Ariyan Zarei

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I am a PhD student in the Department of Computer Science at the University of Arizona. My research interests are Neural Networks, Deep Learning and applications of Computer Vision. I am working with Prof. Kobus Barnard on applications of Computer Vision and Neural Networks in Medical and Plant sciences. More specifically, I am applying Deep Learning and Image Processing methods to detect and localize tumors in large and high resolution slides (WSI) of human breast tissues. Also, I am working on applying Computer Vision and Machine Learning methods to stitch and preprocess high resolution images of crop fields as well as detecting and localizing crop heads and classifying their different characteristics.

I previously received my B.Sc. in Computer Science from Shahid Beheshti University where I acted as the secretory of Scientific Association of Computer Science Students for one year. During my undergraduate program, I worked with Dr. Ali Katanforoush on a few small projects.

Recent News
I am pleased to announce that I will be joining Meta/Facebook for the summer 2022 as a Machine Learning Software Engineer in the Ads Fairness team.
We gave a series of presentations at AG2PI with the title of "Introduction to Scientific Computing". Find out more here.
I am pround to announce that our paper "NowCasting-nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products using Convolutional and Recurrent Neural Networks" has been published in IEEE Transaction on Geoscience and Remote Sensing (TGRS). Find out more here.
We gave a presentation at AG2PI with the tile of "Hands-On Machine Learning with Agricultural Applications". Find out more here.
I am pround to announce that our paper "MegaStitch: Robust Large-Scale Image Stitching" has been published in IEEE Transaction on Geoscience and Remote Sensing (TGRS). Find out more here.
We presented on Cyverse Webinar the MLflow and its use cases. Find out more here.
I presented a brief literature review on image stitching and alignment during IVILAB colloquium. Find out more here.
I presented the Image Orthomosaicing method that we have developed in PhytoOracle, i.e. the MegaStitch, in the AG2PI field day seminar. AG2PI or Agricultural Genome to Phenome Initiatives hosts monthly seminars on the correlations between genetipic and phenotipic information. Find out more here.
During the IVILAB Colloquium I presented a study on the effects of using different methods for estimating and calculating the Jacobian matrix of a nonlinear least squares equation for minimization of projection error in a Bundle Adjustment problem. Find out more here.
I presented the MegaStitch method in the TRIPODS Seminar. I discussed the details of our proposed methods for stitching the images in large scale. I illustrated the efficiency and accuracy of MegaStitch in geo-correcting agricultral images captured by drones and ground-based platforms like the Gantry. Find out more here.
I presented the paper by Triggs, Bill et. al. on Bundle Adjustment in the IVILAB Colloguium. I discussed different formulations of the bundle adjustment problem and different methods for estimating parameters of those bundle adjustment problems. Find out more here.
I presented the geo-correction and image stitching as part of our PhytoOracle pipeline presentation in the Cyverse webinar. We discussed different parts of the pipeline and I specifically focused on explaining the stitching method I develped. Find out more here.
I presented one of my research projects in IVILAB Colloquium. I talked about large scale image stitching in high level and discussed the MGRAPH method in detail. Find out more here.
I gave a talk in IVILAB Colloquium, about Second Order methods in optimization as well as the Conjugate Gradient Method. Find out more here.
I got an internship offer from Roche Tissue Diagnostics for summer 2020. But unfortunately it got cancelled due to the Coronavirus pandemic.
I gave a talk in IVILAB Colloquium about LRP. Find out more here.
I gave a talk in IVILAB Colloquium about SSVMs. Find out more here.