Education History
- Ph.D, Computer Science, University of Maryland Baltimore County
Work Experience
Senior Professional Staff, JHU Applied Physics Laboratory
Publications
Markowitz, Jared, Alexander New, Jennifer Sleeman, Chace Ashcraft, Jay Brett, Gary Collins, Stella In, and Nathaniel Winstead. “Discovering strategies for coastal resilience with AI-based prediction and optimization.” arXiv preprint arXiv:2509.19263 (2025).
Sleeman, Jennifer, Christoph Keller, Christopher Ribaudo, Victor Julio Leon, Reed Chen, Caroline Tang, Collin Kofroth et al. “Deep Learning Ensemble Emulation for NASA’s Goddard Earth Observing System (GEOS) Composition Forecast.” Artificial Intelligence for the Earth Systems 1, no. aop (2025).
Hamer, Sophia, Jennifer Sleeman, and Milton Halem. “Towards a Dynamic Data Driven AΙ Regional Weather Forecast Model.” In Dynamic Data Driven Applications Systems: 5th International Conference, DDDAS/Infosymbiotics for Reliable AI 2024, New Brunswick, NJ, USA, November 6–8, 2024, Proceedings, p. 126. Springer Nature, 2025.
Gnanadesikan, Anand, Gianluca Fabiani, Jingwen Liu, Renske Gelderloos, G. Jay Brett, Yannis Kevrekidis, Thomas Haine, Marie-Aude Pradal, Constaninos Siettos, and Jennifer Sleeman. “Tipping points in overturning circulation mediated by ocean mixing and the configuration and magnitude of the hydrological cycle: A simple model.” Journal of Physical Oceanography (2024).
River Chen, Reed, Christopher Ribaudo, Jennifer Sleeman, Chace Ashcraft, Collin Kofroth, Marisa Hughes, Ivanka Stajner, Kevin Viner, and Kai Wang. “Difference Learning for Air Quality Forecasting Transport Emulation.” arXiv e-prints (2024): arXiv-2402.
Keller, Mary Ruth, Christine Piatko, Mary Versa Clemens-Sewall, Rebecca Eager, Kevin Foster, Christopher Gifford, Derek Rollend, and Jennifer Sleeman. “Short-Term (7 Day) Beaufort Sea Ice Extent Forecasting with Deep Learning.” Artificial Intelligence for the Earth Systems 2, no. 4 (2023): e220070.
Sleeman, Jennifer, Christoph A. Keller, Christopher Ribaudo, David Chung, and Mimi Szeto. “Deep Learning Ensembles for Improved Atmospheric Composition Modeling.” In Proceedings of the AAAI Symposium Series, vol. 2, no. 1, pp. 148-152. 2023.
AAAI Fall Symposium – Knowledge Guided ML (Nov 2022) (Sleeman, Jennifer, David Chung, Chace Ashcraft, Jay Brett, Anand Gnanadesikan, Yannis Kevrekidis, Marisa Hughes et al. “Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points.” arXiv preprint arXiv:2302.06852 (2023).
Sleeman, Jennifer, David Chung, Anand Gnanadesikan, Jay Brett, Yannis Kevrekidis, Marisa Hughes, Thomas Haine et al. “A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN).” arXiv preprint arXiv:2302.10274 (2023).
“A Bidirectional Neuro-symbolic Methodology for Translating Between Generative Latent Representations and Natural Language Questions”, AAAI Spring Symposium – AI Climate Tipping-Point Discovery (March 2023)
Patel, Kinjal, Jennifer Sleeman, and Milton Halem. “Physics-aware deep edge detection network.” In Remote Sensing of Clouds and the Atmosphere XXVI, vol. 11859, pp. 32-38. SPIE, 2021.
Halem, Milton, Kinjal Patel, Zhifeng Yang, and Jennifer Sleeman. “The Use of Machine Learning to Infer the Transport Influence of US West Coast WildFires on the US East Coast Planetary Boundary Layer.” In AGU Fall Meeting 2021. 2021.
Keller, Mary, Christine Piatko, Mary Clemens-Sewall, Rebecca Eager, Kevin Foster, Christopher Gifford, Derek Rollend, and Jennifer Sleeman. “Short-Term Sea Ice Extent Forecasting with Deep Learning.” (2021).
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 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, March 2021.
Z. Ali, D. Ziaei, J. Sleeman, Z. Yang, and M. Halem, “LSTMs for Inferring Planetary Boundary Layer Height (PBLH)”, InProceedings, AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, March 2021.
S. Vallurupalli, J. Sleeman, and T. Finin, “Fine and Ultra-Fine Entity Type Embeddings for Question Answering”, InProceedings, International Semantic Web Conference, November 2020
J. Sleeman, Z. Yang, V. Caicedo, M. Halem, B. Demoz, R. Delgado, “A Deep Machine Learning Approach for LIDAR Based Boundary Layer Height Detection”, International Geoscience and Remote Sensing Symposium (IGARSS), September 2020.
J. Sleeman, T. Finin, and M. Halem, “Temporal Understanding of Cybersecurity Threats”, InProceedings, IEEE International Conference on Big Data Security on Cloud, May 2020.
