By the time she was six years old, Nadya Bliss had already figured out her
professional calling. She knew that one day she would be a mathematician.
"I'm a geek at heart," confesses Bliss, now the assistant vice
president for research strategy in Arizona State University's Office of
Knowledge Enterprise Development. "But I've never wanted to be the kind of
mathematician who just sits in the corner and does things on her own."
Bliss knew that crunching numbers could have a broad impact beyond just the
"geek" community. As a result, she seeks out interdisciplinary
research opportunities that let her contribute to a wide variety of fields.
She is currently working with science historian Manfred Laubichler, a
professor in ASU's School of Life Sciences, part of the College of Liberal Arts
and Sciences. The two researchers have developed a set of mathematical
techniques to detect the emergence of innovation in research. It's a broad
framework that pulls together concepts from graph theory, electrical
engineering and applied mathematics to identify interesting patterns from large
"Analysis of networks is basically analysis of entities and
relationships among them--for example, people and their friends and how they're
interconnected," Bliss says. Other examples of networks could be cars on a
road, the interaction of proteins or computer networks. Bliss and Laubichler
are focused on a network of research citations from about 300,000 authors in
the field of developmental biology.
Laubichler has compiled detailed analyses of certain periods of innovation,
especially in developmental biology. He has extensive records of research
breakthroughs and the scientists involved in them, dating back to the 1960s.
Bliss used this data to create a mathematical filter that can detect certain patterns
a citation network, ultimately identifying people who spurred innovation in
When she applied the construct to a network of citations produced from 1969
to 1980, she got a positive result. The filter pinpointed a couple of key individuals,
and after cross-referencing with Laubichler's historical records, Bliss
determined they were involved in innovation.
Next, the researchers applied the same filter to the developmental biology
citation network from 1990 to 2000. Again, the results were positive, correctly
identifying scientists involved in innovation. By analyzing the interactions
among authors of scientific papers, the mathematical model serves as a kind of
"formula for innovation," Bliss says.
The filter Bliss and Laubichler created has several promising applications.
For example, being able to detect the emergence of innovation would allow
funding agencies to provide resources or support to the right people at the
"One application of this could be working with NSF to continuously
track publications and apply the filters to these large networks and see where
there are emerging patterns, and maybe detect them before they've emerged and
identify those as areas of potential in the scientific community," Bliss says.
A next logical step in the research would be to apply the filter to citation
networks of other fields, outside of developmental biology. Ultimately,
researchers believe the results could provide evidence for the efficacy of
"One of the things you actually see in publication networks is that a
lot of times when there is a major change to the field, there is a set of
fields that are touching each other, so authors from different areas end up
working together," Bliss says.