Areas of interest: 

Computational neuroscience, artificial intelligence, brain-computer interfaces


Computational Neuroscience and Brain-Computer Interfaces
The primary goal of my research is to discover the computational principles underlying the brain's remarkable ability to learn, process and store information, and to apply this knowledge to the task of building adaptive robotic systems and brain-computer interfaces (BCIs). How does the brain learn efficient representations of objects and events occurring in the natural environment? What are the algorithms that allow useful sensorimotor behaviors to be learned? What computational mechanisms allow the brain to adapt to changing circumstances and remain fault-tolerant and robust? How can the knowledge gained through computational studies of the brain be used in biomedical applications such as BCIs for the disabled? My students and I are investigating these questions using a combination of probabilistic techniques, computer simulations, and collaborative neurobiological experiments. Such an interdisciplinary approach has provided functional interpretations of several otherwise puzzling neurobiological properties while at the same time suggesting biologically-inspired solutions to problems in computer vision, robotics and artificial intelligence.


Humanoid Robots that Learn from Humans
We are developing new methods that allow a humanoid robot to learn new actions and skills from a human teacher in much the same way that human infants and adults learn through observation and experimentation. Such an approach opens the door to a potentially powerful way of programming general-purpose humanoid robots--through human demonstration, obviating the need for complex physics-based models and explicit programming of behaviors.



Analysis of the 4000-year-old Indus Script
Despite a large number of attempts, the script of the Indus civilization (circa 2500-1900 BC) remains undeciphered. The absence of a multilingual "Rosetta stone" as well as our lack of knowledge of the underlying language have stymied decipherment efforts. Rather than attempting to ascribe meaning to the inscriptions, we are applying statistical techniques from the fields of machine learning, information theory, and computational linguistics to first gain an understanding of the sequential structure of the script. The goal is to discover the grammatical rules that govern the sequencing of signs in the script, with the hope that such rules will aid future decipherment efforts.