Insight-Driven Problem Solving: Analytics Science to Improve the World

by Soroush Saghafian

Overview

Improving public policies, creating the next generation of AI systems, reducing crime, making hospitals more efficient, addressing climate change, controlling pandemics, and reducing disruption in supply chains are all problems where big picture ideas from analytics science have had large-scale impact. What are those ideas? Who came up with them? Will insights from analytics science help solve even more daunting societal challenges? This book takes readers on an engaging tour of the evolution of analytics science and how it brought together ideas and tools from many different fields – AI, machine learning, data science, OR, optimization, statistics, economics, and more – to make the world a better place. Using these ideas and tools, big picture insights emerge from simplified settings that get at the essence of a problem, leading to superior approaches to complex societal issues. A fascinating read for anyone interested in how problems can be solved by leveraging analytics.

Product Details

ISBN-13:	9781009379144
Publisher:	Cambridge University Press
Publication date:	10/31/2025
Pages:	377
Product dimensions:	6.50(w) x 1.50(h) x 9.50(d)

About the Author

Soroush Saghafian is a professor at Harvard University. He is the founder and director of the Public Impact Analytics Science Lab (PIAS-Lab) at Harvard, which is devoted to advancing and applying the science of analytics for solving societal problems that can have public impact. His teaching at Harvard focuses on machine learning and big data analytics tools for solving societal problems. His research has appeared numerous times in the media, and he has received various notable awards for his work.

Table of Contents

Introduction; 1. The big picture of analytics science; 2. Artificial intelligence and machine learning; 3. Queueing theory and resource allocation; 4. Game theory and mechanism design; 5. Linear and nonlinear mathematical programming; 6. Dynamic programming and sequential decision-making; 7. Data analysis; 8. Graph theory, combinatorial optimization, and supply chains; Acknowledgments; Notes; References; Index.