Eero Simoncelli is an outstanding computational neuroscientist who is working to understand how sensory systems arrive at reliable interpretations of the world, allowing us to make predictions and perform difficult tasks with surprising accuracy. His work specifically aims to answer several key questions in this area: How do populations of neurons encode sensory information, and how do subsequent populations extract that information for recognition, decisions, and action? And from a more theoretical perspective, why do sensory systems use these particular representations, and how can we use these principles to design better man-made systems for processing sensory signals? Dr. Simoncelli uses a combination of computational theory and modeling, coupled with perceptual and physiological experiments, and has provided crucial insights that address these key questions in neuroscience.
One area of study by his group is the optimal encoding of visual information. Since the mid 1990's his group has developed successively more powerful models describing the statistical properties of local regions of natural images, using them in parallel to understand the structure and function of both visual and auditory neurons, and more recently pioneering the development of new forms of signal-adaptive representations. He has also contributed to the experimental characterization of neural and perceptual responses, and developed key models for understanding neuronal cell activities and neuronal mechanisms. Finally, he has worked on problems in optimal decoding and its relationship to human visual perception.
His work has been widely recognized by the scientific community. He has held the prestigious position of Howard Hughes Medical Institute (HHMI) Investigator since 2000. He has been awarded an NSF CAREER Award (1996-2000), a Sloan Research Fellowship (1998-2000), and was named a Hilgard Visiting Scholar at Stanford in 2013. In 2008, he was elected a Fellow of the Institute of Electrical and Electronic Engineers (IEEE). He serves as associate editor of the Annual Review of Vision Science, and is on the editorial board of the Journal of Vision. In the past, he has served as an associate editor IEEE Transactions on Image Processing and as a member of the Faculty of 1000 Theoretical Neuroscience section. In 2015, he was awarded an Engineering Emmy Award from the Television Academy, for his work on computational modeling of perceived visual quality of images.
Although his teaching has primarily focused on graduate-level education, he did teach the Undergraduate Tutorial Research course, and has mentored a number of undergraduates doing research projects in his group.