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About

Paul Schrater is a computational neuroscientist and computer vision researcher whose work spans human sensorimotor control, probabilistic perception, and intelligent surveillance systems. His most influential contributions lie at the intersection of Bayesian modeling and real-world action, exploring how humans and machines navigate uncertainty to make effective decisions. Schrater's most cited work on optimal camera placement (107 citations) established principled frameworks for automated surveillance, addressing how camera positioning fundamentally determines the quality of human activity recognition — a problem he tackled across multiple studies. This line of research provided both theoretical grounding and practical algorithms still referenced in computer vision and robotics communities. Equally compelling is his work on human motor control under uncertainty. His research on grasping objects amid environmentally induced positional uncertainty reveals how the brain compensates for noisy sensory and motor signals during purposive movement — contributing meaningfully to our understanding of biological control systems. His more recent work on Inverse Rational Control pushes further, developing frameworks to decode control principles from animal behavior in continuous, partially observable environments. Together, these contributions reflect a career devoted to understanding intelligent behavior — biological or artificial — as principled inference under uncertainty, making Schrater a distinctive voice bridging neuroscience, robotics, and machine learning.