This data management infrastructure implements the principle of Findable, Accessible, Interoperable and Reusable (FAIR) by standardizing the data management pipeline and spatiotemporally co-locating the heterogeneous datasets from across disciplines and formats. He is responsible for the design and implementation of the Scientific Data Management Infrastructure for the Institute of Agriculture and Natural Resources (IANR) of UNL. He has published 11 papers in relevant conferences and journals. To reach this goal, research is conducted on data indexing and partitioning strategies and high performance computing frameworks such as MPI and CUDA is also utilized. Now collaborating with domain scientists from Biological System Engineer and Meteorology, he helps the researchers from interdisciplinary domains to have a deeper understanding of their datasets by transforming raw data to a more meaningful representation with deep learning based methods, and by visualizing the meaningful representation with various front-end technologies. His research interest focuses on Graph Learning and Manifold Learning, trying to gain key insight and meaningful representation from datasets defined on a graph or Euclidean space.
Activity View my verified achievement from Amazon Web Services (AWS). He obtained a BS in Computer Science and Technology from University of Electronic Science and Technology of China (UESTC), an MS in Information Management from Illinois Institute of Technology (IIT) and a PhD in Computer Science from University of Nebraska-Lincoln (UNL). 500+ connections Join to connect EY Columbia University About Actively seeking full-time positions in data science/statistics. Yu Pan (潘宇) is a research assistant professor in the Department of Biological System Engineering (BSE) at the University of Nebraska-Lincoln.
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