- Rhodes Family Distinguished Professor of Electrical and Computer Engineering
- Professor of Electrical and Computer Engineering
- Professor of Mathematics (Secondary)
- Professor of Computer Science (Secondary)
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.
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, the Rhodes Family Professor of Electrical and Computer Engineering, has been named members of the National Academy of Engineering (NAE). Election to the NAE is one of the highest professional distinctions for engineers. Tarokh was... read more »
Vahid Tarokh and Amanda Randles were two of seven faculty members endowed Bass Connections Professorships. This honor recognizes faculty whose scholaraship and teaching align with the interdisciplinary and collaborative nature of the program, while... read more »
Only ten professors were named Distinguished Professors this year in the Trinity College of Arts and Sciences. This distinction is given to professors for their outstanding research and teaching accomplishments. We are proud to announce that three... read more »