Lawrence Carin
Professor of Statistical Science
Overview
Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989 he joined the Electrical Engineering Department at Polytechnic University (Brooklyn) as an Assistant Professor, and became an Associate Professor there in 1994. In September 1995 he joined the Electrical Engineering Department at Duke University, where he is now a Professor, and Vice Provost for Research. From 2003-2014 he held the William H. Younger Distinguished Professorship, and he was ECE Department Chair from 2011-2014. Dr Carin's early research was in the area of electromagentics and sensing, and over the last 15 years his research has moved to applied statistics and machine learning. He has recently served on the Program Committee for the following machine learning conferences: International Conf. on Machine Learning (ICML), Neural and Information Processing Systems (NIPS), Artificial Intelligence and Statistics (AISTATS), and Uncertainty in Artificial Intelligence (UAI). He was previously an Associate Editor (AE) of the IEEE Trans. on Antennas and Propagation, the IEEE Trans. on Signal Processing, and the SIAM J. of Imaging Science. He is currently an AE for the J. of Machine Learning Research. He is an IEEE Fellow.
Selected Grants
Machine Learning for Submesoscale Characterization, Ocean Prediction, and Exploration (ML-SCOPE) awarded by Massachusetts Institute of Technology (Principal Investigator). 2019 to 2024
From Deep Neural Networks to Kernel Machines: Advanced Data Science for Verification and Forensics awarded by Georgia Institute of Technology (Principal Investigator). 2019 to 2024
Novel Approaches to Infant Screening for ASD in Pediatric Primary Care awarded by National Institutes of Health (Co Investigator). 2019 to 2024
Clinical and Molecular Epidemiology of High Risk Coronary Plaque awarded by National Institutes of Health (Co Investigator). 2019 to 2023
Microsoft Investigator Fellowship awarded by (Principal Investigator). 2018 to 2023
Advancing Artificial Intelligence for the Naval Domain awarded by Office of Naval Research (Principal Investigator). 2018 to 2022
Medical Scientist Training Program awarded by National Institutes of Health (Mentor). 1997 to 2022
Adversarial Learning for Nonproliferation Applications awarded by (Principal Investigator). 2018 to 2021
RI: Small: Feature Encoding for Reinforcement Learning awarded by National Science Foundation (Co-Principal Investigator). 2018 to 2021
Mapping Epigenetic Memory of Exposure New To Observe (MEMENTO) awarded by Defense Advanced Research Projects Agency (Co Investigator). 2019 to 2021
Pages
Elliott Range, Danielle D., et al. “Application of a machine learning algorithm to predict malignancy in thyroid cytopathology.” Cancer Cytopathol, vol. 128, no. 4, Apr. 2020, pp. 287–95. Pubmed, doi:10.1002/cncy.22238. Full Text
Chapfuwa, P., et al. “Survival cluster analysis.” Acm Chil 2020 Proceedings of the 2020 Acm Conference on Health, Inference, and Learning, Feb. 2020, pp. 60–68. Scopus, doi:10.1145/3368555.3384465. Full Text
Shen, D., et al. “Improved semantic-aware network embedding with fine-grained word alignment.” Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Emnlp 2018, Jan. 2020, pp. 1829–38.
Engelhard, Matthew M., et al. “Identifying Smoking Environments From Images of Daily Life With Deep Learning.” Jama Netw Open, vol. 2, no. 8, Aug. 2019, p. e197939. Pubmed, doi:10.1001/jamanetworkopen.2019.7939. Full Text
Strawn, N., et al. “Erratum: Finite sample posterior concentration in high-dimensional regression (Information and Inference (2015) 3 (103-133) DOI: 10.1093/imaiai/iau003).” Information and Inference, vol. 4, no. 1, Jan. 2015. Scopus, doi:10.1093/imaiai/iau008. Full Text
Strawn, N., et al. “Finite sample posterior concentration in high-dimensional regression.” Information and Inference, vol. 3, no. 2, Jan. 2014, pp. 103–33. Scopus, doi:10.1093/imaiai/iau003. Full Text
Shen, D., et al. “Learning compressed sentence representations for on-device text processing.” Acl 2019 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2020, pp. 107–16.
Zhang, X., et al. “Syntax-infused variational autoencoder for text generation.” Acl 2019 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2020, pp. 2069–78.
Shen, D., et al. “Towards generating long and coherent text with multi-level latent variable models.” Acl 2019 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2020, pp. 2079–89.
Yang, Q., et al. “An end-to-end generative architecture for paraphrase generation.” Emnlp Ijcnlp 2019 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2020, pp. 3132–42.
Chen, L., et al. “Improving textual network embedding with global attention via optimal transport.” Acl 2019 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2020, pp. 5193–202.
Shen, D., et al. “Learning context-aware convolutional filters for text processing.” Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Emnlp 2018, 2020, pp. 1839–48.
Chen, L., et al. “Improving sequence-to-sequence learning via optimal transport.” 7th International Conference on Learning Representations, Iclr 2019, 2019.
Cong, Y., et al. “Go gradient for expectation-based objectives.” 7th International Conference on Learning Representations, Iclr 2019, 2019.
Chen, L., et al. “Improving sequence-to-sequence learning via optimal transport.” 7th International Conference on Learning Representations, Iclr 2019, 2019.
Pu, Y., et al. “Adaptive feature abstraction for translating video to language.” 5th International Conference on Learning Representations, Iclr 2017 Workshop Track Proceedings, 2019.