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, Neel, et al. "Group Equivariant Generative Adversarial Networks", arXiv pre-print cs.CV 2005.01683, 2020
Dey, Neel, et al. "Tensor Decompositions for Hyperspectral Images of Autofluorescent Retinal Tissue", Medical Image Analysis, 2019. (Impact Factor: 8.88)
Dey, Neel, et al. "Robust Non-negative Tensor Factorization, Diffeomorphic Motion Correction, and Functional Statistics to Understand Fixation in Fluorescence Microscopy", MICCAI, 2019. (Early Accept)
Dey, Neel, et al. "Multi-modal Image Fusion for Multispectral Super-resolution in Microscopy", SPIE Medical Imaging, 2019. (Oral Presentation)
Selected Conference Abstracts:
Gisbert, Dey, et al. "Improved Denoising of Optical Coherence Tomography via Repeated Acquisitions and Unsupervised Deep Learning", ARVO ISIE, 2020 (to appear).
Dey, Neel, et al. "Consistent Automatic Spectral Signature Recovery of Human retinal pigment epithelium (RPE) Lipofuscin Components and Drusen in Donors with Age-related Macular Degeneration (AMD) using Multi-Excitation Hyperspectral Autofluorescence (AF) Imaging." Investigative Ophthalmology & Visual Science 58.8 (2017): 399-399.
Ach, Thomas, et al. "High-resolution and multispectral imaging of autofluorescent retinal pigment epithelium (RPE) granules." Investigative Ophthalmology & Visual Science 58.8 (2017): 3382-3382
New York University 2017
Master of Science, Electrical Engineering