Daniel Leeds

Dr. Daniel D. Leeds is an assistant professor of Computer and Information Science at Fordham University. He received his PhD in the Program in Neural Computation at Carnegie Mellon University, and received his SB and MEng degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. His research focuses on models of biological perception, developing techniques in machine learning, signal processing, and computer vision. He is a recipient of RK Mellon Foundation's Presidential Fellowship in Life Sciences as well as the National Science Foundation's IGERT Fellowship.

My research studies the computational principles underlying visual perception. I pursue connections between the statistical patterns of the natural world of sights and the resulting representations in the minds of human and animal observers. My work draws on theories in computer vision, machine learning, psychology, biology, and statistics, among other areas. I also dedicate significant attention to the development and application of data analysis techniques to gain better understandings of neural (and other biological) data.

My current projects include:

• Computer vision models of object representations in the brain
• Realtime fMRI search for visual properties encoded in the brain
• Analysis of semantic representations of objects in the brain

Daniel D. Leeds, Darren A. Seibert, John A. Pyles, and Michael J. Tarr (2013). "Comparing visual representations across human fMRI and computational vision", Journal of Vision, 13(13)

Daniel D. Leeds, Darren A. Seibert, John A. Pyles, and Michael J. Tarr (2011). "Unraveling the visual and semantic components of object representation", Vision Sciences Society, Naples, Florida, USA, May 11 - 16.