establish a connection between Koopman operator approximation and linear Recurrent Neural Networks (RNNs), which have recently demonstrated remarkable success in sequence modeling. We show that by considering an extended state consisting of lagged observations, we can establish an equivalence between a structured Koopman operator and linear RNN updates. Building on this connection, we present SKOLR, which integrates a learnable spectral decomposition of the input signal with a multilayer perceptron (MLP) as the measurement functions and implements a structured Koopman operator via a highly parallel linear RNN stack. Numerical experiments on various forecasting benchmarks and dynamical systems show that this streamlined, Koopman-theory-based design delivers exceptional performance.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Bidirectional Learning for Offline Model-based Biological Sequence Design
Can (Sam) Chen
Yingxue Zhang
Xue Liu
Mark Coates
Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores. In this … (see more)paper, we focus on biological sequence design to maximize some sequence score. A recent approach employs bidirectional learning, combining a forward mapping for exploitation and a backward mapping for constraint, and it relies on the neural tangent kernel (NTK) of an infinitely wide network to build a proxy model. Though effective, the NTK cannot learn features because of its parametrization, and its use prevents the incorporation of powerful pre-trained Language Models (LMs) that can capture the rich biophysical information in millions of biological sequences. We adopt an alternative proxy model, adding a linear head to a pre-trained LM, and propose a linearization scheme. This yields a closed-form loss and also takes into account the biophysical information in the pre-trained LM. In addition, the forward mapping and the backward mapping play different roles and thus deserve different weights during sequence optimization. To achieve this, we train an auxiliary model and leverage its weak supervision signal via a bi-level optimization framework to effectively learn how to balance the two mappings. Further, by extending the framework, we develop the first learning rate adaptation module \textit{Adaptive}-
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press