Maithilee Kunda

Maithilee Kunda is assistant professor of computer science and computer engineering at Vanderbilt University. Her work in artificial intelligence, in the area of cognitive systems, looks at how visual thinking contributes to learning and intelligent behavior, with a focus on applications for individuals on the autism spectrum. She currently directs Vanderbilt’s Laboratory for Artificial Intelligence and Visual Analogical Systems, and is a deputy director of the Vanderbilt Center for Autism and Innovation. She holds a B.S. in mathematics with computer science from MIT and a Ph.D. in computer science from Georgia Tech, and in 2016, was recognized as a visionary on the MIT Technology Review’s annual list of 35 Innovators Under 35.

Please see the AIVAS Lab website for news, research, and publications, as well as information about current lab openings.


Maithilee Kunda
mkunda [at] vanderbilt [dot] edu

PMB 351679
2301 Vanderbilt Place
Nashville, TN 37235-1679, USA

Phone: 615-875-8469
Fax: 615-343-5459

AIVAS Lab Website

Selected Publications

Ainooson, J., and Kunda, M. (2017). A computational model for reasoning about the Paper Folding task using visual mental images. In Proceedings of the 39th Annual Meeting of the Cognitive Science Society, London, UK.

Eliott, F. M., Stassun, K., and Kunda, M. (2017). Visual data exploration: How expert astronomers use flipbook-style visual approaches to understand new data. In Proceedings of the 39th Annual Meeting of the Cognitive Science Society, London, UK.

Kunda, M., El-Banani, M., and Rehg, J. (2016). A computational exploration of problem-solving strategies and gaze behaviors on the Block Design task. In Proceedings of the 38th Annual Meeting of the Cognitive Science Society, Philadelphia, PA.

Kunda, M., and Ting, J. (2016). Looking around the mind’s eye: Attention-based access to visual search templates in working memory. Advances in Cognitive Systems, 4, 113–129.

Kunda, M., McGreggor, K., and Goel, A. K. (2013). A computational model for solving problems from the Raven’s Progressive Matrices intelligence test using iconic visual representations. Cognitive Systems Research, 22-23, pp. 47-66.

Kunda, M., and Goel, A. K. (2011). Thinking in Pictures as a cognitive account of autism. Journal of Autism and Developmental Disorders, 41 (9), pp. 1157-1177.

A more complete list of publications can be found here.


Imagery-based Artificial Intelligence. Mathematical and computational techniques for imagery-based artificial intelligence (AI). Topics include imagery-based knowledge representations, imagery-based reasoning and problem solving approaches, and machine learning in imagery-based systems, as well as cognitive science findings related to human visual mental imagery in autism, education, and scientific discovery. (Previous names: Computational Mental Imagery. Previously taught: Fall 2016.)

Introduction to Machine Learning. Fundamentals of machine learning (ML), with a focus on supervised learning and reinforcement learning. Topics include decision trees, neural networks, instance-based learning, boosting, temporal difference learning, and also data privacy, human subjects research protections, and impacts of ML on society. (Catalog listing: Projects in AI. Previously taught: Spring 2017, Spring 2018.)

Computation and Cognition. Computational approaches to understanding human cognition, including research design and methods for integrating models with theory and observation. Topics include knowledge representation, concept formation, reasoning and search, analogy, mental imagery, and connectionism, as well as multidisciplinary perspectives on mind, brain, behavior, and society. (Previous names: Introduction to Cognitive Science. Previously taught: Summer 2013, Fall 2013, Summer 2015 (Georgia Tech), Spring 2016, Fall 2017.)

Resources on machine learning, and other areas of AI

People frequently ask me for recommendations on resources for learning about machine learning. I have two favorites:

1. Machine Learning by Charles Isbell and Michael Littman

This is a free online course offered through Udacity and Georgia Tech’s online masters program. I think watching these lectures (and doing all the quizzes!) gives a great conceptual introduction to machine learning. It is divided into three sections: supervised, unsupervised, and reinforcement, which gives a good coverage of the areas. The lectures are also quite entertaining (Isbell and Littman are practically a two-man comedy team).

2. Neural Networks and Deep Learning by Michael Nielsen

This free, online textbook is a very effective introduction to neural networks, especially (but not only) if you are starting from absolutely zero knowledge about them. If you do go through this book, I strongly encourage doing all of the exercises, including playing around with the Python code that comes with the book. I still refer back to Chapter 2 when I am trying to remind myself of the details of backpropagation.

Of course, there is more to AI than machine learning! For an insightful window into another area of AI, I recommend the following:

3. Knowledge-Based AI: Cognitive Systems by Ashok Goel and David Joyner

This is another Udacity class offered by Georgia Tech. (Ashok Goel was my PhD advisor, and David Joyner one of my PhD “siblings.”) The introduction alone gives an excellent birds-eye view of the big “conundrums” that drive AI research, and how different areas of AI attempt to frame and solve these conundrums in different ways. (This was also the course in which one of the TAs was the infamous Jill Watson….)