Thinking Critically About How We Do Science
Dani S. Bassett, Ph.D.
Associate Professor of Bioengineering, Professor of Physics and Astronomy
University of Pennsylvania
Professor Dani S. Bassett’s group studies the structure and function of networks, predominantly in physical and biological systems. Their interests lie in using and developing tools and theories from complex systems science, statistical mechanics, and applied mathematics to study dynamic changes in network architecture, the interaction between topological properties of networks and physical or other constraints, and the influence of network topology on signal propagation (mechanical, electrical, informational) and system function. In physical systems, their group conducts research in dynamical systems as well as granular and particulate matter, and recent studies have considered synchronization dynamics in Kuramoto oscillators, force chain network structure in granular matter, reconfiguration of force chains under compression, and acoustic transmission through force chains. In biological systems, their group conducts research in brain connectivity and human behavior. Among other things in this area, their group has investigated collective dynamics in human behavior, how humans learn graphs of related concepts, how brain connectivity reflects cognitive capacities and changes during adolescent development, and how brain connectivity is altered in neurological disease (such as epilepsy and Alzheimer’s) and disorders of mental health (such as schizophrenia and autism); these studies touch on applied algebraic topology, network control theory, maximum entropy models, multilayer networks, multiplex networks, temporal networks, and annotated graphs.
Science is a beautiful rational process of highly structured inquiry that allows us to learn more about our world. By it, we see past old theories, and build new ones. We realize a phenomenon occurs because of this, and not that. Perennially the skeptic, we spar with our own internal models of how things might happen: always questioning, ever critical, rarely certain. What if we were to turn this audacious questioning towards—not science—but how we do science? Not broadly a natural phenomenon but more specifically a human phenomenon? This query is precisely what drives the field of the science of science. How does science happen? How do we choose scientific questions to pursue? How do we map fields of inquiry? How do we determine where the frontiers are, and then step beyond them? In this talk, I will canvas this broader research agenda while foregrounding recent advances at the intersection of science of science, machine learning, and big data. Along the way, I’ll uncover gender, racial, and ethnic inequalities in the most obvious of places (the demographics of scientists) and also in the most unexpected and out-of-the-way places (the reference list of journal articles). I will consider what these data mean for the way we think about science—for our theories of what science is. What opportunities might we have to see past old theories and build a new one? What possibilities to lay down a new praxis for a science of tomorrow?
This page was last updated on Wednesday, May 18, 2022