Jennifer Sleeman
Jennifer Sleeman

Affiliated Faculty, Alumnus, Faculty

University of Maryland, Baltimore County
Computer Science and Electrical Engineering, 1000 Hilltop CIrcle, Baltimore, MD 21250, United States

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.

Jennifer Sleeman

Publications

2022

  1. M. Halem, a. kochanski, J. Mandel, P. Nguyen, R. Atlas, Z. Yang, A. Bargteil, J. Sleeman, V. Caicedo, B. Demoz, D. Chapman, and K. Patel, "A Machine Learning Plume-Resolving Model Implementation over North America for Mega-Wildland Fire Smoke Impacts on Distant Planetary Boundary Layers", InProceedings, 102nd American Meteorological Society Annual Meeting, January 2022.

2021

  1. M. Halem, K. Patel, Z. Yang, and J. Sleeman, "The Use of Machine Learning to Infer the Transport Influence of US West Coast WildFires on the US East Coast Planetary Boundary Layer", InProceedings, AGU Fall Meeting 2021, December 2021.
  2. J. Sleeman, I. Stajner, C. Keller, M. Halem, C. Hamer, R. Montuoro, B. Baker, and e., "The Integration of Artificial Intelligence for Improved Operational Air Quality Forecasting", InProceedings, AGU Fall Meeting 2021, December 2021.
  3. K. Patel, J. Sleeman, and M. Halem, "Physics-aware deep edge detection network", InProceedings, Proceedings Volume 11859, Remote Sensing of Clouds and the Atmosphere XXVI; SPIE Remote Sensing, September 2021, 270 downloads.
  4. J. Sleeman, T. Finin, and M. Halem, "Understanding Cybersecurity Threat Trends through Dynamic Topic Modeling", Article, Frontiers in Big Data, June 2021, 219 downloads.
  5. D. Ziaei, J. Sleeman, M. Halem, V. Caicedo, R. M. Delgado, and B. Demoz, "Convolutional LSTM for Planetary Boundary Layer Height (PBLH) Prediction", InProceedings, AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, March 2021, 347 downloads.
  6. Z. Ali, D. Ziaei, J. Sleeman, Z. Yang, and M. Halem, "LSTMs for Inferring Planetary Boundary Layer Height (PBLH)", InProceedings, AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, March 2021, 391 downloads.

2020

  1. S. Vallurupalli, J. Sleeman, and T. Finin, "Fine and Ultra-Fine Entity Type Embeddings for Question Answering", InProceedings, International Semantic Web Conference, November 2020, 360 downloads.
  2. J. Sleeman, M. Halem, V. Caicedo, B. Demoz, and R. M. Delgado, "A Deep Machine Learning Approach for LIDAR Based Boundary Layer Height Detection", InProceedings, IEEE International Geoscience and Remote Sensing Symposium, October 2020.
  3. J. Sleeman, T. Finin, and M. Halem, "Temporal Understanding of Cybersecurity Threats", InProceedings, IEEE International Conference on Big Data Security on Cloud, May 2020, 795 downloads.
  4. J. Sleeman, J. E. Dorband, and M. Halem, "A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning", InProceedings, Quantum Information Science, Sensing, and Computation XII, May 2020, 365 downloads.
  5. J. Sleeman, J. E. Dorband, and M. Halem, "A Hybrid Quantum Enabled RBM Advantage: Convolutional Autoencoders for Quantum Image Compression and Generative Learning", Article, arXiv preprint arXiv:2001.11946, January 2020, 570 downloads.

2019

  1. J. Sleeman, V. Caicedo, M. Halem, and B. Demoz, "Using Lidar and Machine Learning to Identify Planetary Boundary Layer Heights", InProceedings, American Geophysical Union Fall Meeting Abstracts, December 2019.
  2. J. Sleeman, M. Halem, and J. E. Dorband, "RBM Image Generation Using the D-Wave 2000Q", Misc, Poster Presentation Presented at the 2019 Rising Stars in EECS Workshop, October 2019, 407 downloads.
  3. J. Sleeman, M. Halem, and J. E. Dorband, "RBM Image Generation Using the D-Wave 2000Q", Misc, D-Wave Qubits North America Conference, September 2019, 487 downloads.

2018

  1. J. Sleeman, "Variational Autoencoders using D-Wave Quantum Annealing", InProceedings, American Geophysical Union Fall Meeting Abstracts, December 2018.
  2. J. Sleeman, T. Finin, and M. Halem, "Ontology-Grounded Topic Modeling for Climate Science Research", InCollection, Emerging Topics in Semantic Technologies. ISWC 2018 Satellite Events, October 2018, 870 downloads.

