Lawrence Carin

Lawrence Carin

Professor of Statistical Science

External address: 
119 Allen Building, Durham, NC 27708
Internal office address: 
Box 90291, Durham, NC 27708-0291
Phone: 
(919) 681-6436

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.

Education & Training

  • Ph.D., University of Maryland, College Park 1989

  • M.Sc.Eng., University of Maryland, College Park 1986

  • B.S.E., University of Maryland, College Park 1985

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.

Pages