Guy Wolf

Email: guy.wolf [at] umontreal.ca

Professeur adjoint / Assistant Professor
Département de mathématiques et de statistique / Department of Mathematics and Statistics
Université de Montréal

Membre associé / Associate Member
Mila - Institut québécois d’intelligence artificielle / Québec AI Institute

IVADO Professor
L'institut de valorisation des données / The institute for data valorization

Intérêts de recherche

Mes sujets de recherche se situent à l'intersection de l'apprentissage automatique, des sciences des données et des mathématiques appliquées. En particulier, je suis intéressé par les méthodes d'exploration des données qui utilisent l'apprentissage des variétés et l'apprentissage profond géométrique. Je m'intéresse aussi aux applications d'analyse exploratoire des données biomédicales, surtout ceux qui portent sur les données de cellules uniques (p.ex. scRNA-seq et CyTOF).

Major research interests

  1. Exploratory data analysis with manifold learning and deep learning
  2. Applied harmonic analysis, spectral graph theory, and diffusion geometry
  3. Graph signal processing and geometric deep learning
  4. Data-driven characterization of nonlinear structrures, patterns, and dynamics
  5. Biomedical big data applications (e.g., genomics and neuroscience)

Équipe de recherche: http://diffusion.space

PhD and MSc students who are interested in doing research at the intersection applied math, machine learning, and data science, are welcome to contact me by email.


Enseignement / Teaching

Trimestre en cours / Current trimester:

Trimestres à venir / Next trimesters:

For courses taught at Yale between 2015-2018 please use this link.


List of Publications

Preprints:

Journals:

  1. M. Amodio, D. van Dijk, K. Srinivasan, W.S. Chen, H. Mohsen, K.R. Moon, A. Campbell, Y. Zhao, X. Wang, M. Venkataswamy, A. Desai, V. Ravi, P. Kumar, R. Montgomery, G. Wolf, and S. Krishnaswamy. Exploring Single-Cell Data with Deep Multitasking Neural Networks. Nature Methods, 2019. DOI: 10.1038/s41592-019-0576-7
  2. D. van Dijk, R. Sharma, J. Nainys, K. Yim, P. Kathail, A.J. Carr, C. Burdziak, K.R. Moon, C.L. Chaffer, D. Pattabiraman, B. Bierie, L. Mazutis, G. Wolf, S. Krishnaswamy, and D. Pe’er. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. Cell, 174(3):716-729.e27, 2018.
  3. M. Salhov, A. Bermanis, G.Wolf, and A. Averbuch. Diffusion Representations. Applied and Computational Harmonic Analysis, 45(2):324-340, 2018.
  4. A. Bermanis, G. Wolf, and A. Averbuch. Diffusion-based kernel methods on Euclidean metric measure spaces. Applied and Computational Harmonic Analysis, 41(1):190-213, 2016.
  5. M. Salhov, A. Bermanis, G.Wolf, and A. Averbuch. Learning from patches by efficient spectral decomposition of a structured kernel. Machine Learning, 103(1):81-102, 2016.
  6. A. Bermanis, M. Salhov, G. Wolf, and A. Averbuch. Measure-based diffusion grid construction and high-dimensional data discretization. Applied and Computational Harmonic Analysis, 40(2):207-228, 2016.
  7. G. Wolf, S. Mallat, and S. Shamma. Rigid motion model for audio source separation. IEEE Transactions on Signal Processing, 64(7):1822-1831, 2016.
  8. M. Salhov, A. Bermanis, G. Wolf, and A. Averbuch. Approximately-isometric diffusion maps. Applied and Computational Harmonic Analysis, 38(3):399-419, 2015.
  9. A. Bermanis, G. Wolf, and A. Averbuch. Cover-based bounds on the numerical rank of Gaussian kernels. Applied and Computational Harmonic Analysis, 36(2):302-315, 2014.
  10. G. Wolf and A. Averbuch. Linear-projection diffusion on smooth Euclidean submanifolds. Applied and Computational Harmonic Analysis, 34(1):1-14, 2013.
  11. G. Wolf, A. Rotbart, G. David, and A. Averbuch. Coarse-grained localized diffusion. Applied and Computational Harmonic Analysis, 33(3):388-400, 2012.
  12. Y. Shmueli, G. Wolf, and A. Averbuch. Updating kernel methods in spectral decomposition by affinity perturbations. Linear Algebra and its Applications, 437(6):1356-1365, 2012.
  13. M. Salhov, G. Wolf, and A. Averbuch. Patch-to-tensor embedding. Applied and Computational Harmonic Analysis, 33(2):182-203, 2012.

