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Vahid Tarokh

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 Professor of Electrical and Computer Engineering
  • Professor of Electrical and Computer Engineering
  • Professor of Computer Science
  • Professor of Mathematics

Contact Information

Research Interests

Representation, modeling, inference and prediction from data

Courses Taught

  • ECE 590: Advanced Topics in Electrical and Computer Engineering

Representative Publications

  • 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].
  • Shahrampour, S; Beirami, A; Tarokh, V, Supervised Learning Using Data-dependent Random Features with Application to Seizure Detection, Proceedings of the Ieee Conference on Decision and Control, vol 2018-December (2019), pp. 1168-1173 [10.1109/CDC.2018.8619558] [abs].
  • Shao, S; Jacob, PE; Ding, J; Tarokh, V, Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency, Journal of the American Statistical Association (2019) [10.1080/01621459.2018.1518237] [abs].
  • Ding, J; Tarokh, V; Yang, Y, Model Selection Techniques: An Overview, Ieee Signal Processing Magazine, vol 35 no. 6 (2018), pp. 16-34 [10.1109/MSP.2018.2867638] [abs].