David Pugh

Biography

Professor David Pugh works at the intersection of artificial intelligence, data science, and high-performance computing. He develops applications in machine learning, deep learning, and generative AI, including work with Large Language Models (LLMs). He is an experienced research software engineer and data scientist with deep knowledge of the Python data science ecosystem, including NumPy, SciPy, Pandas, Matplotlib, NetworkX, Jupyter, Scikit-learn, PyTorch, and TensorFlow.

Pugh recently developed training materials to help data scientists manage virtual environments using Conda and Docker. He is currently developing data engineering solutions to accelerate distributed training of deep neural networks on high-performance computing resources.

He leads the Accelerating GenAI Adoption theme at the KAUST Center of Excellence for Generative AI (GenAI), where his work includes developing GenAI platforms, supporting innovation and creating training and residency programs to foster GenAI adoption, particularly among non-experts.

Pugh completed his postdoctoral research at the University of Oxford in 2016, after earning an M.Sc. in 2009 and a Ph.D. in Economics in 2014 from the University of Edinburgh. He holds a B.S. in Mathematics from the College of William and Mary.

His teaching covers the full AI pipeline, including data collection, preprocessing, model training, evaluation and deployment. He emphasizes responsible model development and encourages students to consider the ethical and societal implications of AI technologies.