Portrait of Eric Kolaczyk

Eric Kolaczyk

Associate Academic Member
Professor, McGill University, Department of Mathematics and Statistics
Research Topics
Computational Biology
Computational Neuroscience
Learning on Graphs
Molecular Modeling
Probabilistic Models
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Biography

Eric Kolaczyk is a professor in McGill University’s Department of Mathematics and Statistics, and the inaugural director of the McGill Computational and Data Systems Initiative (CDSI). His research is focused on how statistical and machine learning theory and methods can support human endeavours enabled by computing and engineered systems, frequently from a network-based perspective of systems science. He collaborates regularly on problems in computational biology, computational neuroscience and, most recently, AI-assisted chemistry and materials science. He has published over one hundred articles, including several books on the topic of network analysis.

As an associate editor, Kolaczyk has served on the boards of JASA and JRSS-B in statistics, IEEE IP and TNSE in engineering, and SIMODS in mathematics. He formerly served as co-chair of the U.S. National Academies of Sciences, Medicine, and Engineering Roundtable on Data Science Education. He is an elected fellow of the AAAS, ASA and IMS, an elected senior member of IEEE, and an elected member of the ISI.

Current Students

Michael Montemurri
Master's Research - McGill University
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Publications

Data Privacy for Record Linkage and Beyond
Shurong Lin
Eric Kolaczyk
In a data-driven world, two prominent research problems are record linkage and data privacy, among others. Record linkage is essential for i… (see more)mproving decision-making by integrating information of the same entities from different sources. On the other hand, data privacy research seeks to balance the need to extract accurate insights from data with the imperative to protect the privacy of the entities involved. Inevitably, data privacy issues arise in the context of record linkage. This article identifies two complementary aspects at the intersection of these two fields: (1) how to ensure privacy during record linkage and (2) how to mitigate privacy risks when releasing the analysis results after record linkage. We specifically discuss privacy-preserving record linkage, differentially private regression, and related topics.
2024-09-01
Social Science Research Network (published)
doi.org
Autoregressive Networks with Dependent Edges
Jinyuan Chang
Qin Fang
Eric Kolaczyk
Peter W. MacDonald
Qiwei Yao
2024-04-24
ArXiv (preprint)
arxiv.org
arxiv.org
Stochastic gradient descent-based inference for dynamic network models with attractors
Hancong Pan
Xiaojing Zhu
Cantay Caliskan
Dino P. Christenson
Konstantinos Spiliopoulos
Dylan Walker
Eric Kolaczyk
2024-03-11
ArXiv (preprint)
doi.org
arxiv.org
Stochastic gradient descent-based inference for dynamic network models with attractors
Hancong Pan
Xiaojing Zhu
Cantay Caliskan
Dino P. Christenson
Konstantinos Spiliopoulos
Dylan Walker
Eric Kolaczyk
In Coevolving Latent Space Networks with Attractors (CLSNA) models, nodes in a latent space represent social actors, and edges indicate thei… (see more)r dynamic interactions. Attractors are added at the latent level to capture the notion of attractive and repulsive forces between nodes, borrowing from dynamical systems theory. However, CLSNA reliance on MCMC estimation makes scaling difficult, and the requirement for nodes to be present throughout the study period limit practical applications. We address these issues by (i) introducing a Stochastic gradient descent (SGD) parameter estimation method, (ii) developing a novel approach for uncertainty quantification using SGD, and (iii) extending the model to allow nodes to join and leave over time. Simulation results show that our extensions result in little loss of accuracy compared to MCMC, but can scale to much larger networks. We apply our approach to the longitudinal social networks of members of US Congress on the social media platform X. Accounting for node dynamics overcomes selection bias in the network and uncovers uniquely and increasingly repulsive forces within the Republican Party.
2024-03-11
ArXiv (preprint)
doi.org
arxiv.org
Assessing the impact of aircraft arrival on ambient ultrafine particle number concentrations in near-airport communities in Boston, Massachusetts.
Chloe S. Chung
K. Lane
Flannery Black-Ingersoll
Eric Kolaczyk
Claire Schollaert
Sijia Li
Matthew C. Simon
J. Levy
2023-03-01
Environmental Research (published)
doi.org
Assessing the Impact of Aircraft Arrival on Ambient Ultrafine Particle Number Concentrations in Near-Airport Communities in Boston, Massachusetts
Chloe S. Chung
Chloe S. Kim
Kevin James Lane
K. Lane
Flannery Black-Ingersoll
Eric Kolaczyk
Claire Schollaert
Sijia Li
Matthew C. Simon
Jonathan I. Levy
J. Levy
2023-03-01
Environmental Research (published)
doi.org