Rhodes Family Professor of Electrical and Computer Engineering
Vahid Tarokh’s research is in pursuing new formulations and approaches to getting the most out of datasets.
Appointments and Affiliations
- Rhodes Family Distinguished Professor of Electrical and Computer Engineering
- Professor of Electrical and Computer Engineering
Contact Information
- Office Location: 130 Hudson Hall, Durham, NC 27708
- Office Phone: +1 919 660 7594
- Email Address: vahid.tarokh@duke.edu
- Websites:
Research Interests
Foundations of AI, Foundations of Signal Processing, Learning Representations, Transfer Learning, Meta-Learning, Physics Infused Learning, Extreme Value Theory, Dependence Modeling, Hypothesis Testing, Sequential Analysis.
Awards, Honors, and Distinctions
- Member. National Academy of Engineering. 2019
Courses Taught
- MATH 493: Research Independent Study
- ECE 899: Special Readings in Electrical Engineering
- ECE 891: Internship
- ECE 689: Advanced Topics in Deep Learning
- ECE 685D: Introduction to Deep Learning
- ECE 590: Advanced Topics in Electrical and Computer Engineering
- ECE 493: Projects in Electrical and Computer Engineering
- COMPSCI 676: Advanced Topics in Deep Learning
- COMPSCI 675D: Introduction to Deep Learning
- COMPSCI 590: Advanced Topics in Computer Science
Representative Publications
- Wu, S., E. Diao, T. Banerjee, J. Ding, and V. Tarokh. “Quickest Change Detection for Unnormalized Statistical Models.” IEEE Transactions on Information Theory 70, no. 2 (February 1, 2024): 1220–32. https://doi.org/10.1109/TIT.2023.3328274.
- Diao, Enmao, Taposh Banerjee, and Vahid Tarokh. “Large Deviation Analysis of Score-based Hypothesis Testing,” January 27, 2024.
- Padilla, W. J., Y. Deng, O. Khatib, and V. Tarokh. “Fundamental absorption bandwidth to thickness limit for transparent homogeneous layers.” Nanophotonics, January 1, 2024. https://doi.org/10.1515/nanoph-2023-0920.
- Venkatasubramanian, Shyam, Ahmed Aloui, and Vahid Tarokh. “Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks,” November 21, 2023.
- Aloui, Ahmed, Juncheng Dong, Cat P. Le, and Vahid Tarokh. “Counterfactual Data Augmentation with Contrastive Learning,” November 6, 2023.