We aim to build predictive models to help understand human movement using a combination of theory, computational modeling, and experiments
We are a part of Brain and Cognitive Sciences at MIT , have a shared affiliation with Electrical Engineering and Computer Science, and the new Schwarzman College of Computing.
We are hiring for multiple open positions. Please join us.
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 build truly predictive models i.e. either theory-guided model predictions that suggest new experiments, or data-driven model predictions of phenomena not present in training
to let our scientific questions guide the datasets, tools, and level of biological analysis we work with, 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
