Daniel Quinn

Assistant Professor

Publications: Google Scholar Profile


Professor Quinn first came to the University of Virginia as an undergraduate student in 2006. After graduating with a BS in Aerospace Engineering, he attended Princeton University and completed a PhD in the Hydrodynamics Lab working on bio-inspired propulsion with Professor Lex Smits. Professor Smits nominated Professor Quinn for the American Physical Society’s Andreas Acrivos Dissertation Award in Fluid Dynamics, which Quinn earned in recognition of his doctoral thesis, Optimizing the Efficiency of Batoid-Inspired Swimming. As a winner, he was invited to the Annual Meeting of the Division of Fluid Dynamics of the American Physical Society, where in November he will give an award lecture describing his PhD research.

While at Princeton, Quinn was also a Visiting Fellow at the Museum of Comparative Zoology at Harvard University. He went on to become a Postdoctoral Fellow in the Bio-Inspired Research and Design group at Stanford University, studying the stability characteristics of birds flying in turbulent gusts. Professor Quinn joined the UVA faculty in the fall of 2016. He is a member of the Link Lab, a group of researchers studying Cyber-Physical Systems – particularly autonomous vehicles, body sensor networks, and smart homes.

Research Interests

Professor Quinn’s research group studies how fluid dynamics can improve cyber-physical systems like autonomous vehicles and energy harvesters. In the realm of autonomous vehicles, his group explores how advanced aerial and underwater robots can make use of predictive fluid models. These models are especially important in conditions where traditional data-driven controllers are unstable, such as when traveling in swarms, near solid boundaries, in crossflows, or in heavy turbulence. Special attention is paid to bio-inspired robotics, so experiments are designed to not only improve vehicle control, but also to provide insights into biolocomotion. The second branch of Quinn’s group studies how fluid models can enable next-generation flow sensors and energy harvesters. Specifically, his group explores how future sensors/harvesters can improve efficiency by combining fluid dynamics, network dynamics, and machine learning. As with autonomous vehicles, experiments are designed to share insights with biologists, in this case by drawing analogies with sensors and energy recapture strategies found in nature.