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Yi Lu Murphey

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Dr. Yi Lu Murphey is an Associate Dean for Graduate Education and Research, a Professor of the ECE(Electrical and Computer Engineering) department and the director of the Intelligent Systems Lab at the University of Michigan, Dearborn. She received a M.S. degree in computer science from Wayne State University, Detroit, Michigan, in 1983, and a Ph.D degree with a major in Computer Engineering and a minor in Control Engineering from the University of Michigan, Ann Arbor, Michigan, in 1989. Her current research interests are in the areas of machine learning, pattern recognition, computer vision and intelligent systems with applications to automated and connected vehicles, optimal vehicle power management, data analytics, and robotic vision systems. She has authored over 130 publications in refereed journals and conference proceedings. She is an editor for the Journal of Pattern Recognition, a senior life member of AAAI and a fellow of IEEE.

Jason Corso

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The Corso group’s main research thrust is high-level computer vision and its relationship to human language, robotics and data science. They primarily focus on problems in video understanding such as video segmentation, activity recognition, and video-to-text; methodology, models leveraging cross-model cues to learn structured embeddings from large-scale data sources as well as graphical models emphasizing structured prediction over large-scale data sources are their emphasis. From biomedicine to recreational video, imaging data is ubiquitous. Yet, imaging scientists and intelligence analysts are without an adequate language and set of tools to fully tap the information-rich image and video. His group works to provide such a language.  His long-term goal is a comprehensive and robust methodology of automatically mining, quantifying, and generalizing information in large sets of projective and volumetric images and video to facilitate intelligent computational and robotic agents that can natural interact with humans and within the natural world.

Relating visual content to natural language requires models at multiple scales and emphases; here we model low-level visual content, high-level ontological information, and these two are glued together with an adaptive graphical structure at the mid-level.

Relating visual content to natural language requires models at multiple scales and emphases; here we model low-level visual content, high-level ontological information, and these two are glued together with an adaptive graphical structure at the mid-level.

Matthew Johnson-Roberson

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Matthew Johnson-Roberson, PhD, is Assistant Professor of Naval Architecture and Marine Engineering and Assistant Professor of Electrical Engineering and Computer Science, College of Engineering, the University of Michigan, Ann Arbor.

The increasing economic and environmental pressures facing the planet require cost-effective technological solutions to monitor and predict the health of the earth. Increasing volumes of data and the geographic dispersion of researchers and data gathering sites has created new challenges for computer science. Remote collaboration and data abstraction offer the promise of aiding science for great social benefit. Prof. Johnson-Roberson’s research in this field has been focused on developing novel methods for the visualization and interpretation of massive environments from multiple sensing modalities and creating abstractions and reconstructions that allow natural scientists to predict and monitor the earth through remote collaboration. Through the promotion of these economically efficient solutions, his work aims to increase access to hundreds of scientific sites instantly without traveling. In undertaking this challenge he is constantly aiming to engage in research that will benefit society.

Traditional marine science surveys will capture large amounts of data regardless of the contents or the potential value of the data. In an exploratory context, scientists are typically interested in reviewing and mining data for unique geological or benthic features. This can be a difficult and time consuming task when confronted with thousands or tens of thousands of images. The technique shown here uses information theoretic methods to identify unusual images within large data sets.

Traditional marine science surveys will capture large amounts of data regardless of the contents or the potential value of the data. In an exploratory context, scientists are typically interested in reviewing and mining data for unique geological or benthic features. This can be a difficult and time consuming task when confronted with thousands or tens of thousands of images. The technique shown here uses information theoretic methods to identify unusual images within large data sets.