Achieving Low-Delay and Fast-Convergence in Data-Intensive Mobile Computing
Abstract:
Abstract:
Widely used systems such as operating systems and web servers are implemented in unsafe programming languages for efficiency, and system designers often prioritize performance over security. Hence, these foundational systems inherently suffer from a variety of vulnerabilities and insecure designs that have been exploited by adversaries to launch critical system attacks. Two typical goals of these attacks are to leak sensitive data and to control victim systems.
Dr. Bao will present two projects that employ data analytics on neuroscience and Natural Language, respectively. On one hand, certain neuropsychiatric disorders, such as Alzheimer's disease or depression, are associated with morphological changes on human brain cortex. A key to effective diagnosis and treatment is to detect such changes that are too minor to be seen by doctors from MRI images directly. Dr. Bao's research in MRI image processing focuses on extracting anatomical landmarks on cortical surface.
Abstract:
Computer vision systems seek to recover properties of the physical world from measurements of reflected light. To do so, they must solve ill-posed estimation problems by leveraging the statistical structure present in natural scenes. In the first part of the talk, I will introduce a new inference framework that can efficiently reason with different notions of spatial structure, at different scales and over different regions across the visual field. This allows the accurate recovery of continuous-valued maps of scene properties---depth, surface orientation, reflectance, motion, etc.---from image data. Specifically, I will describe a method that uses this framework to estimate scene depth from a single image, by training a neural network to produce dense probabilistic estimates of different elements of local geometric structure, and harmonizing these estimates to produce consistent depth maps.