About Me

I'm a passionate professional with a strong academic background, holding a master's degree in Computer Science from the Delft University of Technology and a master's degree in Electrical and Computer Engineering from the National Technical University of Athens. My interest lies in the fields of Artificial Intelligence, Machine Learning, and Computer Vision.

Professional Experience

AI Engineer

PwC Greece
April 2024 - Present

Developed generative AI solutions: Designed and implemented retrieval-augmented generation (RAG) virtual assistants in Python and C#, enhancing domain-specific query resolution and user engagement
Built end-to-end ML pipelines: Created comprehensive machine learning workflows—from data ingestion and feature engineering to model training and deployment - using Azure Machine Learning
Developed scalable backend systems: Engineered robust APIs using Django Rest Framework to support high-impact analytics applications in the public sector
Streamlined deployment workflows: Leveraged containerization and Azure cloud services to deliver scalable, reliable AI solutions
Collaborated across teams: Worked closely with cross-functional stakeholders to translate business requirements into technical deliverables, contributing to the continuous improvement of development practices and tooling

Artificial Intelligence (AI) GenerativeAI Machine Learning Microsoft Azure Cross-functional Collaborations Backend Development Python Django Rest Framework C# ASP.NET Core Qdrant Scikit-learn FLAML

Military Service

Hellenic Army
November 2020 - July 2021

Digitized archived documents
Developed scripts to automate the generation of patrol schedules

Python MS EXcel VBA

Internships

Contributor

Google Summer of Code @ Intel OpenVINO Toolkit
June 2022 - September 2022

Project: Train a DL model for synthetic data generation for model optimization

Developed a GAN for conditional image generation through knowledge distillation
Evaluated post-training quantization using synthetic data
Results demonstrated less than 0.6% accuracy degradation
Paper: Post-training Model Quantization Using GANs for Synthetic Data Generation
Code: github.com/ThanosM97/gsoc2022-openvino

Python PyTorch OpenVINO Flask API HTML JS Deep Learning Computer Vision Generative Adversarial Networks (GANs) Knowledge Distillation Model Quantization LaTeX

Machine Learning Intern

NCSR DEMOKRITOS
July 2020 - September 2020

Project: Automatic Video-Game Review Summarization

Refactored and rewrote the entire codebase of the pipeline for the review summarization system in Python
Evaluated machine learning classifiers for identifying the reviewed aspects of the video-game in users' reviews
Proposed a meta-classification approach which improved the overall performance of the pipeline

Python PyTorch Scikit-learn Natural Language Processing (NLP) Classification Embeddings

Projects

RAGPal: A PoC RAG-based virtual assistant

RAGPal is implemented as a web application with a front-end interface for user interaction and a FastAPI-based back-end for handling requests and business logic. The system utilizes Azure OpenAI API resources for chat completion and embedding generation, and the Qdrant vector database to serve as the knowledge base for storing and retrieving documents. View more..

Python FastAPI AzureOpenAI API Qdrant HTML/CSS JavaScript Large Language Models (LLMs) Retrieval Augmented Generation (RAG) Prompt Engineering

End-to-End Chess Recognition

The goal of this project was to develop a methodology that would predict the chessboard configuration in an input image of a chessboard. Contrary to the predominant approaches, that aim to solve this task through the pipeline of chessboard detection, square localization, and piece classification, we relied on the power of deep learning models to directly predict the configuration from the entire image. View more..

Python PyTorch Lightning Deep Learning Computer Vision Image Processing

Chess Recognition Dataset (ChessReD)

The Chess Recognition Dataset (ChessReD) is a comprehensive collection of images of chess formations that were captured using various smartphone cameras. It comprises 10,800 images from 100 chess games. The dataset features a wide range of chess piece configurations, captured under different angles and lighting conditions. The dataset includes detailed annotations about the pieces' formations in chess algebraic notation, providing valuable information for chess recognition research. View more..

Deep Learning Computer Vision Chess Recognition Object Detection

TeleGAN: Text-To-Image Synthesis using GANs

The goal of this project was to develop a novel architecture which would be able to generate high-resolution images conditioned on a given text description. The proposed model (TeleGAN) decomposes the difficult task of high-quality image generation, into the more manageable sub-problems of low-res black-and-white image generation, colorization, and resolution enhancement, in three consecutive stages. View more..

Python PyTorch Deep Learning Computer Vision Conditional Image Generation Text-to-Image Generative Adversarial Networks (GANs)

Other projects

The rest of my projects can be found on my GitHub account.

Education

Delft University of Technology

MSc in Computer Science
September 2021 - August 2023

Track: Artificial Intelligence
Grade: 8.5/10 (Cum Laude)
Thesis: End-to-End Chess Recognition

National Technical University of Athens

Joint BSc & MSc in Electrical and Computer Engineering
September 2015 - August 2020

Concentration field: Computer Science
Grade: 8.27/10
Member of the Artificial Intelligence and Learning Systems Laboratory (AILS lab)
Thesis: Text-to-image synthesis using Generative Adversarial Networks (GANs)

Certificates

Azure Data Fundamentals (March 2025) by Microsoft [credential]
Azure AI Engineer Associate (Dec 2024) by Microsoft [credential]
Machine Learning Engineering for Production (MLOPs) (Nov. 2023) by deeplearning.ai [credential]
Azure AI Fundamentals (May 2023) by Microsoft [credential]
Azure Fundamentals (May 2023) by Microsoft [credential]
Deep Learning Specialization (Aug. 2019) by deeplearning.ai [credential]
Machine Learning (Apr. 2019) by Stanford|Online [credential]

Publications

Athanasios Masouris and Jan van Gemert (2024). End-to-End Chess Recognition. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, pages 393-403
[SciTePress] [arXiv]

Athanasios Masouris