LAMPROS PAPAIOANNOU

ELECTRICAL AND COMPUTER ENGINEER

Final-year Electrical and Computer Engineering student at the National Technical University of Athens (NTUA), specializing in Energy Systems. I combine the rigorous mathematical and analytical foundation of an engineering degree with practical skills in Python and Machine Learning. Passionate about applying AI techniques to energy infrastructure, with a strong aptitude for reverse-engineering complex systems and codebases to deliver data-driven solutions.

[email protected] 6989686482 Athens, GREECE

Languages

Skills

CAD/CAM & Simulation: Fusion 360, ANSYS, PanelCADTools/Other: Git, GitHub, VS Code, Linux/Bash, LaTeXAI/ML: Machine Learning Fundamentals, Data Analysis, Neural NetworksEngineering: Power Systems Analysis, Mathematical Modeling, Circuit DesignProgramming: Python (NumPy, Pandas, Scikit-learn, Matplotlib)Problem SolvingEffective Communication

Education

Master of Science, Electrical and computer engineering

National Technical University of Athens (NTUA)

2018 — Present72,9%

Expected Graduation: September 2026
Specialization: Energy Systems
elevant Coursework: Control Systems, Electric Power Systems, Algorithms & Data Structures, Statistics, Systems Theory.

MIT Learn Certificate, Universal AI Course

Massachusetts Institute of Technology (MIT)

2026 — Present

I am currently taking the Universal AI course via MIT learn, that provides a certificated focusing on advanced Machine Learning architectures, generative models, AI-driven system optimization and hands-on application of AI frameworks to solve real-world problems.

Languages

Greek

Native

English

Advanced

PROFESSIONAL COMPETENCIES

Reverse Engineering Mindset

Skilled in analyzing existing complex codebases and systems, identifying logical bottlenecks, and implementing optimized solutions through rigorous debugging and deduction.

Technical Adaptability

Proven ability to rapidly acquire and apply new AI frameworks and software tools.

Analytical Reasoning

Strong capability in breaking down complex engineering challenges into manageable, solvable components with high attention to detail.

PROJECTS AND RESEARCH

Diplomatic Thesis

AI-Driven Performance Prediction in Hydrogen Fuel Cells

Designed and implemented a Neural Network model to predict the efficiency and performance of hydrogen fuel cells.
Performed end-to-end data preprocessing, cleaning, and feature engineering on experimental datasets to optimize model training in a Python environment.
Conducted rigorous analysis of model outputs to determine optimal operational parameters for maximum system efficiency.
Key Competency: Successfully bridged the gap between physical phenomena (Hydrogen Fuel Cells) and algorithmic modeling.

Medical AI

Parkinson’s Disease Symptom Detection

Developed a Neural Network in Python trained on PubMed medical datasets to identify Parkinson’s symptoms, focusing on data preprocessing and model accuracy.

Electrical & Physical Simulation

Signal Analysis: Conducted wave propagation simulations using MATLAB Ray Tracing.
Professional Design: Applied PanelCAD for the technical design and documentation of electrical installations, ensuring compliance with industry standards.