Professor of Statistical Science
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.
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
Postdoctoral Training in Genomic Medicine Research awarded by National Institutes of Health (Mentor). 2017 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
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
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.
Mehta, N., et al. “Stochastic Blockmodels meet Graph Neural Networks.” 36th International Conference on Machine Learning, Icml 2019, vol. 2019-June, 2019, pp. 7849–57.
Gan, Z., et al. “Learning generic sentence representations using convolutional neural networks.” Emnlp 2017 Conference on Empirical Methods in Natural Language Processing, Proceedings, 2017, pp. 2390–400.
Pu, Y., et al. “A generative model for deep convolutional learning.” 3rd International Conference on Learning Representations, Iclr 2015 Workshop Track Proceedings, 2015.
Vats, D., et al. “Test-size reduction for concept estimation.” Proceedings of the 6th International Conference on Educational Data Mining, Edm 2013, 2013.
Felsen, L. B., et al. “Short pulse electromagnetics for sensing applications.” Proceedings of Spie the International Society for Optical Engineering, vol. 1471, 1991, pp. 154–62. Scopus, doi:10.1117/12.44874.short. Full Text