Research

We seek to uncover the computational principles that give rise to intelligence, both in humans and in machines. Our research bridges cognitive science and artificial intelligence to understand how minds learn, decide, and adapt in complex, realistic environments. By modeling human behavior mathematically and comparing it with modern AI systems, we aim to distill the core algorithms of cognition. These insights not only deepen our understanding of human thought but also inspire new ways to make artificial agents more robust, efficient, and aligned with human values. Our work centers on three main areas:

Understanding and assisting human behavior

One of our main research areas is computational cognitive science. We study how people think, learn, and decide using methods from psychology, data science, machine learning, and neuroscience. Our goal is to formally understand the building blocks of human intelligence. Here, we focus primarily on how humans solve complex tasks, e.g., in video games or naturalistic experimental scenarios (e.g., ordering food or making other health-related decisions), in an attempt to distill the principles of intelligence into formal algorithms that can help us understand human behavior.

Understanding and improving artificial agents

With the advent of powerful artificial agents such as Large Language Models comes the urgency to understand how they learn and make decisions. These foundation models are not only incredibly large, but they are also extremely opaque. We, therefore, study these algorithms using tools from cognitive science in the hope to understand better how they behave. Moreover, we use insights from cognitive science and neuroscience to improve current deep learning algorithms in general, for example, by making them more sample-efficient.

Computational mental health

In the third area of research, we use computational models to understand and improve people’s mental health. The nascent field of “computational psychiatry” considers the interactions between individuals and evolutionary, developmental, and current environments that collectively define mental illness. It then attempts to use these to provide insights into the nosology, prognoses, and possible cures for some of the most common mental disorders.