Maithilee Kunda

Reader in Computational Cognitive Science, School of Informatics, University of Edinburgh

[first initial][lastname]@ed.ac.uk

For more on specific projects, publications, datasets, and of course the amazing students who make all of this research possible, please visit the AIVAS Lab website. (Substantially out of date... new version coming soon!)

For various ramblings, on AI, cognitive science, technology, education, and society, check out my new blog: https://www.mknotes.com

Bio

Maithilee Kunda is a reader in computational cognitive science in the School of Informatics at the University of Edinburgh. Her work in artificial intelligence (AI), in the area of cognitive systems, looks at how visual thinking contributes to learning and intelligent behavior, with a focus on topics related to autism and neurodiversity. More broadly, her research examines connections between knowledge representations and advanced reasoning abilities in humans, animals, and machines, making use of interdisciplinary methods such as computational modeling and human studies. She directs the Laboratory for Artificial Intelligence and Visual Analogical Systems and is a member of the University of Edinburghs Institute for Language, Cognition, and Computation. Previously, she worked at Vanderbilt University as assistant and then associate professor of computer science, where her research also helped launch Vanderbilts Frist Center for Autism and Innovation. In 2016, she was recognized as a visionary on the MIT Technology Reviews global list of 35 Innovators Under 35 for her work at the intersection of autism, AI, and visual thinking. In 2025, she received the U.S. Presidential Early Career Award for Scientists and Engineers (PECASE). She holds a B.S. in mathematics with computer science from MIT and a Ph.D. in computer science from Georgia Tech.

Recent Highlights

Full list of research papers here.

PECASE award, 2025. I was honored to be among the recipients of the U.S. Presidential Early Career Award for Scientists and Engineers (PECASE). The PECASE is the highest honor bestowed by the U.S. government on outstanding scientists and engineers early in their careers, and recognizes innovative and far-reaching developments in science and technology. January 2025.

Best Paper award, ICDL 2024. Michelson, J., Sanyal, D., Ainooson, J., Farhana, E., and Kunda, M. (2024). Standoff: Benchmarking representation learning for nonverbal theory of mind tasks. IEEE International Conference on Development and Learning (ICDL). One of two papers selected for best paper awards.

4th place in ARCathon, 2022. 4th place tie in 2022 global ARCathon competition, led by PhD student James Ainooson. The ARCathon is a recent AI benchmark challenge for solving the Abstraction & Reasoning Corpus (ARC) test of machine intelligence.

New York Times, 2022. Our work featured in the New York Times in an article on AI and special education. How Robots Can Assist Students With Disabilities, by Alina Tugend. "New tools use artificial intelligence to assist students with autism and dyslexia and address accessibility for those who are blind or deaf."

CogSci 2021, 22% acceptance rate. *Dunn, A., *Qiao, A., *Johnson, M., & Kunda, M. (2021). Measuring more to learn more from the block design test: A literature review. Proceedings of the 43rd Annual Meeting of the Cognitive Science Society. p. 611-617. *Co-first authors. [pdf]

JADD 2021. Rashedi, R., Bonnet, K., Schulte, R., Schlundt, D., Swanson, A., Kinsman, A., Bardett, N., Warren, Z., Juarez, P., Biswas, G., & Kunda, M. (2021). Opportunities and challenges in developing technology-based social skills interventions for adolescents with autism spectrum disorder: A qualitative analysis of parent perspectives. Journal of Autism and Developmental Disorders. [pdf]

PNAS 2020. Kunda, M. (2020). AI, visual imagery, and a case study on the challenges posed by human intelligence tests. Proceedings of the National Academy of Sciences, 117 (47), 29390-29397. [pdf]

Best Paper award, ACS 2020. Yang, Y., McGreggor, K., and Kunda, M. (2020). Not quite any way you slice it: How different analogical constructions affect Raven's Matrices performance. Eighth Annual Conference on Advances in Cognitive Systems (ACS). Winner of the inaugural ACS Patrick Henry Winston Award for Best Student Paper. [pdf]

NSF Idea Machine, 2019. Finalist for NSF 2026 Idea Machine, as part of Vanderbilt team led by Keivan Stassun. "Harnessing the Human Diversity of Mind" selected as 1 of top 33 finalists in national competition.

Keynote speaker, ICCM 2019. 17th International Conference on Computational Modeling (ICCM), Montreal, Canada.

60 Minutes

Video ś "Recruiting for talent on the autism spectrum" on CBS 60 Minutes with Anderson Cooper (Vanderbilt portion begins at 7:40).

Video ś A 90-second snippet from our portion of this 60 Minutes piece.

Research behind the demo featured on 60 Minutes, including a more in-depth look at the data from our volunteers Dan Burger and Anderson Cooper. (Under construction.)

Temple Grandin visits our AI class!

I was honored to help organize Dr. Temple Grandin's visit to Vanderbilt and arrange this visit to our class. In fact, it was reading her autobiography during graduate school that kick-started my interests in visual thinking; this class exists as a direct result of her writings!

Video ś Short video highlighting this classroom visit

Video ś Dr. Grandin's Chancellor's Lecture

Grandin rejects low expectations, insists workforce critically needs people with autism in VU lecture, Vanderbilt News, Nov. 30, 2018.

Orangutans

One of the more fun projects my lab has done is to build a cognitive enrichment app for the orangutans at the Atlanta Zoo.

