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Guy Wolf

Guy Wolf

Appointment

  • Canada CIFAR AI Chair
  • Pan-Canadian AI Strategy

Connect

Université de Montréal

Google Scholar

About

Guy Wolf is a Canada CIFAR AI Chair at Mila and an associate professor in the department of mathematics and statistics at Université de Montréal.

Wolf’s research spans over a wide range of theoretical, computational, and practical data analysis aspects at the intersection of machine learning and data science. He is interested in data exploration involving dimensionality reduction and representation learning, where big high-dimensional data require processing and organization to be made approachable and interpretable by domain experts, who are not necessarily computation-oriented. The techniques he employs are versatile and multidisciplinary in order to combine their advantages and strengths, and include, among others, manifold learning, geometric deep learning, graph signal processing, and non-Euclidean harmonic analysis. 

His recent work leverages such interdisciplinary tools to find emergent patterns, dynamics, and structure in big data, with applications in multiple fields, such as biomedical data analysis, neuroscience, and bioinformatics.

Awards

  • Top 400 reviewers, NeurIPS, 2019
  • Deutsch Prize for excellence in Ph.D. studies, Tel Aviv University, 2012
  • Eshkol Fellowship, Israeli Ministry of Science & Technology, 2011
  • Excellence scholarship by the Faculty of Exact Sciences, Tel Aviv University, 2010
  • Prize of Excellence for MSc students, Tel Aviv University, 2007

Relevant Publications

  • Stanley III, J. S., Gigante, S., Wolf, G., & Krishnaswamy, S. (2020). Harmonic Alignment. In Proceedings of the 2020 SIAM International Conference on Data Mining (pp. 316-324). Society for Industrial and Applied Mathematics.

  • Moon, K. R., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D. B., Chen, W. S., Yim, K., van den Elzen, A., Hirn, M.J., Coifman, R.R., Ivanova, N.B., Wolf, G., & Krishnaswamy, S. (2019). Visualizing structure and transitions in high-dimensional biological data. Nature biotechnology, 37(12), 1482-1492.

  • Amodio, M., Van Dijk, D., Srinivasan, K., Chen, W. S., Mohsen, H., Moon, K. R., Campbell, A., Zhao, Y., Wang, X., Venkataswamy, M., Desai, A., Ravi, V., Kumar, P., Montgomery, R., Wolf, G., & Krishnaswamy, S. (2019). Exploring single-cell data with deep multitasking neural networks. Nature methods, 1-7.

  • Gao, F., Wolf, G., & Hirn, M. (2019). Geometric scattering for graph data analysis. In International Conference on Machine Learning (pp. 2122-2131). PMLR.

  • Lindenbaum, O., Stanley, J., Wolf, G., & Krishnaswamy, S. (2018). Geometry based data generation. Advances in Neural Information Processing Systems, 31, 1400-1411.

Institution

  • Mila
  • Université de Montréal

Department

Mathematics and Statistics

Education

  • PhD (Computer Science), Tel Aviv University
  • MSc (Computer Science), Tel Aviv University
  • BA (Computer Science), the Open University of Israel

Country

  • Canada

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