Lawrence Carin

Carin

James L. Meriam Distinguished Professor of Electrical and Computer Engineering

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 and Computer Engineering (ECE) Department at Duke University, where he is now a Professor. He was ECE Department Chair from 2011-2014, the Vice Provost for Research from 2014-2019, and since 2019 he has served as Duke's Vice President for Research. From 2003-2014 he held the William H. Younger Distinguished Professorship, and since 2018 he has held the James L. Meriam Distinguished Professorship. Dr. Carin's research focuses on machine learning (ML), artificial intelligence (AI) and applied statistics. He publishes widely in the main ML/AI conferences, and he has also engaged in translation of research to practice. He was co-founder of the small business Signal Innovations Group, which was acquired by BAE Systems in 2014, and in 2017 he co-founded the company Infinia ML. He is an IEEE Fellow.

Appointments and Affiliations

  • James L. Meriam Distinguished Professor of Electrical and Computer Engineering
  • Professor of Electrical and Computer Engineering
  • Professor of Computer Science
  • Member of the Duke Clinical Research Institute

Contact Information

  • Office Location: 119 Allen Building, Durham, NC 27708
  • Office Phone: (919) 681-6436
  • Email Address: lcarin@ee.duke.edu
  • Websites:

Education

  • 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

Research Interests

Applied statistics and machine learning

Awards, Honors, and Distinctions

  • Fellows. Institute for Electrical and Electronics Engineers. 2001

Courses Taught

  • BME 493: Projects in Biomedical Engineering (GE)
  • CEE 780: Internship
  • COMPSCI 391: Independent Study
  • COMPSCI 393: Research Independent Study
  • COMPSCI 394: Research Independent Study
  • ECE 291: Projects in Electrical and Computer Engineering
  • ECE 392: Projects in Electrical and Computer Engineering
  • ECE 891: Internship
  • ECE 899: Special Readings in Electrical Engineering
  • EGR 190: Special Topics in Engineering
  • EGR 590: Special Topics in Engineering
  • POE 190: Practice Oriented Education
  • POE 505: Winter Breakaway
  • POE 790: Practice Oriented Education
  • STA 393: Research Independent Study
  • STA 493: Research Independent Study
  • STA 993: Independent Study

In the News

Representative Publications

  • Meireles, OR; Rosman, G; Altieri, MS; Carin, L; Hager, G; Madani, A; Padoy, N; Pugh, CM; Sylla, P; Ward, TM; Hashimoto, DA; SAGES Video Annotation for AI Working Groups,, SAGES consensus recommendations on an annotation framework for surgical video., Surgical Endoscopy, vol 35 no. 9 (2021), pp. 4918-4929 [10.1007/s00464-021-08578-9] [abs].
  • Datta, S; Mariottoni, EB; Dov, D; Jammal, AA; Carin, L; Medeiros, FA, RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure., Scientific Reports, vol 11 no. 1 (2021) [10.1038/s41598-021-91493-9] [abs].
  • Chapfuwa, P; Assaad, S; Zeng, S; Pencina, MJ; Carin, L; Henao, R, Enabling counterfactual survival analysis with balanced representations, Acm Chil 2021 Proceedings of the 2021 Acm Conference on Health, Inference, and Learning (2021), pp. 133-145 [10.1145/3450439.3451875] [abs].
  • Dov, D; Assaad, S; Si, S; Wang, R; Xu, H; Kovalsky, SZ; Bell, J; Range, DE; Cohen, J; Henao, R; Carin, L, Affinitention nets: Kernel perspective on attention architectures for set classification with applications to medical text and images, Acm Chil 2021 Proceedings of the 2021 Acm Conference on Health, Inference, and Learning (2021), pp. 14-24 [10.1145/3450439.3451856] [abs].
  • Dai, S; Cheng, Y; Zhang, Y; Gan, Z; Liu, J; Carin, L, Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 12625 LNCS (2021), pp. 268-283 [10.1007/978-3-030-69538-5_17] [abs].