Vahid Tarokh

Vahid Tarokh
  • Rhodes Family Distinguished Professor of Electrical and Computer Engineering
  • Professor of Electrical and Computer Engineering
  • Professor of Mathematics (Secondary)
  • Professor of Computer Science (Secondary)
External address: Rhodes Information Initiative at Duke, 327 Gross Hall , 140 Science Drive, Durham, NC 27708
Phone: (919) 660-7594

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.

Tarokh, V. Preface. 2009. Scopus, doi:10.1007/978-1-4419-0673-1. Full Text

Saligheh, H., et al. “Analog Transmission.” Handbook of Computer Networks, vol. 1, 2011, pp. 102–14. Scopus, doi:10.1002/9781118256053.ch8. Full Text

Ochiai, H., et al. “Random array theory and collaborative beamforming.” Handbook on Advancements in Smart Antenna Technologies for Wireless Networks, 2008, pp. 94–106. Scopus, doi:10.4018/978-1-59904-988-5.ch005. Full Text

Devroye, N., et al. “Information theoretic analysis of cognitive radio systems.” Cognitive Wireless Communication Networks, 2007, pp. 45–78. Scopus, doi:10.1007/978-0-387-68832-9_2. Full Text

Devroye, N., and V. Tarokh. “Fundamental limits of cognitive radio networks.” Cognitive Wireless Networks: Concepts, Methodologies and Visions Inspiring the Age of Enlightenment of Wireless Communications, 2007, pp. 327–51. Scopus, doi:10.1007/978-1-4020-5979-7_17. Full Text

Shin, O. S., et al. “Cooperation, competition and cognition in wireless networks: From theory to implementation.” Cooperation in Wireless Networks: Principles and Applications: Real Egoistic Behavior Is to Cooperate!, 2006, pp. 69–100. Scopus, doi:10.1007/1-4020-4711-8_3. Full Text

Angjelichinoski, Marko, et al. “Cross-subject decoding of eye movement goals from local field potentials.Journal of Neural Engineering, Jan. 2020. Epmc, doi:10.1088/1741-2552/ab6df3. Full Text

Shao, S., et al. “Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency.” Journal of the American Statistical Association, vol. 114, no. 528, Oct. 2019, pp. 1826–37. Scopus, doi:10.1080/01621459.2018.1518237. Full Text

Angjelichinoski, Marko, et al. “Minimax-optimal decoding of movement goals from local field potentials using complex spectral features.Journal of Neural Engineering, vol. 16, no. 4, Aug. 2019, p. 046001. Epmc, doi:10.1088/1741-2552/ab1a1f. Full Text

Krishnamurthy, S., et al. “Peak sidelobe level gumbel distribution of antenna arrays with random phase centers.” Ieee Transactions on Antennas and Propagation, vol. 67, no. 8, Aug. 2019, pp. 5399–410. Scopus, doi:10.1109/TAP.2019.2917469. Full Text

Banerjee, T., et al. “Sequential Detection of Regime Changes in Neural Data.” International Ieee/Embs Conference on Neural Engineering, Ner, vol. 2019-March, May 2019, pp. 139–42. Scopus, doi:10.1109/NER.2019.8716926. Full Text

Ding, J., et al. “Asymptotically Optimal Prediction for Time-Varying Data Generating Processes.” Ieee Transactions on Information Theory, vol. 65, no. 5, May 2019, pp. 3034–67. Scopus, doi:10.1109/TIT.2018.2882819. Full Text

Xiang, Y., et al. “Estimation of the evolutionary spectra with application to stationarity test.” Ieee Transactions on Signal Processing, vol. 67, no. 5, Mar. 2019, pp. 1353–65. Scopus, doi:10.1109/TSP.2018.2890369. Full Text

Banerjee, T., et al. “Cyclostationary statistical models and algorithms for anomaly detection using multi-modal data.” 2018 Ieee Global Conference on Signal and Information Processing, Globalsip 2018  Proceedings, Feb. 2019, pp. 126–30. Scopus, doi:10.1109/GlobalSIP.2018.8646417. Full Text

Diao, Enmao, et al. “Distributed Lossy Image Compression with Recurrent Networks.Corr, vol. abs/1903.09887, 2019.

Diao, Enmao, et al. “Restricted Recurrent Neural Networks.Corr, vol. abs/1908.07724, 2019.


Farhadi, Hamed, et al. “Inferring the causality network of Abeta and Tau accumulation in the aging brain: a statistical inference approach.” Journal of Nuclear Medicine, vol. 58, SOC NUCLEAR MEDICINE INC, 1 May 2017.

Zhou, Y., et al. “Multi-Level Mean-Shift Clustering for Single-Channel Radio Frequency Signal Separation.” Ieee International Workshop on Machine Learning for Signal Processing, Mlsp, vol. 2019-October, 2019. Scopus, doi:10.1109/MLSP.2019.8918879. Full Text

Shahrampour, S., et al. “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. 1168–73. Scopus, doi:10.1109/CDC.2018.8619558. Full Text

Ding, J., et al. “A Penalized Method for the Predictive Limit of Learning.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing  Proceedings, vol. 2018-April, 2018, pp. 4414–18. Scopus, doi:10.1109/ICASSP.2018.8461832. Full Text

Ding, J., et al. “Optimal prediction of data with unknown abrupt change points.” 2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017  Proceedings, vol. 2018-January, 2018, pp. 928–32. Scopus, doi:10.1109/GlobalSIP.2017.8309096. Full Text

DIng, J., et al. “Detecting structural changes in dependent data.” 2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017  Proceedings, vol. 2018-January, 2018, pp. 750–54. Scopus, doi:10.1109/GlobalSIP.2017.8309060. Full Text

Han, Q., et al. “Modeling nonlinearity in multi-dimensional dependent data.” 2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017  Proceedings, vol. 2018-January, 2018, pp. 206–10. Scopus, doi:10.1109/GlobalSIP.2017.8308633. Full Text

Soloveychik, I., et al. “Explicit symmetric pseudo-random matrices.” Ieee International Symposium on Information Theory  Proceedings, vol. 2018-January, 2018, pp. 424–28. Scopus, doi:10.1109/ITW.2017.8277999. Full Text

Shahrampour, Shahin, et al. “On Data-Dependent Random Features for Improved Generalization in Supervised Learning.Aaai, edited by Sheila A. McIlraith and Kilian Q. Weinberger, AAAI Press, 2018, pp. 4026–33.

Banerjee, Taposh, et al. “Classification of Local Field Potentials using Gaussian Sequence Model.Ssp, IEEE, 2018, pp. 683–87.

Shahrampour, Shahin, and Vahid Tarokh. “Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels.Neurips, edited by Samy Bengio et al., 2018, pp. 4695–706.


Vahid Tarokh

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