Skills & Profile
A hybrid profile at the intersection of particle physics and data / machine learning engineering — from designing experiments and simulations to shipping models and services in production‑like environments.
Particle Physics & Research
Academic training as a particle physicist, from theory to simulation and analysis.
High‑Energy Physics
Collider phenomenology, SM & BSM intuition, stats‑heavy analysis
Simulation & Tooling
GEANT4, ROOT, Monte Carlo workflows
Data Reduction Pipelines
From raw events to analysis‑ready ntuples
Uncertainty & Inference
Likelihoods, systematics, significance, fits
Scientific Communication
Papers, talks, posters, peer‑review feedback loops
Data Science & ML Engineering
Turning messy data into deployed models and measurable impact.
Core Stack
Python, NumPy, pandas, Jupyter, SQL, Docker
Classical ML
Scikit‑learn, XGBoost, LightGBM, feature engineering, evaluation
Deep Learning
PyTorch / TensorFlow, CNNs, RNNs, Transformers
MLOps Mindset
Reproducible experiments, versioning, monitoring metrics
Applied Domains
Tabular forecasting, classification, image processing
Software & Data Stack
Engineering practices that make research and ML actually shippable.
Backend & APIs
Python/FastAPI, Node.js, RESTful services
Data Stores
PostgreSQL, MongoDB, efficient schema design
Dev Environment
Linux, bash, Git, containers, remote workflows
Productivity
VS Code/Cursor, testing mindset, automation scripts
Collaboration
Code review, documentation, mentoring mindset
Certifications & Achievements
ALX Software Engineering
Graduate of the ALX Software Engineering program with hands-on experience in software development, algorithms, data structures, and web development. Equipped with skills to tackle real-world challenges and build efficient software solutions.
Deep Learning Specialization
Completed 5 courses covering neural network architectures including CNNs, RNNs, LSTMs, and Transformers. Mastered optimization strategies like Dropout, BatchNorm, and Xavier/He initialization. Applied concepts to real-world cases including speech recognition, NLP, and chatbots.
Machine Learning Specialization
Completed all three courses covering modern ML concepts including supervised learning, unsupervised learning, recommender systems, and reinforcement learning. Gained practical skills to apply ML techniques to challenging real-world problems.