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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. Current projects are focused on representation, modeling, inference and prediction from data such as determining how different people will respond to exposure to certain viruses, predicting rare events from small amounts of data, formulation and calculation of limits of learning from observations, and prediction of a macaque monkey's future actions from its brain waves.

Appointments and Affiliations

  • Rhodes Family Distinguished Professor of Electrical and Computer Engineering
  • Professor of Electrical and Computer Engineering
  • Professor of Computer Science
  • Professor of Mathematics

Contact Information

  • Office Location: Rhodes Information Initiative at Duke, 327 Gross Hall , 140 Science Drive, Durham, NC 27708
  • Office Phone: (919) 660-7594
  • Email Address:
  • Websites:

Research Interests

Representation, modeling, inference and prediction from data

Courses Taught

  • COMPSCI 675D: Introduction to Deep Learning
  • ECE 280L9: Signals and Systems - Lab
  • ECE 280L: Introduction to Signals and Systems
  • ECE 590D: Advanced Topics in Electrical and Computer Engineering
  • ECE 685D: Introduction to Deep Learning
  • ECE 899: Special Readings in Electrical Engineering

Representative Publications

  • Angjelichinoski, M; Soltani, M; Choi, J; Pesaran, B; Tarokh, V, Deep James-Stein Neural Networks for Brain-Computer Interfaces, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol 2020-May (2020), pp. 1339-1343 [10.1109/ICASSP40776.2020.9053694] [abs].
  • Diao, E; Ding, J; Tarokh, V, DRASIC: Distributed recurrent autoencoder for scalable image compression, Data Compression Conference Proceedings, vol 2020-March (2020), pp. 3-12 [10.1109/DCC47342.2020.00008] [abs].
  • Angjelichinoski, M; Choi, J; Banerjee, T; Pesaran, B; Tarokh, V, Cross-subject decoding of eye movement goals from local field potentials., Journal of Neural Engineering, vol 17 no. 1 (2020) [10.1088/1741-2552/ab6df3] [abs].
  • Jeong, S; Li, X; Yang, J; Li, Q; Tarokh, V, Sparse representation-based denoising for high-resolution brain activation and functional connectivity modeling: A task fMRI study, Ieee Access, vol 8 (2020), pp. 36728-36740 [10.1109/ACCESS.2020.2971261] [abs].
  • Zhou, Y; Wang, Z; Ji, K; Liang, Y; Tarokh, V, Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization., Corr, vol abs/2002.11582 (2020) [abs].