Guy 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.
- 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
- 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, May). 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.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta, Ontario, and Quebec as well as foundations, individuals, corporations, and international partner organizations.