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Math @ Duke





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Vahid Tarokh, Rhodes Family Distinguished Professor of Electrical and Computer Engineering and Professor of Mathematics

Vahid Tarokh

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.

Contact Info:
Office Location:  Rhodes Information Initiative at Duke, 327 Gross Hall , 140 Science Drive, Durha
Office Phone:  (919) 660-7594
Email Address: send me a message
Web Page:  http://www.duke.edu/~vt45

Teaching (Fall 2021):

  • ECE 685D.001, INTRO TO DEEP LEARNING Synopsis
    FITZPATRK 1464, MW 10:15 AM-11:30 AM
    (also cross-listed as COMPSCI 675D.001)
  • ECE 685D.01D, INTRO TO DEEP LEARNING Synopsis
    FITZPATRK 1464, Tu 05:15 PM-06:30 PM
    (also cross-listed as COMPSCI 675D.01D)
  • ECE 685D.02D, INTRO TO DEEP LEARNING Synopsis
    Hudson 212, W 03:30 PM-04:45 PM
    (also cross-listed as COMPSCI 675D.02D)
  • ECE 685D.03D, INTRO TO DEEP LEARNING Synopsis
    FITZPATRK 1464, Th 05:15 PM-06:30 PM
    (also cross-listed as COMPSCI 675D.03D)
Recent Publications   (More Publications)

  1. 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, vol. 69 no. 8 (August, 2021), pp. 5152-5167 [doi]  [abs]
  2. Feng, Y; Wongkamthong, C; Soltani, M; Ng, Y; Gogineni, S; Kang, B; Pezeshki, A; Calderbank, R; Rangaswamy, M; Tarokh, V, Knowledge-Aided Data-Driven Radar Clutter Representation, Ieee National Radar Conference Proceedings, vol. 2021-May (May, 2021), ISBN 9781728176093 [doi]  [abs]
  3. Ding, J; Diao, E; Zhou, J; Tarokh, V, On Statistical Efficiency in Learning, Ieee Transactions on Information Theory, vol. 67 no. 4 (April, 2021), pp. 2488-2506 [doi]  [abs]
  4. 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 (March, 2021), pp. 371, ISBN 9780738112275 [doi]  [abs]
  5. 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 (January, 2021), pp. 1-10, ISBN 9781624106095  [abs]

 

dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320