Jennifer Sleeman

Affiliated Faculty, Alumnus, Faculty

jsleem1@umbc.edu 4104693522

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Dr. Jennifer Sleeman is a Research Assistant Professor in Computer Science at the University of Maryland, Baltimore County (UMBC). She defended her Ph.D. thesis, Dynamic Data Assimilation for Topic Modeling (DDATM) in 2017 under Tim Finin and Milton Halem. Her research interests include generative models, natural language processing, semantic representation, and deep learning. Her early work included entity disambiguation and coreference resolution, which led to fine-grained entity type identification, published in AI Magazine in 2015. Following her novel contribution of generative topic modeling for graphs, she used generative topic models to discover cross-domain influence and relatedness among scientific research papers. Her Ph.D. thesis adapted the theory of data assimilation, which is based on theoretical approaches for temporal integration of physical observations with dynamic simulation models, and applied it to a large, multi-sourced, scientific text collection. She performed a data assimilation of the Intergovernmental Panel for Climate Change (IPCC) reports across 30 years, using an initial model and integrating research documents to produce subsequent models over time. This methodology provides a new approach for multi-source data integration and trend prediction that uses an innovative method for filtering noise and accounting for missing model data. Her work was awarded a Microsoft "AI for Earth" resource grant in 2017 and 2018 and also won the best paper award in the Semantic Web for Social Good Workshop presented at International Semantic Web Conference in 2018. She is also an active research scientist in generative deep learning methods for which a patent is pending.