2017

  1. J. Sleeman, T. Finin, M. Cane, and M. Halem, "Discovering Scientific Influence using Cross-Domain Dynamic Topic Modeling", InProceedings, IEEE International Conference on Big Data, December 2017, 910 downloads.
  2. J. Sleeman, "Dynamic Data Assimilation for Topic Modeling (DDATM)", PhdThesis, University of Maryland, Baltimore County, July 2017.
  3. J. Sleeman, M. Halem, T. Finin, and M. Cane, "Modeling the Evolution of Climate Change Assessment Research Using Dynamic Topic Models and Cross-Domain Divergence Maps", InProceedings, AAAI Spring Symposium on AI for Social Good, March 2017, 1837 downloads.

2016

  1. J. Sleeman, M. Halem, T. Finin, and M. Cane, "Advanced Large Scale Cross Domain Temporal Topic Modeling Algorithms to Infer the Influence of Recent Research on IPCC Assessment Reports", InProceedings, American Geophysical Union Fall Meeting 2016, December 2016, 1537 downloads.
  2. J. Sleeman, M. Halem, T. Finin, and M. Cane, "Dynamic Topic Modeling to Infer the Influence of Research Citations on IPCC Assessment Reports", InProceedings, Big Data Challenges, Research, and Technologies in the Earth and Planetary Sciences Workshop, IEEE Int. Conf. on Big Data, December 2016, 1269 downloads.
  3. A. L. Kashyap, L. Han, R. Yus, J. Sleeman, T. W. Satyapanich, S. R. Gandhi, and T. Finin, "Robust Semantic Text Similarity Using LSA, Machine Learning and Linguistic Resources", Article, Language Resources and Evaluation, March 2016, 2129 downloads.

2015

  1. J. Sleeman, "Entity Disambiguation for Wild Big Data Using Multi-Level Clustering", InProceedings, Doctoral Consortium, 14th International Semantic Web Conference, October 2015, 923 downloads.
  2. J. Sleeman, T. Finin, and A. Joshi, "Topic Modeling for RDF Graphs", InProceedings, 3rd International Workshop on Linked Data for Information Extraction, 14th International Semantic Web Conference, October 2015, 1560 downloads.
  3. J. Sleeman, T. Finin, and A. Joshi, "Entity Type Recognition for Heterogeneous Semantic Graphs", Article, AI Magazine, March 2015, 1548 downloads.

2014

  1. J. Sleeman and T. Finin, "Taming Wild Big Data", InProceedings, AAAI Fall Symposium on Natural Language Access to Big Data, November 2014, 1279 downloads.
  2. A. L. Kashyap, L. Han, R. Yus, J. Sleeman, T. W. Satyapanich, S. R. Gandhi, and T. Finin, "Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity systems", InProceedings, Proceedings of the 8th International Workshop on Semantic Evaluation, August 2014, 3861 downloads.

2013

  1. J. Sleeman and T. Finin, "Recognizing Entity Types in Heterogeneous Semantic Graphs", InProceedings, AAAI 2013 Fall Symposium on Semantics for Big Data, November 2013, 1561 downloads.
  2. J. Sleeman and T. Finin, "Type Prediction for Efficient Coreference Resolution in Heterogeneous Semantic Graphs", InProceedings, Proceedings of the Seventh IEEE International Conference on Semantic Computing, September 2013, 1291 downloads.

2012

  1. J. Sleeman, "Online unsupervised coreference resolution for semi-structured heterogeneous data", InProceedings, Proceedings of the 11th International Semantic Web Conference, November 2012, 1002 downloads.
  2. J. Sleeman and T. Finin, "Cluster-based Instance Consolidation For Subsequent Matching", InProceedings, First International Workshop on Knowledge Extraction and Consolidation from Social Media, November 2012, 1324 downloads.
  3. J. Sleeman, R. Alonso, H. Li, A. Pope, and A. Badia, "Opaque Attribute Alignment", InProceedings, Proceedings of the 3rd International Workshop on Data Engineering Meets the Semantic Web, April 2012, 1244 downloads.

2011

  1. K. Krishnaswamy, J. Sleeman, and T. Oates, "Real-Time Path Planning for a Robotic Arm", InProceedings, Proceedings of the 4th International Conference on Pervasive Technologies Related to Assistive Environments , May 2011, 1816 downloads.

2010

  1. J. Sleeman and T. Finin, "Learning Co-reference Relations for FOAF Instances", InProceedings, Proceedings of the Poster and Demonstration Session at the 9th International Semantic Web Conference, November 2010, 2796 downloads, 2 citations.
  2. J. Sleeman and T. Finin, "Computing FOAF Co-reference Relations with Rules and Machine Learning", InProceedings, Proceedings of the Third International Workshop on Social Data on the Web at ISWC2010, November 2010, 4045 downloads, 30 citations.
  3. J. Sleeman and T. Finin, "A Machine Learning Approach to Linking FOAF Instances", InProceedings, Proceedings of the AAAI Spring Symposium on Linked Data Meets Artificial Intelligence, January 2010, 2614 downloads, 5 citations.

Past Projects

  1. Modelling the evolution of climate change research, Principal Investigator