William Korcari

Designed and developed machine learning models—including GANs, VAEs, Diffusion Models, and Normalizing Flows—both independently and as part of collaborative teams. Built pipelines for retrieving, cleaning, and exploring large and complex datasets. Built and automated ML pipelines on distributed compute clusters, experience directly applicable to modern cloud-based ML workflows. Applied industry-standard ML libraries (PyTorch, TensorFlow) to design and scale deep learning models. Developed effective communic

Contributed to large-scale data analysis and ML-driven simulations within one of the world’s largest scientific data environments (petabyte-scale datasets). Designed and applied machine learning workflows to optimize simulation and event reconstruction, improving efficiency and scalability. Collaborated with international teams of physicists and engineers, demonstrating effective cross-functional teamwork in a highly distributed environment. Developed reproducible research pipelines and contributed to share

Doctoral student in the field of Generative Machine Learning applied to High Energy Physics.