Conference Proceedings:

  1. A.F. Duque G. Wolf K.R. Moon, Visualizing High Dimensional Dynamical Processes, In IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Pittsburgh, PA, to appear 2019.
  2. F. Gao and M. Hirn and M. Perlmutter and G. Wolf. Geometric wavelet scattering on graphs and manifolds. In SPIE Optical Engineering + Applications, San Diego, CA; Wavelets and Sparsity XVIII, 11138:228-235, 2019. [INVITED]
  3. S. Gigante, D. van Dijk, K.R. Moon, A. Strzalkowski, G. Wolf, and S. Krishnaswamy. Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks. In The 13th international conference on Sampling Theory and Applications (SampTA 2019), Bordeaux, France, 2019.
  4. S. Gigante, J.S. Stanley III, N. Vu, D. van Dijk, K.R. Moon, G. Wolf, and S. Krishnaswamy. Compressed Diffusion. In The 13th international conference on Sampling Theory and Applications (SampTA 2019), Bordeaux, France, 2019.
  5. F. Gao, G. Wolf, and M. Hirn, Geometric Scattering for Graph Data Analysis. In Proceedings of the 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA; PMLR, 97:2122-2131, 2019.
  6. D.B. Burkhardt, J.S. Stanley III, G. Wolf, and S. Krishnaswamy. Vertex Frequency Clustering. In Proceedings of the 2019 IEEE Data Science Workshop (DSW 2019), Minneapolis, MN, USA, pp. 145-149, 2019.
  7. O. Lindenbaum, J.S. Stanley III, G. Wolf, and S. Krishnaswamy. Geometry-Based Data Generation. Advances in Neural Information Processing Systems 31 (NIPS 2018), Montréal, QC, Canada, pp. 1405-1416, 2018.
  8. G. Wolf, S. Mallat, and S. Shamma. Audio source separation with time-frequency velocities. In Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2014), Reims, France, pp. 1-6, 2014.
  9. A. Bermanis, G. Wolf, and A. Averbuch. Measure-based diffusion kernel methods. In Proceeding of the 10th international conference on Sampling Theory and Applications (SampTA 2013), Bremen, Germany, pp. 489-492, 2013.
  10. M. Salhov, G. Wolf, A. Bermanis, and A. Averbuch. Constructive sampling for patch-based embedding. In Proceeding of the 10th international conference on Sampling Theory and Applications (SampTA 2013), Bremen, Germany, pp. 424-427, 2013.
  11. M. Salhov, G. Wolf, A. Bermanis, A. Averbuch, and P. Neittaanmäki. Dictionary construction for patch-to-tensor embedding. In J. Hollmén, F. Klawonn, and A. Tucker, editors, Advances in Intelligent Data Analysis XI (IDA 2012), Helsinki, Finland, volume 7619 of Lecture Notes in Computer Science, pp. 346-356, 2012.
  12. M. Salhov, G. Wolf, A. Averbuch, and P. Neittaanmäki. Patch-based data analysis using linear-projection diffusion. In J. Hollmén, F. Klawonn, and A. Tucker, editors, Advances in Intelligent Data Analysis XI (IDA 2012), Helsinki, Finland, volume 7619 of Lecture Notes in Computer Science, pp. 334-345, 2012.
  13. G. Wolf, Y. Shmuelli, S. Harussi, and A. Averbuch. Polar clustering. In Proceedings of ECCOMAS Thematic Conference on Computational Analysis and Optimization (CAO2011), Jyväskylä, Finland, pp. 174-177, 2011.

Workshops & Symposia:

