Lawrence Carin

简介

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 Brooklyn Polytechnic Institute (now part of NYU) 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, and Vice Provost and Vice President for Research from 2014-2020. He was the Provost at King Abdullah University of Science & Technology (KAUST) from 2020-2023, returning to Duke in 2023. From 2003-2014 he held the William H. Younger Distinguished Professorship, and since 2018 h

教育经历

• B.S.E. University of Maryland, College Park, 1985
• M.Sc.Eng. University of Maryland, College Park, 1986
• Ph.D. University of Maryland, College Park, 1989
• Professor Emeritus of Electrical and Computer Engineering

代表成果

• Si S, Jiang X, Su Q, Carin L. Detecting implicit biases of large language models with Bayesian hypothesis testing. Scientific reports. 2025 Apr;15(1):12415.
• Wang D, Yang Y, Chen L, Gan Z, Henao R, Carin L. Proactive Pseudo-Intervention: Pre-informed Contrastive Learning For Interpretable Vision Models. Proceedings of Machine Learning Research. 2025 Jan 1;281:20u201334.
• Velazquez D, Grace M, Karageorgos K, Carin L, Schliem A, Zaikis D, et al. LangMark: A Multilingual Dataset for Automatic Post-Editing. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2025. p. 32653u201367.
• Cheng X, Carin L, Sra S. GRAPH TRANSFORMERS DREAM OF ELECTRIC FLOW. In: 13th International Conference on Learning Representations Iclr 2025. 2025. p. 75663u201384.
• Wang AT, Convertino W, Cheng X, Henao R, Carin L. On Understanding Attention-Based In-Context Learning for Categorical Data. In: Proceedings of Machine Learning Research. 2025. p. 62701u201328.
• Zhang H, Cong Y, Wang Z, Zhang L, Zhao M, Chen L, et al. Text Feature Adversarial Learning for Text Generation With Knowledge Transfer From GPT2. IEEE Trans Neural Netw Learn Syst. 2024 May;35(5):6558u201369.
• Assaad S, Dov D, Park C, Davis R, Kovalsky SZ, Lee WT, et al. A Preliminary Study Comparing the Performance of Thyroid Molecular Tests to a Deep Learning Algorithm in Predicting Malignancy in Indeterminate Thyroid Fine Needle Aspiration Biopsies. Thyroid. 2024 Apr;34(4):531u20135.
• Verma V, Mehta N, Liang KJ, Mishra A, Carin L. Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning. In: Proceedings 2024 IEEE Winter Conference on Applications of Computer Vision Wacv 2024. 2024. p. 2709u201319.
• Glass M, Ji Z, Davis R, Pavlisko EN, DiBernardo L, Carney J, et al. A machine learning algorithm improves the diagnostic accuracy of the histologic component of antibody mediated rejection (AMR-H) in cardiac transplant endomyocardial biopsies. Cardiovasc Pathol. 2024;72:107646.
• Dow ER, Jeong HK, Katz EA, Toth CA, Wang D, Lee T, et al. A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography. JAMA Ophthalmol. 2023 Nov 1;141(11):1052u201361.