Vahid Tarokh

Tarokh

Rhodes Family Distinguished 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: (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

  • COMPSCI 590: Advanced Topics in Computer Science
  • COMPSCI 675D: Introduction to Deep Learning
  • ECE 392: Projects in Electrical and Computer Engineering
  • ECE 493: Projects in Electrical and Computer Engineering
  • ECE 494: Projects in Electrical and Computer Engineering
  • ECE 590: Advanced Topics in Electrical and Computer Engineering
  • ECE 685D: Introduction to Deep Learning
  • ECE 891: Internship
  • ECE 899: Special Readings in Electrical Engineering
  • MATH 493: Research Independent Study

Representative Publications

  • Soloveychik, I; Tarokh, V, Region selection in Markov random fields: Gaussian case, Journal of Multivariate Analysis, vol 196 (2023) [10.1016/j.jmva.2023.105178] [abs].
  • Le, Q; Diao, E; Wang, X; Anwar, A; Tarokh, V; Ding, J, Personalized Federated Recommender Systems with Private and Partially
    Federated AutoEncoders     (2022) [abs].
  • Li, B; Wu, S; Tripp, EE; Pezeshki, A; Tarokh, V, Minimax Concave Penalty Regularized Adaptive System Identification (2022) [abs].
  • Aloui, A; Dong, J; Le, CP; Tarokh, V, Causal Knowledge Transfer from Task Affinity (2022) [abs].
  • Venkatasubramanian, S; Gogineni, S; Kang, B; Pezeshki, A; Rangaswamy, M; Tarokh, V, Data-Driven Target Localization Using Adaptive Radar Processing and
    Convolutional Neural Networks     (2022) [abs].