Vahid Tarokh

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.