Assistant Professor eff. 1/1/2019
Modern computers perform calculations at speeds that dramatically exceed human performance, and they excel at highly restricted tasks, like strategizing in chess or solving mathematical equations. But even the most advanced AI systems cannot yet match the flexible cognitive abilities of the human brain. The goal of my work is to understand how fundamental cognitive functions, like natural perception, semantic understanding, and commonsense reasoning, are implemented in neural computation. Specifically, my research seeks to reverse engineer the representations and algorithms of human cognition using methods and theories from neuroscience, cognitive psychology, and computer science (e.g., fMRI, neural network models, computer vision, NLP, statistical modeling). This work leverages the overlapping strengths of neuroscience and AI to address problems in vision, memory, semantic understanding, and navigation. Some of the questions addressed by this work include:
- How do we make sense of natural visual scenes?
- How are scenes composed from objects and surfaces?
- How are the statistical properties of natural scenes utilized in neural computation?
- How do we plan navigational behaviors?
- How does high-level vision interact with semantic memory?
- How can we use statistical modeling to understand the information encoded in high-dimensional neural signals?
Before joining the Cognitive Science Department at Johns Hopkins, I worked with Russell Epstein as a postdoctoral fellow in the Department of Psychology at the University of Pennsylvania. I completed my PhD in Neuroscience from the University of Pennsylvania, where I was advised by Murray Grossman.
Michael Bonner will join our faculty as an assistant professor in January 2019.
View Michael Bonner's profile on Google Scholar for a complete publications list.
The Bonner lab will be recruiting graduate students to start in the Fall of 2019. Our lab combines methods from cognitive and computational neuroscience, computer vision, and natural language processing to understand how fundamental aspects of human cognition are implemented in the computational circuity of the brain. Current work in the lab is focused on questions in high-level visual perception and semantic cognition. For example, how do we make sense of complex, natural scenes? What computational processes allow for the remarkable flexibility and speed of human vision? How do semantic knowledge and common-sense reasoning contribute to natural perception? Our goal is to reverse engineer the algorithms that the brain uses to solve these problems. We are seeking graduate students who are interested in developing research projects that address central questions at the intersection of neuroscience, cognitive science, and artificial intelligence. We also welcome graduate students who may be interested in developing collaborations or co-mentorships with other labs at JHU. Applicants should have a strong interest in scientific programming and quantitative modeling. You can find out more about our work by visiting our lab website. If you are considering applying, feel free to send us an email to discuss potential projects and opportunities in the lab.