Dashun Wang is a Professor of Management and Organizations at the Kellogg School of Management, and the McCormick School of Engineering, at Northwestern University. At Kellogg, he is the Founding Co-Director of the Ryan Institute on Complexity and the Founding Director of the Center for Science of Science and Innovation (CSSI). He is also a core faculty at the Northwestern Institute on Complex Systems (NICO). Dashun is a recipient of multiple awards for his research and teaching, including the AFOSR Young Investigator award, Poets & Quants Best 40 Under 40 Professors, Junior Scientific Award from the Complex Systems Society, the Erdos-Renyi Prize, Thinkers50 Radar 2021, and more.

At CSSI, Prof. Dashun Wang leads a group of highly interdisciplinary researchers who are extremely passionate about data. His current research focus is on Science of Science, a quest to turn the scientific methods and curiosities upon ourselves, hoping to use and develop tools from complexity sciences and artificial intelligence to broadly explore the opportunities and promises offered by the recent data explosion in science. His research has been published in such general audience journals as Nature, Science, PNAS, Nature Human Behaviour, Nature Physics, Nature Reviews Physics, Nature Machine Intelligence, Nature Communications, and more. It has been featured in virtually all major global media outlets, including The New York Times, Wall Street Journal, The Economist, Bloomberg, Financial Times, The Today Show, Harvard Business Review, The Atlantic, World Economic Forum, Forbes, The Guardian, The Washington Post, and The Boston Globe, among others. Check out his first book: The Science of Science.

My current research focus is on Science of Science, a quest to turn the scientific methods and curiosities upon ourselves, hoping to use and develop tools from complexity sciences and artificial intelligence to broadly explore the opportunities and promises offered by the recent data explosion in science.

Computational Social Science, Science of Science, Computational Social Science, Complex Systems, Big Data, Social Networks

• Ph.D, 2013, Physics, Northeastern University
• M.S., 2009, Physics, Northeastern University
• B.S., 2007, Physics, Fudan University

• Professor, Kellogg School of Management & McCormick School of Engineering, Northwestern University
• Assistant Professor, College of Information Sciences and Technology, Pennsylvania State University, 7/1/2016
• Research Associate, Dana-Farber Cancer Institute, Harvard University, 6/1/2013
• Associate Professor (Courtesy), Industrial Engineering & Management Sciences, McCormick School of Engineering, Northwestern University, 8/31/2021
• Associate Professor of Management & Organizations, Management and Organizations, Kellogg School of Management, Northwestern University, 8/31/2021

• Research Staff Member, IBM T.J. Watson Research Center, 2013-2014

• Amazon Research Award, 2023-2024
• Young Scientists Award, German Physical Society
• Erd?s-Rényi Prize, The Network Science Society
• Thinkers50 Radar, Thinkers50
• Top 100 most-discussed papers across all sciences in 2020, Altmetrics, 2020
• Junior Scientific Award, Complex Systems Society
• World Changing Ideas Awards (honorable mention), Fast Company
• Innovations that Inspire, AACSB
• Top 100 most-discussed papers across all sciences in 2019, Altmetrics
• The 40 Best Business Professors Under 40, Poets & Quants
• Minerva Award, Department of Defense
• Young Investigator Award, AFOSR

• Editorial Board Member, Journal of the Association for Information Science and Technology, 2016

Social Network Analysis, Data Science, Innovation

Special Topics in Management & Organizations: Emerging Topics in Computational Social Science (MORS-521-3)
Social scientists increasingly have access to data sets of unparalleled scope and complexity. At the same time, there has been remarkable development in fields like network science, data science, and machine learning over the past decade, which offers us a wide range of tools that help us make sense of this data with growing accuracy and robustness. Together, the new data and computational methods offer researchers opportunities to explore and understand human behavior at an unprecedented level of scale and detail, fueling the emergence of an exciting, interdisciplinary field called computational social science. This course surveys the emerging frontiers in the field, open to students from both computational and social science backgrounds. For those new to the social sciences, this is an opportunity to see where your computer science and statistical skills can go, with innovative applications to problems of massive societal interest. For those new to computational methods, this is a chance to develop the tools necessary to make new and exciting contributions, tools that will shape the originality and power of your work for years to come.

Technology and Innovation ll (MECS-549-2)
This course establishes fundamental ways in which ideas differ from other goods, then uses these concepts to evaluate the origins of innovation, economic growth, firm dynamics, entrepreneurship, innovation clusters, and the diffusion of new technology. The course substantially reviews core empirical literature, including methods and data sets that are suited to studying ideas and innovation.

Winning with Networks (MORSX-945-0)
The results of a recent IBM survey of over 1500 CEOs worldwide identified complexity as the most pressing challenge facing today's business leaders. To provide Kellogg EMBA students with the tools and skills necessary to confront this accelerating change and increasing interconnection, Social Dynamics and Networks explores cutting edge research on social networks, social media, tipping points, contagion, herd behavior, the wisdom of crowds, and prediction markets. The course employs simple yet powerful interactive models and hands-on exercises to develop understanding of both the theory and applications of social dynamics and network science.

Winning with Networks (MORS-457-0)
Leaders face new levels of connectivity, complexity, and unpredictability. This course prepares leaders to use the powerful tools of computational thinking and network analysis to better anticipate, understand, and respond to these challenges. The content involves in-depth training within vis-a-vis diverse use cases, including patenting, innovation adoption, influentials, social contagion, crowdsourcing, hot streaks, and prediction markets. Content delivery is lecture-based with in-class experiential exercises and a final group project aided by a "genius bar" where students work hand-in-hand with data scientists. Students leave the course with a practical toolkit for leadership vision enhancement, value creation, and the contagious adoption of your ideas and innovations. The course was developed jointly with Professor Uzzi and complements the MORS-430 leadership and organizations course.