I am a PhD student in Physics with a strong academic background in machine learning, data science, and optimization.
My research focuses on applying reinforcement learning, genetic algorithms, and neural networks to complex quantum systems, involving high-dimensional optimization, noisy environments, and rigorous model evaluation. This work has led to peer-reviewed publications and international conference presentations.
Alongside my doctoral research, I have completed a Data Science Diploma, where I worked on applied projects using real-world datasets, including natural language processing with embeddings, supervised learning on medical data, clustering and representation learning, and offline recommendation systems based on sequential models.
I regularly run experiments on remote university servers and shared computing infrastructure, working with Linux environments, SSH, job schedulers, and long-running training jobs.
- Machine Learning & Deep Learning
- Reinforcement Learning & Sequential Decision Making
- Natural Language Processing & Embeddings
- Optimization & Evolutionary Algorithms
- Quantum Computing & Quantum Machine Learning
Programming
- Python (advanced), Fortran
- Bash / Shell scripting
- C (basic)
Machine Learning & Data
- PyTorch, TensorFlow
- scikit-learn, Pandas, NumPy, SciPy
- Jupyter, Matplotlib, Seaborn, Plotly
Quantum Computing
- Qiskit, QuTiP, QuLacs
Infrastructure & Tools
- Linux, Git, SSH
- Remote servers & HPC environments
- SLURM (job scheduling)
Cloud
- Amazon Web Services (Introductory training)
➡️ More details on my projects and publications are available on my
👉 personal website
⚡ Free culture and free software supporter ⚡