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

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

Advancing Artificial Intelligence for the Naval Domain awarded by Office of Naval Research (Principal Investigator). 2018 to 2022

Microsoft Investigator Fellowship awarded by (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

Mapping Epigenetic Memory of Exposure New To Observe (MEMENTO) awarded by (Co Investigator). 2019 to 2021

Multi-Source Activity Graph Latent Uncovering & Merging (MAGNUM) awarded by Lockheed Martin Corporation (Principal Investigator). 2017 to 2021

Pages

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. Pubmed, doi:10.1001/jamanetworkopen.2019.7939. 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.

Pu, Y., et al. “A generative model for deep convolutional learning.” 3rd International Conference on Learning Representations, Iclr 2015  Workshop Track Proceedings, 2015.