J. Sleeman, M. Halem, and J. E. Dorband, A Hybrid Approach: Convolutional Autoencoders for Quantum Image Compression and RBMs for Generative Learning, Accepted Paper, SPIE Defense + Commercial Sensing, April 2020.
J. Sleeman, J. Dorband, and M. Halem. “A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning.” arXiv preprint arXiv:2001.11946, January 2020.
Sleeman, J., Caicedo, V., Halem, M., Demoz, B., and Delgado, R., 2019. Using Lidar and Machine Learning to Identify Planetary Boundary Layer Heights. AGUFM, 2019, pp.A51M-2721.
Caicedo, V., Delgado, R., Szykman, J., Cavender, K., Westfall, J., Ireland, B., Taylor, D., Welton, E.J., Sleeman, J., Halem, M. and Demoz, B., 2019. EPAMS Profiler and Ceilometer Network. AGUFM, 2019, pp.A33D-06.
J. Sleeman, M. Halem, and J. E. Dorband, “RBM Image Generation Using the D-Wave 2000Q”, Poster Presentation Presented at the 2019 Rising Stars in EECS Workshop, October 2019.
J. Sleeman, M. Halem, and J. E. Dorband, “RBM Image Generation Using the D-Wave 2000Q”, D-Wave Qubits North America Conference, September 2019.
J Sleeman, T Finin, M Halem “Ontology-Grounded Topic Modeling for Climate Science Research”, Emerging Topics in Semantic Technologies, ISWC 2018 Satellite Events, October 2018.
J Sleeman, M Halem, T Finin, M Cane, “Discovering Scientific Influence using Cross-Domain Dynamic Topic Modeling”, IEEE International Conference on Big Data, December 2017.
Jennifer Sleeman, “Dynamic Data Assimilation for Topic Modeling”, Ph.D. dissertation, University of Maryland Baltimore County, 2017.
J Sleeman, M Halem, T Finin, M Cane, “Modeling the Evolution of Climate Change Assessment Research Using Dynamic Topic Models and Cross-Domain Divergence Maps”, AAAI Spring Symposium on AI for Social Good, March 2017.
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”, American Geophysical Union Fall Meeting 2016, December 2016.
J Sleeman, M Halem, T Finin, M Cane., “Dynamic Topic Modeling to Infer the Influence of Research Citations on IPCC Assessment Reports”, Big Data Challenges, Research and Technologies in the Earth and Planetary Sciences Workshop, IEEE Int. Conf. on Big Data, December 2016.
Jennifer Sleeman, “Entity Disambiguation for Wild Big Data Using Multi-Level Clustering”, Doctoral Consortium, 14th International Semantic Web Conference, October 2015.
J Sleeman, T Finin, A Joshi, “Topic Modeling for RDF Graphs”, 3rd International Workshop on Linked Data for Information Extraction, 14th International Semantic Web Conference, October 2015.
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”, Language Resources and Evaluation, March 2016
J Sleeman, T Finin, “Taming Wild Big Data”, AAAI Fall Symposium on Natural Language Access to Big Data, November 2014
J Sleeman, T Finin, A Joshi, “Entity Type Recognition for Heterogeneous Semantic Graphs”, AI Magazine, September 2014
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”, Proceedings of the 8th International Workshop on Semantic Evaluation, August 2014
J. Sleeman and T. Finin, “Recognizing Entity Types in Heterogeneous Semantic Graphs”, AAAI 2013 Fall Symposium on Semantics for Big Data, November 2013
J. Sleeman and T. Finin, “Type Prediction for Efficient Coreference Resolution in Heterogeneous Semantic Graphs”, Proceedings of the Seventh IEEE International Conference on Semantic Computing, September 2013
Jennifer Sleeman, “Online unsupervised coreference resolution for semi-structured heterogeneous data”, Proceedings of the 11th International Semantic Web Conference, November 2012
J. Sleeman and T. Finin, “Cluster-based Instance Consolidation For Subsequent Matching”, First International Workshop on Knowledge Extraction and Consolidation from Social Media, November 2012
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
K. Krishnaswamy, J. Sleeman, and T. Oates, “Real-Time Path Planning for a Robotic Arm”, Proceedings of the 4th International Conference on Pervasive Technologies Related to Assistive Environments, May 2011
J. Sleeman and T. Finin, “Learning Co-reference Relations for FOAF Instances”, Proceedings of the Poster and Demonstration Session at the 9th International Semantic Web Conference, November 2010
J. Sleeman and T. Finin, “Computing FOAF Co-reference Relations with Rules and Machine Learning”, Proceedings of the Third International Workshop on Social Data on the Web, November 2010
J. Sleeman and T. Finin, “A Machine Learning Approach to Linking FOAF Instances”, Proceedings of the AAAI Spring Symposium on Linked Data Meets Artificial Intelligence, January 2010
Honors and Awards
- Rising Star in EECS 2019 (https://publish.illinois.edu/rising-stars/) (2019)
Professional Organizations
Association for Advancement of Artificial Intelligence