Marina Dubova

Complexity Postdoctoral Fellow

Omidyar Postdoctoral Fellow Starting at SFI August 2024

Starting at SFI August 2024

Marina is a computational cognitive scientist trying to reveal and inform the cognitive mechanisms of discovery. She uses insights from the learning sciences to see how theories and data can be integrated to come up with understandings of the world. She develops formal (e.g., computational models) and empirical (e.g., behavioral experimental procedures to study scientists) methods to put the foundations of scientific method to rigorous tests.

Marina’s research has lead to some arguably counterintuitive results, indicating that 1) more exploratory experimentation might help scientists develop more accurate & useful theories than the theory-informed experiments (e.g., the ones that explicitly aim to challenge an existing theory or resolve theoretical disagreements), or that 2) learning theories or models with a complexity bias (i.e., expanding, rather than compressing, the dimensionality of one’s observations) sometimes leads to more efficient learning about the world. While at SFI, Marina hopes to further expand the understanding of cognitive mechanisms of discovery by investigating scientists from various disciplines (including SFI’s own residents and visitors) and conducting formal studies of learning about the world by observing and representing it. Her goal is to help improve the scientific system by characterizing ways in which it can produce more useful knowledge for society.

Marina grew up in Saint Petersburg, Russia. She holds a B.S. in Psychology from Saint Petersburg University and PhD in Cognitive Science from Indiana University—Bloomington. When not learning or studying learning, she likes to play music, travel, conduct clay & glazing studies, work on her bird categorization skills, and practice sailing away from and back to the marinas without sinking.