Mila > Team > Siamak Ravanbakhsh

# Siamak Ravanbakhsh

Core Academic Member
siamak@cs.mcgill.ca
https://www.cs.mcgill.ca/~siamak/
514-398-7076
Deep Learning
Learning on Graphs
Generative Models
Probabilistic Models
Core Academic Member
Assistant Professor, McGill University, Canada CIFAR AI Chair

Siamak Ravanbakhsh has been an assistant professor at McGill University’s School of Computer Science since August 2019. Before joining McGill and Mila as an assistant professor, he held a similar position at the University of British Columbia. Prior to that, he was a postdoctoral fellow at the Machine Learning Department and the Robotics Institute at Carnegie Mellon University and received his Ph.D. from the University of Alberta. He is broadly interested in the problems of representation learning and inference in AI. His current research focuses on the role of invariance and symmetry in deep representation learning.

## Publications

### 2021-07

Equivariant Networks for Pixelized Spheres
Mehran Shakerinava and Siamak Ravanbakhsh
ICML 2021
(2021-07-18)
proceedings.mlr.press PDF[Also on arXiv preprint arXiv:2106.06662 (2021-06-12)]

### 2021-05

Recovering the wedge modes lost to 21-cm foregrounds
Samuel Gagnon-Hartman, Yue Cui, Adrian Liu and Siamak Ravanbakhsh
Monthly Notices of the Royal Astronomical Society
(2021-05-25)
academic.oup.com PDF[Also on arXiv: Cosmology and Nongalactic Astrophysics (2021-05-25)]
Deep generative models for galaxy image simulations
François Lanusse, Rachel Mandelbaum, Siamak Ravanbakhsh, Chun-Liang Li, Peter Freeman and Barnabás Póczos
Monthly Notices of the Royal Astronomical Society
(2021-05-25)
academic.oup.com PDF
Deep generative models for galaxy image simulations
François Lanusse, Rachel Mandelbaum, Siamak Ravanbakhsh, Chun-Liang Li, Peter Freeman and Barnabás Póczos
Monthly Notices of the Royal Astronomical Society
(2021-05-21)
PDF

### 2020-07

Universal Equivariant Multilayer Perceptrons
Siamak Ravanbakhsh
ICML 2020
(2020-07-12)
proceedings.mlr.press[Also on arXiv: Learning (2020-02-07)]
Incidence Networks for Geometric Deep Learning
Marjan Albooyeh, Daniele Bertolini and Siamak Ravanbakhsh
ICML 2020
(2020-07-12)
icml.cc PDF

### 2020-06

Equivariant Maps for Hierarchical Structures.
Renhao Wang, Marjan Albooyeh and Siamak Ravanbakhsh
arXiv preprint arXiv:2006.03627
(2020-06-05)
ui.adsabs.harvard.edu PDF

### 2020-01

Designing networks to accurately learn 2D turbulence closures
Keaton Burns, Ronan Legin, Adrian Liu, Laurence Perreault-Levasseur, Yashar Hezaveh, Siamak Ravanbakhsh and Gregory Wagner
APS Division of Fluid Dynamics Meeting Abstracts
(2020-01-01)
ui.adsabs.harvard.edu
Equivariant Networks for Hierarchical Structures
Renhao Wang, Marjan Albooyeh and Siamak Ravanbakhsh
NEURIPS 2020
(2020-01-01)
papers.nips.cc PDF

### 2019-10

LRP2020: Machine Learning Advantages in Canadian Astrophysics
K.A. Venn, S. Fabbro, A Liu, Y. Hezaveh, L. Perreault-Levasseur, G. Eadie, S. Ellison, J. Woo, Jj. Kavelaars, K.M. Yi, R. Hlozek, J. Bovy, H. Teimoorinia, S. Ravanbakhsh and L. Spencer
arXiv preprint arXiv:1910.00774
(2019-10-02)
export.arxiv.org PDF
Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines
Jakub M. Tomczak, Szymon Zaręba, Siamak Ravanbakhsh and Russell Greiner
Neural Processing Letters
(2019-10-01)
link.springer.com PDF

### 2019-09

Equivariant Entity-Relationship Networks
Devon Graham and Siamak Ravanbakhsh
arXiv preprint arXiv:1903.09033
(2019-09-25)
ui.adsabs.harvard.edu PDF

### 2019-07

Improved Knowledge Graph Embedding Using Background Taxonomic Information
Bahare Fatemi, Siamak Ravanbakhsh and David Poole
AAAI 2019
(2019-07-17)
aaai.org PDF

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