I’m a Ph.D. candidate at New York University, advised by Guido Gerig. I’m part of the multi-disciplinary Visualization, Imaging, and Data Analysis (VIDA) Center at NYU which provides a wonderful environment for both research and not losing your mind in gradschool.
My research primarily focuses on computer vision applied to medical and biological images - a field that fortunately offers both fun technicality and real-world relevance.
Research Interests: Computer Vision, Medical Image Analysis, Geometric Deep Learning, and Tensor Algebra
Dey, et al. “Group Equivariant Generative Adversarial Networks", ICLR 2021 (to appear).
Ren*, Dey*, et al. “Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization", IEEE Transactions on Medical Imaging, 2021 (* co-first authors/to appear; Impact Factor: 6.68).
Elaldi*, Dey*, et al. “Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data", IPMI, 2021 (* co-first authors/to appear).
Dey, et al. “Robust Non-negative Tensor Factorization, Diffeomorphic Motion Correction, and Functional Statistics to Understand Fixation in Fluorescence Microscopy", MICCAI, 2019. (Early accept)
Dey, et al. "Tensor Decompositions for Hyperspectral Images of Autofluorescent Retinal Tissue", Medical Image Analysis, 2019. (Impact Factor: 11.15)
Li, Dey, et al. “Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization", IEEE ISBI, 2021 (oral presentation/to appear).
Gisbert, Dey, et al. “Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography", MICCAI OMIA Workshop, 2020.
Dey, et al. "Multi-modal Image Fusion for Multispectral Super-resolution in Microscopy", SPIE Medical Imaging, 2019. (Oral Presentation)
New York University 2017
Master of Science, Electrical Engineering