🎓 Computer Science student at UniCEUB focused on Machine Learning, AI Engineering and ML Systems.
I like going beyond isolated notebooks and models.
My projects usually revolve around a simple question:
How can we build, evaluate and understand Machine Learning systems in a reproducible way?
My work combines:
🔬 Experimentation & Benchmarking ⚙️ ML Engineering & MLOps 🧠 Machine Learning & Deep Learning 🎮 Reinforcement Learning ☁️ Cloud & ML Platforms
I started programming in 2024 and began focusing heavily on Machine Learning in 2025.
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Educational platform for experimenting with Machine Learning while applying real MLOps practices. Stack
Focus Experiment tracking • Automation • Hyperparameter optimization • Reproducibility |
Experimental interface for comparing different AutoML frameworks under similar conditions. Frameworks
Focus Benchmarking • Metrics • Framework comparison • Experiment organization |
|
Experimental laboratory for Reinforcement Learning and benchmarking. Exploring:
Focus Architecture • Representation • Algorithms • Seeds • Generalization • Benchmarking |
My GitHub contains additional experiments covering different areas of ML: 🧠 Supervised Learning 🎲 Unsupervised Learning 🤖 Reinforcement Learning 📊 AutoML ⚙️ MLOps ☁️ Cloud ML platforms → Explore all repositories |
┌──────────────────────┐
│ Problem │
└──────────┬───────────┘
↓
┌──────────────────────┐
│ Experimentation │
└──────────┬───────────┘
↓
┌──────────────────────┐
│ Model / Architecture│
└──────────┬───────────┘
↓
┌──────────────────────┐
│ Benchmark & Evaluate │
└──────────┬───────────┘
↓
┌──────────────────────┐
│ Analyze & Understand │
└──────────┬───────────┘
↓
┌──────────────────────┐
│ Reproduce / Scale │
└──────────────────────┘
I am particularly interested in experiments where the goal is not simply to find the highest score, but to understand why different approaches behave differently.
Scikit-learn · PyTorch · TensorFlow · Optuna · MLflow
MLflow · DagsHub · Hugging Face · Docker · Git
FastAPI · Flask · React · TypeScript · JavaScript
Azure ML · AWS · Google Cloud · Databricks · IBM watsonx
| Area | Focus |
|---|---|
| 🧠 Machine Learning | Models & representations |
| 🎮 Reinforcement Learning | Agents & environments |
| ⚙️ ML Systems | Pipelines & infrastructure |
| 🔬 Scientific ML | PINNs & learned dynamics |
| 🔗 Causal ML | Causal inference |
| 🎲 Probabilistic ML | Uncertainty & probabilistic models |
Machine Learning
│
├── ML Engineering
│ ├── MLOps
│ ├── Pipelines
│ └── ML Systems
│
├── Experimentation
│ ├── Benchmarking
│ ├── Model Comparison
│ └── Reproducibility
│
└── Research Exploration
├── Reinforcement Learning
├── Scientific ML
├── Causal ML
└── Probabilistic ML
My goal is to become a Machine Learning Engineer capable of building, evaluating and understanding complete ML systems.