See orangutans using our cognitive enrichment app!

Video ś Dumadi uses the musical instruments "game"---apparently his favorite part of the app.

Video ś Madu is surprised (and touched, according to the zookeepers who knew her) to see a video of her late orangutan friend Alan in the "Zoo-Videos-Youtube" part of the app.

Video ś App Inspiration leads Vanderbilt student to code for orangutans

Find Your Impact: Student creates app for orangutans. Vanderbilt Research News. Feb. 22, 2019.

Scheer, B., Renteria, F. C., and Kunda, M. (2019). Technology-based cognitive enrichment for animals in zoos: A case study and lessons learned. In Proceedings of the 41st Annual Meeting of the Cognitive Science Society, p. 2741-2747. [pdf]

In the media

How robots can assist students with disabilities, by Alina Tugend. New York Times. March 29, 2022.

Finding strengths in autism, by Rachel Nuwer. Spectrum News. May 12, 2021.

Film Detective: How an AI-powered game aims to improve outcomes for students with ASD, by Bennett Lunn. Inside IES Research, US Institute of Education Sciences (IES). Nov 3, 2021.

Perspective matters: How diversity of background, expertise, and cognition can lead to good science, by Bennett Lunn. Inside IES Research, US Institute of Education Sciences (IES). Aug 17, 2021.

Leading the Vanguard interview, by Elizabeth Turner. Vanderbilt Kennedy Center Notables. Mar. 5, 2020.

Film Detective helps kids with autism interpret actors actions, by Brenda Ellis. Vanderbilt News. Jan. 27, 2020.

Harnessing the human diversity of mind, Vanderbilt team selected as one of top 33 finalists in NSF's 2026 Idea Machine competition. May 31, 2019.

AAAS explores what artificial intelligence teaches us about ourselves, by Michaela Jarvis. American Association for the Advancement of Science (AAAS). Dec. 5, 2017.

Using AI to understand autism, Top Of Mind podcast with Julie Rose. Nov. 1, 2017.

Visual thinking, autism and artificial intelligence, Assistive Technology Update podcast with Wade Wingler. Oct. 6, 2017.

AI that thinks in pictures, The Women in Tech Show podcast with Edaena Salinas. Nov. 8, 2016.

Teaching

I love teaching. I have taught AI and cognitive science courses at many levels, from undergraduate through advanced PhD courses. I began my teaching career in the School of Interactive Computing at Georgia Tech, then in the Department of Computer Science at Vanderbilt University, and now in the School of Informatics at the University of Edinburgh.

In my teaching, I prioritize in-person learning and engagement. My lectures tend to be very interactive, with a good smattering of small group exercises, mini experiments, and other in-class activities. As the world changes, and access to information is no longer tied to physical locations, I still firmly believe that there is irreplaceable value in having a group of like-minded people get together to learn about something in the same room, so students can learn together and learn from each other. (Plus, it's fun!)

Courses I have taught:

Computation and Cognition (Vanderbilt University), aka Introduction to Cognitive Science (Georgia Tech)
-- 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.

Imagery-based Artificial Intelligence (Vanderbilt University)
-- 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.

Artificial Intelligence (Vanderbilt University)
-- Principles and programming techniques of artificial intelligence. Strategies for searching, representation of knowledge and automatic deduction, learning, and adaptive systems. Survey of applications.

Advanced Artificial Intelligence (Vanderbilt University)
-- Discussion of state-of-the-art and current research issues in heuristic search, knowledge representation, deduction, and reasoning. Related application areas include: planning systems, qualitative reasoning, cognitive models of human memory, user modeling in ICAI, reasoning with uncertainty, knowledge-based system design, and language comprehension.

Introduction to Machine Learning (Vanderbilt University)
-- 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.

Learning about AI

Under construction... old version here:

One of the most common questions I get from students is, "I want to learn about AI. Where should I start?" Here are some resources that are good for beginners:

1. Melanie Mitchells Artificial Intelligence: A Guide for Thinking Humans

This very recent book gives a non-technical but content-rich overview of the broad field of AI, where it has been and where it is going, and descriptions of major approaches.

2. Neural Networks and Deep Learning by Michael Nielsen

This free, online textbook provides a very concise, effective, and easy-to-read introduction to neural networks, especially (but not only) if you are starting from scratch. Particularly valuable are the interactive exercises presented alongside the reading material, including Python code that you can download and mess around with.

3. Tom Mitchells Machine Learning textbook

This book gives an excellent and digestible overview of major approaches and also theoretical angles. (Do not be put off by the 1997 publication date! The fundamentals abide.)

Of course, there is a LOT more to AI than machine learning. (If you are at all surprised by this statement, then you should pay particular attention to this next section!) For an insightful window into other areas of AI, I recommend the following:

4. Knowledge-Based AI: Cognitive Systems

This freely-available online course is taught by Ashok Goel (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. (Trivia: This was also the course in which one of the TAs was the infamous Jill Watson....)

5. Mind Design II, edited by John Haugeland

Reading this book was one of the most influential intellectual journeys I ever took. Starting with Turing's classic paper on "Computing Machinery and Intelligence," going through key ideas from thinkers like Newell & Simon, Dennett, Rumelhart, and Brooks. (You can also find many of the individual papers from this collection online, in their original published form.)