About
Seasoned software engineer with 4+ years of experience and expertise in .NET technologies and Spring, specializing in C#, Java, VB.NET, and SQL. I excel at crafting well-organized code and modules, contributing to high-quality product development.
Notable achievements:
- Led the development of a scientific product for dietitians and sport scientists, featuring custom reporting, a robust SQLExpress database, and advanced UI controls.
- Created a Python application for mole image classification with over 87% accuracy, published in IOPScience: Journal of Physics.
- Played a pivotal role in the development of a highly intricate Java project for the Danish wealthcare benefit and health system, actively shaping the codebase and code reviewing on Azure DevOps.
I’m passionate about embracing challenges, fostering innovation through effective communication, and outside of work, you can find me enjoying MotoGP and hitting the gym for some heavy lifting.
What I'm Working On
Youtube Tutorials on .net
Creating Greek-language YouTube video tutorials with C# and .NET is my endeavor. My objective is to meticulously document and offer comprehensive guidance to aid individuals in their pursuit of knowledge. My aspiration is to inspire individuals to engage more deeply in coding by providing tutorials in their native language.
Licenses & Certifications
Coding Bootcamp (Feb. 202)
Full Stack Development bootcamp (Front End, C# ASP.NET MVC (Back end), SQL and NoSQL Databases)
Final Project: Certification System
PeopleCert
Machine Learning Workshop (Oct. 2017)
Machine Learning with Python and convolutional neural networks
mindspace LAB
Parkinson Disease Detection Using Speech (Apr. 2018)
Detecting Parkison Disease using Python (Machine Learning)
University of West Attica
Certificate of Competency in English (Dec. 2010)
English Proficiency
University of Michigan – Ann Arbor
Getting Started with Python and Data Analytics (Nov. 2016)
Basic of Python and Data Analytics (TensorFlow, NumPy, SciPy, Pandas, Matplotlib)
mindspace Lab
Projects
Mole Image Classification Using CNN (Python)
Using GoogLeNet and changing the last layers so it would work more precise for out type of images. The classification is a score (%) that shows the possibilities the mole is benign and the possibilities the mole is malignant.
Supermarket Management System (Blazor) - .NET 6.0
“Supermarket Management System” is .NET 6.0 software application that helps supermarkets and grocery stores manage their daily operations with ease, including inventory management, point-of-sale, customer management, reporting, and user management using Identity framework.
Github Repository
Home Linux Server
Turn my old Desktop PC into a home server. I will be using Ubuntu Desktop as my OS. Remote access using OpenSSH-Server and proper network setup. Network file share using samba and accessing it thought web using plex. Lastly adding home assistant using Docker for everything server related.
Candidate Management System (ASP.NET) - .NET Framework 4.8
The Candidate Management System and Candidate Login Service is a .NET Framework 4.8 comprehensive solution for managing and accessing candidate information, including login functionality for candidates.
Github Repository
Front End CRUD (HTML/CSS/JS)
A CRUD application has been created with two connected objects utilizing keys, the Database like schema is controlled using javascript to manage the connections. (Project includes only HTML,CSS and JS).
Github Repository
Translation Greek/German Application (Winforms)
A simple application (made using VB.net and SQL DB) that lets you insert a German (DB can understand the encoding of German diphthong) word in it’s Greek translation. Then you can select a random word from the database either Greek or German and try to translate it (with a button in the solution). Also has different group categories.
Github Repository
Publications
A software platform for the analysis of dermatology images
The purpose of this paper is to present a software platform developed in Python programming environment that can be used for the processing and analysis of dermatology images. The platform provides the capability for reading a file that contains a dermatology image. The platform supports image formats such as Windows bitmaps, JPEG, JPEG2000, portable network graphics, TIFF. Furthermore, it provides suitable tools for selecting, either manually or automatically, a region of interest (ROI) on the image. The automated selection of a ROI includes filtering for smoothing the image and thresholding. The proposed software platform has a friendly and clear graphical user interface and could be a useful second-opinion tool to a dermatologist. Furthermore, it could be used to classify images including from other anatomical parts such as breast or lung, after proper re-training of the classification algorithms.