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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 Mathematics
- Office Location: Rhodes Information Initiative at Duke, 327 Gross Hall , 140 Science Drive, Durham, NC 27708
- Office Phone: (919) 660-7594
- Email Address: firstname.lastname@example.org
Representation, modeling, inference and prediction from data
Awards, Honors, and Distinctions
- Member. National Academy of Engineering. 2019
- COMPSCI 675D: Introduction to Deep Learning
- ECE 280L9: Signals and Systems - Lab
- ECE 280L: Introduction to Signals and Systems
- ECE 392: Projects in Electrical and Computer Engineering
- ECE 494: Projects in Electrical and Computer Engineering
- ECE 590: Advanced Topics in Electrical and Computer Engineering
- ECE 590D: Advanced Topics in Electrical and Computer Engineering
- ECE 685D: Introduction to Deep Learning
- ECE 899: Special Readings in Electrical Engineering
- Angjelichinoski, M; Soltani, M; Choi, J; Pesaran, B; Tarokh, V, Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions from Limited Data., Ieee Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the Ieee Engineering in Medicine and Biology Society, vol PP (2021) [10.1109/tnsre.2021.3083755] [abs].
- Ding, J; Diao, E; Zhou, J; Tarokh, V, On Statistical Efficiency in Learning, Ieee Transactions on Information Theory, vol 67 no. 4 (2021), pp. 2488-2506 [10.1109/TIT.2020.3047620] [abs].
- Soltani, M; Wu, S; Li, Y; Ravier, R; Ding, J; Tarokh, V, Compressing Deep Networks Using Fisher Score of Feature Maps, Data Compression Conference Proceedings, vol 2021-March (2021) [10.1109/DCC50243.2021.00083] [abs].
- Yang, H; Jing, D; Tarokh, V; Bewley, G; Ferrari, S, Flow parameter estimation based on on-board measurements of air vehicle traversing turbulent flows, Aiaa Scitech 2021 Forum (2021), pp. 1-10 [abs].
- Kojima, S; Maruta, K; Feng, Y; Ahn, CJ; Tarokh, V, CNN based Joint SNR and Doppler Shift Classification using Spectrogram Images for Adaptive Modulation and Coding, Ieee Transactions on Communications (2021) [10.1109/TCOMM.2021.3077565] [abs].