  1. S. Horoi, G. Lajoie, and G. Wolf. Internal representation dynamics and geometry in recurrent neural networks. In Montreal AI Symposium (MAIS), Montréal, QC, 2019.
  2. J.S. Stanley III, S. Gigante, G. Wolf, and S. Krishnaswamy. Manifold Alignment by Feature Correspondence. In Signal Processing with Adaptive Sparse Structured Representations (SPARS), Toulouse, France, 2019.
  3. S. Gigante, J.S. Stanley III, N. Vu, D. van Dijk, K.R. Moon, G. Wolf, and S. Krishnaswamy. Compressed Diffusion. In Signal Processing with Adaptive Sparse Structured Representations (SPARS), Toulouse, France, 2019.
  4. F. Gao, G. Wolf, and M. Hirn. Geometric Scattering for Graph Data Analysis. In ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds, New Orleans, LA, 2019.
  5. A. Tong, D. van Dijk, J.S. Stanley III, M. Amodio, G. Wolf, and S. Krishnaswamy. Graph Spectral Regularization for Neural Network Interpretability, In ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds, New Orleans, LA, 2019.
  6. D.B. Burkhardt, J.S. Stanley III, A.L. Pertigoto, S.A. Gigante, K.C. Herold, G. Wolf, A.J. Giraldez, D. van Dijk, and S. Krishnaswamy. Enhancing experimental signals in single-cell RNA-sequencing data using graph signal processing. In ICLR 2019 Learning from Limited Labeled Data (LLD) Workshop, New Orleans, LA, 2019.
  7. F. Gao, G. Wolf, and M. Hirn. Geometric Scattering for Graph Data Analysis. In SDM 2019 Workshop on Deep Learning on Graphs, Calgary, AB, 2019.
  8. M. Perlmutter, G. Wolf, M. Hirn. Geometric Scattering on Manifolds. In NeurIPS 2018 Workshop on Integration of Deep Learning Theories, Montréal, QC, 2018.
  9. M. Aksen, S.I. Kronemer, J.S. Prince, Z. Ding, A. Agarwal, G. Wolf, B. Pearlmutter, R.R. Coifman, M. Pitts, H. Blumenfeld. Pupil dynamics as a covert measure of conscious perception in a visual no report paradigm. Program No. 789.12, 2018 Neuroscience Meeting, Society for Neuroscience, San Diego, CA, 2018.
  10. M. Amodio, K. Srinivasan, D. van Dijk, H. Mohsen, K. Yim, R. Muhle, K.R. Moon, R.R.Montgomery, J. Noonan, G. Wolf, S. Krishnaswamy. SAUCIE: Sparse autoencoderfor unsupervised clustering, imputation, and embedding. In Proceedings of the American Association for Cancer Research Annual Meeting 2018, Chicago, IL; Cancer Research, 78(13 Supplement):5306, 2018.
  11. David van Dijk, Scott Gigante, Kevin R. Moon, Alexander Strzalkowski, Katie Ferguson, Jessica Cardin, Guy Wolf and Smita Krishnaswamy, Modeling Dynamics with Deep Transition-Learning Networks, In Joint ICML and IJCAI 2018 Workshop on Computational Biology (WCB 2018), Stockholm, Sweden, 2018.
  12. H. Mohsen, K. Srinivasan, K.R. Moon, G. Wolf, D. van Dijk, S. Krishnaswamy, Deep Neural Networks for Imputation, Clustering, and Embedding of Single-Cell Data. In ISMB 2017: 25th conference on Intelligent Systems for Molecular Biology, Prague, Czech Republic, 2017.
  13. K.R. Moon, D. van Dijk, Z. Wang, T. Welp, G. Wolf, R.R. Coifman, N. Ivanova, S. Krishnaswamy, PHATE: Potential Heat-diffusion Affinity-based Trajectory Embedding for Visualization of Progression Structure. In 11th Annual Machine Learning Symposium, New York, NY, USA, 2017.
  14. T. Welp, G. Wolf, M. Hirn, S. Krishnaswamy. A Diffusion-based Condensation Process for Multiscale Analysis of Single Cell Data. In ICML 2016 Workshop on Computational Biology (WCB), New York, NY, USA, 2016.

Book Chapters:

  1. G. Wolf, A. Averbuch, and P. Neittaanmäki. Parameter Rating by Diffusion Gradient. In W. Fitzgibbon, Y.A. Kuznetsov, P. Neittaanmäki, O. Pironneau, editors, Modeling, Simulation and Optimization for Science and Technology, volume 34 of Computational Methods in Applied Sciences, pages 225-248. Springer Netherlands, 2014.
  2. G. Wolf, Y. Shmuelli, S. Harussi, and A. Averbuch. Polar classification of nominal data. In S. Repin, T. Tiihonen, and T. Tuovinen, editors, Numerical methods for differential equations, optimization, and technological problems, volume 27 of Computational Methods in Applied Sciences, pages 253-271, Springer Netherlands, 2013.

Reviews:

  1. K.R. Moon, J.S. Stanley III, D. Burkhardt, D. van Dijk, G. Wolf, and S. Krishnaswamy. Manifold learning-based methods for analyzing single-cell RNA-sequencing data. Current Opinion in Systems Biology, 7:36-46, 2018.

Office address:
Pavillon André-Aisenstadt (AA-6165)
2920 chemin de la Tour
Montréal (Québec) H3T 1J4
Canada