Shayan Shekarforoush

I am a CS PhD Student at University of Toronto, and a graduate researcher at Vector Institute. I am fortunate to be supervised by David Fleet and Marcus Brubaker, and closely collaborate with David Lindell.
I interned at Samsung AI Center during summer 2022, working with Alex Levinshtein on dual-camera image enhancement.
I also did two summer internships at Technical University of Munich (2018, 2019) with Nassir Navab, working on Geometric Deep Learning and GNNs. I received my B.Sc. in Computer Engineering from Sharif University of Technology, Iran.

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

Research

My research interests broadly span CV and ML. In particular, I am interested in the protein structure determination using experimental scientific imaging. I've worked on neural implicit representations as well as pose estimation methods for cryo-EM 3D reconstruction.

	CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference Shayan Shekarforoush, David Lindell, Marcus Brubaker, David Fleet NeurIPS 2024 (+ Oral in ML for Structural Biology workshop) arXiv / project page / code We introduce cryoSPIN, a new approach to ab-initio cryo-EM 3D reconstruction using semi-amortization to accelerate convergence and multi-head encoder to handle pose uncertainty.
	Dual-Camera Joint Deblurring-Denoising Shayan Shekarforoush, Aman Walia, Marcus Brubaker, Kosta Derpanis, Alex Levinshtein arXiv / project page A new method for joint image deblurring-denoising using a burst of short exposure images synchronized with a long exposure image, captured using a dual-camera rig. Deblurring is guided with flow/motion information estimated from the burst.
	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, ML 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.
	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 (Oral Presentation) 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.

Teaching Assistant

- Intro to Machine Learning (Head TA) - Winter 2024
- Introduction to Image Understanding - Fall and Winter 2023
- Computational Imaging - Fall 2022, 2024

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.