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During Dean’s Seminar Series Lecture, Dashun Wang Stresses Hope

Wang said “your miracle year may still be ahead of you”

Your best work could still be ahead of you.

During his Dean’s Seminar Series lecture “Distilling Lessons from the Science of Science,” Northwestern Engineering’s Dashun Wang used the example of 92-year-old John B. Fenn’s 2002 Nobel Prize in Chemistry to show why you can hope your most important contributions are still to come.

“The randomness of when the hot streak occurs within your sequence of work can be a very hopeful message,” Wang said. “The conventional wisdom tells us that you do your best work somewhere in mid-career. As you get older, it seems like you could be less hopeful, but what this is saying is that your miracle year may still be ahead of you; it’s just out of sight.”

Wang is a professor of management and organizations at the Kellogg School of Management, and of industrial engineering and management sciences at the McCormick School of Engineering. He also is director of the Center for Science of Science Innovation, and a core member of the Northwestern Institute for Complex Systems.

Dashun Wang

Best known for his contributions on the science of science, a quest to turn the scientific methods and curiosities upon science itself, Wang highlighted examples of research in this area, illustrating the promise of the discipline as well as its limitations. During the discussion, Wang revealed that smaller scientific teams are more disruptive than bigger ones, and that people who barely miss early success are actually more likely to thrive later than people who narrowly succeed.

“We want to do work that has impact, and now more than ever we can quantify that impact,” Dean Julio M. Ottino said during his introductory remarks. “I think the ability to examine the scientific enterprise in a quantitative way is something extremely important.”

The increasing availability of large-scale datasets that trace the entirety of the scientific enterprise has created an unprecedented opportunity to explore scientific production and reward. Parallel developments in data science, network science, and artificial intelligence offer powerful tools and techniques to make sense of these millions of data points. Together, they tell a complex yet insightful story about how scientific careers unfold, how collaborations contribute to discovery, and how scientific progress emerges through a combination of multiple interconnected factors.

I think the ability to examine the scientific enterprise in a quantitative way is something extremely important. Julio M. Ottino Dean, McCormick School of Engineering

One of Wang’s discoveries concerns hot streaks.

Wang found that hot streaks are ubiquitous across artists, movie directors, and scientists. Ninety percent of creators have a hot streak, while having two is uncommon and three is exceedingly rare. The hot streaks last 4-5 years and are not associated with a change in productivity.

After experiencing his eureka moment when visiting the Van Gogh Museum in Amsterdam, Wang found that hot streaks directly result from years of exploration (studying diverse styles or topics) immediately followed by years of exploitation (focusing on a narrow area to develop deep expertise).

“Hot streaks are important for science decision-making, because in science the projection of future impact is important for decisions such as hiring, promotion, and tenure as well as investment in research,” Wang said. “What this tells us is that if you want to capture an individual’s future impact, you ought to take into account hot streaks in your calculus. If you ignore hot streaks, it may lead you to over- or underestimate an individual’s future potential.”