Danial Saber

Danial Saber

Graduade Research/Teaching Assistant

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About Me

Hello! I’m Danial Saber. I am passionate about researching on Deep Learning with a particular focus on Graph Neural Networks. My main programming language is Python and I am eager to expand my proficiency in coding with Pytorch and Pytorch Geometric. I am a quick learner and a team worker that gets the job done.

Currently, I am a graduate research/teaching assistant at the Sain Lab under the supervision of Dr. Amirali Abari at Ontario Tech University.

Bio

Age
23
Email
danial.saber@ontariotechu.net
Skype
Danial.saber

Professional Skills

Python3
Master
PyTorch & PyTorch Geometric
Advance
Numpy, Pandas, Matplotlib
Master
Tensorflow, Keras
Advance
MySQL, SQL Server
Master
Power BI
Expert
Java, C++
Advance
Git, Linux
Master

Work Experience

Data Analyst at Koosha Tejarat Nopadid
April 2022 - August 2022
Developed an interactive performance dashboard for Kharazmi University using Python, MySQL and Power BI to provide visual representations of teaching, conferences, publications, books, and patents at three levels for professors, departments, and faculties.
Administrative Member of Scientific Association of Computer Engineering at Kharazmi University
July 2021 - August 2022
Organazied useful events for Computer Engineering students, and mentored first-year students regarding their future plans.

Education

Master of Science in Computer Science at Ontario Tech University
2023 - 2025
Bachelor of Computer Engineering - Software at Kharazmi University
2019 - 2023
GPA: 3.52 / 4.00

Projects

Python / Pytorch / PyG

Fake News Detection on Twitter

Implemented several GNN models such as GCN, GAT, GIN and GraphSAGE to compare the performance of GNN models with MLP for this graph classification task.

Python / Tensorflow / FastAPI

Brain Tumor Detection

Proposed a CNN model with 98% accuracy in Python (Tensorflow) based on the concept of Transfer Learning to classify brain tumor from brain MRI images

Python / Pytorch / PyG

GNNs on Cora

Implemented models such as MLP, GCN, and GraphSAGE to compare the performance of architectures with and without structural information of the graph datasets using Pytorch.

Contact

danial.saber@ontariotechu.net