Shayan Shekarforoush

I am Computer Science PhD Student at University of Toronto supervised by David Fleet and Marcus Brubaker. I am also a graduate student affiliated with Vector Institute.
I did an internship at Samsung AI Center Toronto, working with Alex Levinshtein on dual-camera image enhancement.
I finished my undergrad at Sharif University of Technology, during which I interned at Technical University of Munich under supervision of Nassir Navab, where I worked on Geometric Deep Learning.

CV / LinkedIn / Google Scholar / Github / Twitter
email: < firstname > [at] cs.toronto [dot] edu

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.

	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 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.
	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.
	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.
	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.
	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

Template adapted from Jon Barron.