Hi, I'm George Vergos

I’m a passionate developer and creative thinker with a deep love for coding and technology. I enjoy crafting elegant solutions and tackling complex challenges through programming

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Experience

Software Developer at Teka Systems

SAP BTP Applications, Integration / Automation of Business Processes

Sept 2024 - Today

Military Service

Engineer Directorate - Greek Army

Sept 2023 - Sept 2024

Smart Embryo Project

Classification and Characterization of fetal images for IVF applications using DL and CV methodologies.

Nov 2022 - Sept 2023

Server Administrator

Administration and maintenance of Linux SSH Server in the Physics Department.

Nov 2022 - Sept 2023

Technical Skills

Programming Languages

Python, C/C++, Groovy, Matlab, Mathematica

Frontend

HTML5, TailwindCSS/CSS, SAPUI5, Svelte 5, Next.js

Backend

Javascript/Typescript, Node.js, CAP Progamming Model

Cloud

SAP BTP, Cloud Foundry, Docker, AWS

Databases

HANA, PostgreSQL, SQLite

Operating Systems

Arch Linux, Debian Linux, Windows

Version Control

Git

Best Editor

NeoVim

Documents

Microsoft Office Suite, LaTeX

Publications

1
Artificial Intelligence-Empowered Embryo Selection for IVF Applications: A Methodological Review

This work reviews recent advances in applying artificial intelligence and deep learning to improve embryo selection in IVF, highlighting current architectures used, their potential to enhance success rates and reduce costs, and outlining future challenges for AI-based assisted reproductive technologies.

2025
2
Fetal Images Trophectoderm Score Prediction Using Deep Learning Methodologies

In-vitro fertilization (IVF) is an assisted reproductive technology (ART) and is regarded as one of the most successful. This work utilizes deep learning methods for the task of TE score prediction.

2024
3
Ensemble Learning Technique for Artificial Intelligence Assisted IVF Applications

A deep learning ensemble model trained to classify blastocyst images to enhance the success rate of IVF procedures by utilizing novel AI techniques.

2023
4
Machine Learning based Power Converter Large Signal Simulation for Energy Harvesting Applications

Machine learning models simulate the dynamic behavior of power DC-DC converters, significantly improving simulation time while maintaining accuracy.

2022
5
Comparing Machine Learning Methods for Air-to-Ground Path Loss Prediction

The comparison of various machine learning algorithms (kNN, SVR, RF, AdaBoost) for radio coverage simulation, highlighting the performance of tree-based ensemble models.

2021

My Projects

Text Editor

Text Editor

A simple text editor built with modern Rust.

Calculator

Calculator

An interactive calculator app, supporting basic arithmetic operations.