Shayan Shekarforoush

I am a 3rd year Computer Science PhD Student at University of Toronto supervised by David Fleet and Marcus Brubaker. I am also a graudate student affiliated with Vector Institute.
Currently, I am a research intern at Samsung AI Center Toronto, working with Alex Levinshtein.
During my undergrad, I was an intern at Technical University of Munich under supervision of Nassir Navab, where I worked on Geometric Deep Learning with applications to disease prediction.

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email: < firstname > [at] cs.toronto [dot] edu

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Research

My research interests broadly span computer vision and machine learning. In particular, I am interested in the 3D reconstruction problem in cryoEM. I use recent advances in implicit representation learning in modeling protein structures.

resMFN Residual Multiplicative Filter Networks for Multiscale Reconstruction
Shayan Shekarforoush, David Lindell, David Fleet, Marcus Brubaker
NeurIPS 2022
arXiv / project page / code

A new coordinate network and training scheme for optimizing multi-scale representation in a coarse-to-fine fashion.

physics_cryoem Physics aware inference for the cryo-EM inverse problem
Geoffrey Woollard, Shayan Shekarforoush, Frank Wood, Marcus Brubaker, Khanh Dao Duc
NeurIPS Workshop 2022, Machine Learning in Structural Biology
paper

Stochastic Variational Inference of pose, defocus and conformational heterogeneity of atomic models while using anisotropic network to model variations in conformations.

gene_reg scGCN: A Geometric Deep Learning Framework on Single-cell Gene Networks
Elyas Heidari, Shayan Shekarforoush, Laleh Haghverdi
EuroBioc 2020
code

A software package in R and python to apply geometric deep learning models for gene regulatory networks.

miccai2019 Graph Convolution Based Attention Model for Personalized Disease Prediction
Anees Kazi, Shayan Shekarforoush, S.Arvind Krishna, Hendrik Burwinkel, Gerome Vivar, Benedict Wiestler, Karsten Kortum, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab
MICCAI 2019

Combining LSTM-based attention mechanism and GCNs.

incpetion InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Anees Kazi*, Shayan Shekarforoush*, S.Arvind Krishna, Hendrik Burwinkel, Gerome Vivar, Karsten Kortuem, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab
IPMI 2019
arXiv / code

We define geometric Inception modules capable of capturing intra and inter-graph structural heterogeneity thanks to multiple kernels of different sizes.

isbi Self-Attention Equipped Graph Convolutions for Disease Prediction
Anees Kazi, S.Arvind Krishna, Shayan Shekarforoush, Karsten Kortuem, Shadi Albarqouni, Nassir Navab
ISBI 2019
arXiv

Introducing self-attention layer to GCNs which learns relations between indiviudal demographic data and diseases.

Education

University of Toronto
Department of Computer Science
PhD Student
September 2020 - Present
Supervisors: David Fleet and Marcus Brubaker

Sharif University of Technology
Department of Computer Engineering
Bachelor Student
September 2015 - September 2020


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