I'm based in the Knowledge and Concepts Lab, Schloss Visual Reasoning Lab, and Social Interaction Lab at the Wisconsin Institute for Discovery. I've also been very fortunate to be a Kohler Fellow at WID, which has let me think about how science and art can intersect to give rise to fun new ideas. I believe we do our best science when working collaboratively as groups and so I can often be found in virtual meeting rooms with members of the Cognitive Tools Lab at Stanford, Visual Intelligence and Technological Advances Lab at York, and Neuroscience of Cognitive Control Lab at Princeton.
I grew up in Kolkata, India, a bustling city with hot summers and torrential monsoons. Noticing a distinct lack of snow in my climate collection, I moved to upstate New York, where I received my BA in Cognitive Science and Japanese from Vassar College. While at Vassar, I was advised by Ken Livingston and Josh de Leeuw. I also spent a summer at the Computation and Cognition Lab at Stanford as a CSLI intern, working with Judy Fan and Robert Hawkins (and continue to today!).
|Oct 6, 2023||Our paper SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction was accepted to the NeurIPS Datasets and Benchmarks track! See publications for more!|
|Oct 6, 2023||Excited that our paper Conceptual structure coheres in human cognition but not in large language models was accepted to EMNLP 2023! Preprint forthcoming!|
|Aug 1, 2023||Our paper Leveraging Artificial Neural Networks to Enhance Diagnostic Efficiency in Autism Spectrum Disorder: A Study on Facial Emotion Recognition was accepted for an oral at CCN 2023! Looking forward to seeing everyone in England!|
|Jul 26, 2023||We presented two posters at CogSci 2023. Evaluating machine comprehension of sketch meaning at different levels of abstraction and Behavioral estimates of conceptual structure are robust across tasks in humans but not large language models. See publications for more details~|
|Apr 11, 2023||I will be giving a talk at the UW-Madison ML+X forum on Representations for Learning.|