Sethapathi joins the BCS faculty in January 2022 and is currently seeking graduate students, postdoctoral researchers, and undergraduate researchers. The Seethapathi lab aims to build predictive models to help understand human movement using a combination of theory, computational modeling, and experiments.
Understanding the objectives governing movement decisions
We aim to study the computational objectives that govern movement decisions, the contexts in which they arise, and how different objectives are traded-off with one another. For instance, we have studied the role of energy, stability, and time in governing movement.
Understanding the strategies used to execute movement
We aim to study the internal and external variables that guide our actions, the mathematical relationship between these variables, and the algorithms by which they are coordinated. For instance, we have studied the internal variables that guide step to step locomotor control in the presence of noisy actuation.
Understanding how new movements are learned
We aim to study how new movements are selected in the face of novel demands, how the space of solutions is explored, and the ways in which learning can be improved. For instance, we have developed a theory of locomotor adaptation that predicts multiple observed experimental phenomena.
Our research is guided by the following principles
to study movements that are ecologically relevant and capture the complexity of movement in the real world
to let our scientific questions determine the tools we use, and not vice versa; what Nidhi calls a nails-not-hammers approach
to extend our science to the development of tools for neuromotor rehabilitation
to openly share all the data collected and code developed by us in useful form
to accelerate the rate of discovery in our field through meaningful collaborations