I'm

Omar Sulaivany

Data Specialist

PhD candidate in Informatics Engineering

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Learn About Me

10 Years Experience

As a passionate professional specializing in data automation and machine learning, I excel in creating intelligent systems that simplify complex tasks. With proficiency in Python programming, Google Apps Script, and server-side technologies like Unicorn and Celery, I have successfully automated various data processes, including web scraping and data flow management. Currently, I freelance part-time with DoubleRo's "Elephant Stock" wall art company, where I contribute to departments like ART, SEO, and Development by addressing their data needs. As an academically-driven postgraduate student in Informatics Engineering, my research interests span machine learning, reinforcement learning algorithms, data mining, computer vision, human-machine interaction, and parallel computing. I possess strong research and communication abilities, am a self-starter, and am always eager to learn and excel in every task. My ultimate goal is to be recognized for my exceptional performance and contributions to the field.





Publications

Mathematical Computations Based on a Pre trained AI Model and Graph Traversal

Published in: 2020 9th Mediterranean Conference on Embedded Computing (MECO) - (IEEE)



Optimizing regular computations based on neural networks and Graph Traversal

Published in: 2020 14th International Symposium "Intelligent Systems - (Elsiever)




Towards Optimization of Big Numbers Computation through an AI Pre-trained Model and Graph Traversal

Published in: 2020 XXIII International Conference on Soft Computing and Measurements (SCM) - (IEEE)






Using OpenMP to Optimize Model Training Process in Machine Learning Algorithms

Published in: 2021 II International Conference on Neural Networks and Neurotechnologies (NeuroNT) - (IEEE)



Accelerating Neural Network Training Process on Multi-Core Machine Using OpenMP

Published in: 2022 II International Conference on Neural Networks and Neurotechnologies (NeuroNT) - (IEEE)



Conferences

Budva, Montenegro June 8-12, 2020 MECO

The 9th Mediterranean Conference on Embedded Computing MECO'2020 8th International Conference on Cyber-Physical Systems and Internet-of-Things CPS&IoT'2020

December 10th - 11th, 2020 MICSECS

At the Anniversary 11th Majorov International Conference on Software Engineering and Computer Systems

July 6-9 2020 Hydra

Concurrent and distributed computing conference

July 6-9 2020SPTDC

concurrent and distributed computing conference

June 28 2020SCM

scientific-practical conference

December 23 2020 ISC

International Scientific Conference of Students, Postgraduates and Young Scientists

June 16 2021 NeuroNT

International Conference on Neural Networks and Neurotechnologies

June 16 2022 NeuroNT

International Conference on Neural Networks and Neurotechnologies

My Resume

Working Experience

2013-2014

Database Designer

Government directorate

Duhok national meusum

2014-2017

Monitoring and evaluation officer

IRC

International Rescue Committee.

2018-2020

Freelancer

World Wide Web

Virtual assistant.

2020-2021

Research and Development

LETI

Computer science department at electrotechni- cal university of Saint-Petersburg(LETI).

2021- Present

Data specialist

Elephant Stock

Development department at DoubleRo 'Elephant Stock" company.

Awards

2020


Honor of best international student in computer science and Knowledge discovery(Outstanding performance)

Electrotechnical University of Saint-Petersburg(LETI)



2020


Computer Program Registration (Optimization program for performing arithmatic operations with large numbers based on pretrained neural network)

Federal Service for Intelectual Property (RosPatent) of Russian Federation


Lectures

Here you can find my lectures

Parallelization of AI algorithms
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Parallelization in Genetic Algorithms GAs
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Machine learning on big data
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Types Of Parallelsim In Machine Learning Algorithms
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Accelerating training process in neural network algorithms using OpenMP
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Face Processing Using Hybrid Machine Learning Approaches
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