Guy Wolf

Email: guy.wolf@yale.edu

Gibbs Assistant Professor
Applied Math Program
Department of Mathematics
Yale University

Major interests

  1. High-dimensional data analysis & Big Data
  2. Dimensionality reduction
  3. Manifold learning & differential geometry
  4. Diffusion geometry & spectral graph theory
  5. Biomedical data analysis applications
  6. Multiview approaches to data analysis
  7. Nonlinear locally low dimensional data geometries

Teaching

Current Semester:

Next Semesters:

Previous Semesters:


List of Publications

Journals:

  1. K.R. Moon, D. van Dijk, Z. Wang, W. Chen, M.J. Hirn, R.R. Coifman, N.B. Ivanova, G. Wolf, S. Krishnaswamy. PHATE: A Dimensionality Reduction Method for Visualizing Trajectory Structures in High-Dimensional Biological Data. Preprint (bioRxiv.org), 2017. DOI: 10.1101/120378
  2. D. van Dijk, J. Nainys, R. Sharma, P. Kathail, A.J. Carr, K.R. Moon, L. Mazutis, G. Wolf, S. Krishnaswamy, D. Pe'er. MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data. Preprint (bioRxiv.org), 2017. DOI: 10.1101/111591
  3. M. Salhov, A. Bermanis, G.Wolf, and A. Averbuch. Diffusion Representations. Applied and Computational Harmonic Analysis, 2016. DOI: 10.1016/j.acha.2016.10.003
  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.

Conferences:

  1. 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.
  2. 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.
  3. 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.
  4. G. Wolf, S. Mallat, and S. Shamma. Audio source separation with time-frequency velocities. In 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Reims, France, 2014.
  5. A. Bermanis, G. Wolf, and A. Averbuch. Measure-based diffusion kernel methods. In SampTA 2013: 10th international conference on Sampling Theory and Applications, Bremen, Germany, 2013.
  6. M. Salhov, G. Wolf, A. Bermanis, and A. Averbuch. Constructive sampling for patch-based embedding. In SampTA 2013: 10th international conference on Sampling Theory and Applications, Bremen, Germany, 2013.
  7. 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, volume 7619 of Lecture Notes in Computer Science, pages 346-356. Springer Berlin Heidelberg, 2012.
  8. 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, volume 7619 of Lecture Notes in Computer Science, pages 334-345. Springer Berlin Heidelberg, 2012.
  9. G. Wolf, Y. Shmuelli, S. Harussi, and A. Averbuch. Polar clustering. In CAO2011: ECCOMAS Thematic Conference on Computational Analysis and Optimization, 2011.

Book Chapters:

  1. 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.
  2. 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.

Office address:
51 Prospect St. (#103)
New Haven, CT 06511
USA