Pratim Sengupta

Assistant Professor, Learning Sciences & Science Education

Director, Mind, Matter & Media Lab

Vanderbilt University - Peabody College

Pratim Sengupta is an Assistant Professor in the following PhD programs at Vanderbilt University: Learning Sciences & Learning Environments Design (LSLED), and Mathematics & Science Education (MSE). He also directs the Mind, Matter & Media Lab.

Sengupta's research is funded by several grants from the National Science Foundation, and he is the recipient of an NSF CAREER Award.

Sengupta designs and develops agent-based computational technologies for learning (Strand 1), models of how people think and learn (Strand 2), and multi-agent models of complex social and and natural phenomena (Strand 3). Strand 1: Sengupta designs and develops agent-based, visual and tangible programming languages and modeling platforms for a wide age group of learners. Much of this work is for K12 students, to help them develop two kinds of expertise: scientific modeling, and computational thinking. He is particularly passionate about supporting long-term development of generative representational practices in children - a focus that (unfortunately) has largely been missing in the field of learning technologies.

Strand 2: Along the cognition strand, Senpgupta build models of how people (experts, professionals, students) learn, represent, and reason about complex phenomena. He uses a combination of theoretical, empirical and computational approaches to do so. Note that Strands 1 and 2 are often deeply intertwined.

Strand 3: Sengupta began his research life as an upper-undergraduate Physics student with an interest in non-linear dynamical systems. After nearly two decades of metamorphosis, he still finds himself deeply interested in complexity. Since 2004, when Sengupta formally shifted from Physics to Learning Sciences, his work has been shaped by multi-agent systems, which he finds to be both delightful and powerful as a modeling paradigm. As a result, along with his colleagues and students, Sengupta continues to build agent-based models in domains as diverse as political economy, environmental engineering, and art, including a crowdsourced model of artist networks that was exhibited at the MoMA, NYC. He find that this exercise is not only intellectually enriching, but that it also helps him develop better tools for learning.