Mark Bugden

About Me

I'm a Machine Learning Scientist with a background in theoretical physics, driven by curiosity and a desire to get to the heart of complex problems. I care about turning raw data into insight — not just predictions, but understanding.

I have 4+ years of experience with Machine Learning, from University research to working on end-to-end development of models for real-world applications. My work spans both classical machine learning and deep learning, using tools like XGBoost, scikit-learn, and neural networks built with TensorFlow and PyTorch.

I work primarily in Python and have strong experience with core data science and visualization libraries including Pandas, NumPy, Matplotlib, Seaborn, and Jupyter. I have experience deploying machine learning models into production, using Docker for containerization, FastAPI for serving models as APIs, and Kubernetes for orchestration in cloud-based environments. I'm also familiar with using GitLab CI/CD to automate development and deployment workflows, and I work with APIs to access external data sources and expose models for downstream use. For experiment management and scalable configuration, I typically use MLflow for tracking and model versioning, and Hydra for modular configuration across development environments.

You can find more information about the academic papers I have written here.