I'm trying to optimize a notoriously complex loss function, navigating a high-dimensional, non-convex landscape—one small step at a time. Probably, so are you!

• Choose your objective with caution—you drift toward whatever you optimize.
• Curate what you train on—you learn from what you keep feeding yourself.
• Set the pace—bold enough to move, small enough not to overshoot.
• Use momentum—keep going; let earlier progress carry you through flat stretches.

MEng in Electrical and Computer Engineering (Aristotle University of Thessaloniki, top 3% of class) and an MSc in Artificial Intelligence (magna cum laude, KU Leuven), plus two published papers on AutoML for anomaly detection and smart-meter security.

Since 2015: research associate at CERTH (EU cloud computing), software engineer at Veltio (supply-chain data pipelines for retailers like Sainsbury's), ML research engineer at ISSEL/AUTH (technical lead on an EU energy-monitoring project — NLP, event streaming), and since 2022, machine learning scientist at Expedia Group in London, working on large-scale ranking and retrieval with deep learning, LLMs, and multimodal methods.

Four active, self-directed repos exploring ML/AI from first principles — most other side projects, coursework, and research code are archived.

• algorithm-visualizers (live demo) — 21 classic ML/CS algorithms, each a from-scratch NumPy implementation with an interactive Streamlit + Plotly walkthrough: clustering, dimensionality reduction, classification, deep learning building blocks, generative models, graph algorithms, probabilistic methods, and reinforcement learning.
• transformer-atlas (map) — a structured map of the transformer architecture, from the 2017 original through the attention variants, positional encodings, MoE routing, and inference tricks behind today's frontier LLMs, each with a minimal runnable PyTorch implementation.
• modern-ai-systems-and-methods (site) — a 19-chapter field guide to modern AI, from supervised learning through agents and responsible AI, with real computed visualizations and explicit links between chapters.
• optimization-lab (docs) — from-scratch implementations of ~30 classical optimization algorithms behind a shared Problem -> OptimizeResult interface, a solver-arena comparison harness, and cross-domain applications in physics, economics, and ML.

Full CV here.

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