Vahid Tarokh

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 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: vahid.tarokh@duke.edu
  • Websites:

Research Interests

Representation, modeling, inference and prediction from data

Courses Taught

  • ECE 590: Advanced Topics in Electrical and Computer Engineering

Representative Publications

  • Banerjee, T; Allsop, S; Tye, KM; Ba, D; Tarokh, V, Sequential Detection of Regime Changes in Neural Data, International Ieee/Embs Conference on Neural Engineering, Ner, vol 2019-March (2019), pp. 139-142 [10.1109/NER.2019.8716926] [abs].
  • Ding, J; Zhou, J; Tarokh, V, Asymptotically Optimal Prediction for Time-Varying Data Generating Processes, Ieee Transactions on Information Theory, vol 65 no. 5 (2019), pp. 3034-3067 [10.1109/TIT.2018.2882819] [abs].
  • Angjelichinoski, M; Banerjee, T; Choi, J; Pesaran, B; Tarokh, V, Minimax-optimal decoding of movement goals from local field potentials using complex spectral features., Journal of Neural Engineering, vol 16 no. 4 (2019) [10.1088/1741-2552/ab1a1f] [abs].
  • Xiang, Y; Ding, J; Tarokh, V, Estimation of the evolutionary spectra with application to stationarity test, Ieee Transactions on Signal Processing, vol 67 no. 5 (2019), pp. 1353-1365 [10.1109/TSP.2018.2890369] [abs].
  • Banerjee, T; Whipps, G; Gurram, P; Tarokh, V, Cyclostationary statistical models and algorithms for anomaly detection using multi-modal data, 2018 Ieee Global Conference on Signal and Information Processing, Globalsip 2018 Proceedings (2019), pp. 126-130 [10.1109/GlobalSIP.2018.8646417] [abs].