Natalia Summerville
Director of Applied Data Science

Natalia Summerville is the Director of Applied Data Science in the Strategy and Innovation Division at Memorial Sloan Kettering Cancer Center. Her team develops data analytics products to support hospital strategy and innovations in care delivery, as well as cutting-edge cancer research. Previously, she led a team of Operations Research and Machine Learning experts at SAS, building analytical engines for customers across industries such as Health Care, Life Sciences, Retail, and Manufacturing. Natalia has been teaching undergrad and grad-level classes in Operations Research, Data Analytics, and Machine Learning since 2005, and she is currently an Adjunct Professor at Duke University. She is deeply passionate about the Data4Good movement and has been collaborating with many non-profit and mission-driven organizations to implement data analytics for social good. She is a board member within the “Pro-Bono Analytics” committee and is part of the “Franz Edelman Award” committee at INFORMS.


By This Author

Natalia Summerville | SAS Support

Operations Research for Social Good: A Practitioner’s Introduction Using SAS® and Python

Advance your knowledge of operations research and social good!

Recent technological developments allow data analytics practitioners to solve large problems better and faster with state-of-the-art artificial intelligence (AI) tools. At the same time, humanity faces overarching challenges such as the climate crisis, child malnutrition, systemic racism, and global pandemics, among others. Operations Research for Social Good: A Practitioner’s Introduction Using SAS® and Python showcases operations research (OR) methodologies typically required in engineering curricula to applications targeted to make this world a better place.

Designed for data scientists, analytics and operations research practitioners, and graduate-level students interested in learning optimization modeling with applied use cases, this book provides the skills to model and solve OR problems with both SAS and Python as well as practical tools and tips to bridge the gap between academic learning and real-world implementations based on Data4Good initiatives.