Vahid Tarokh's Home Page


Contact Information:


Vahid Tarokh
The Rhodes Family Professor of Electrical and Computer Engineering
Professor of Mathematics
Professor of Computer Science
Quantitative Initiative at Duke &
Information Initiative at Duke
327 Gross Hall
140 Science Drive
Durham, NC 27708
U.S.A.
(919) 660-7594
E-mail: vahid (dot) tarokh (at) duke (dot) edu


  • Short Biography

  • Advising Information:


  • Current Students and Postdocs

  • Former Postdoctoral Fellows and Their Positions

  • Former PhD Students and Their Positions

  • Former M.S./M.Eng. Students

  • Undergrad Thesis Students and Summer High School Students Supervised

  • Graduate Students Supervised in Other Capacity

  • Theses Examined

  • My Lab, Open Positions


  • The Signal Processing and Applied Statistics (SPAS) Group (My Lab—Under Construction)

  • Postdoctoral Positions Available at My Lab

  • Information for Perspective Graduate Students

  • Teachings


  • Courses Taught

  • Research


  • Past Research

  • Current Research

  • Some Recent Papers
  • Bridging AIC and BIC: A New Criterion for Autoregression
  • Multiple Change Point Analysis: Fast Implementation And Strong Consistency
  • SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series
  • Symmetric Pseudo-Random Matrices
  • Pseudo-Wigner Matrices
  • Convergence of Limited Communications Gradient Methods
  • Analysis of Multi-State Autoregressive Models
  • On Sequential Elimination Algorithms for Best-Arm Identification in Multi-Armed Bandits
  • Online Learning for Multimodal Data Fusion with Application to Object Recognition
  • Evolutionary Spectra Based on the Multitaper Method with Application to Stationarity Test
  • Large Deviations of Convex Polyominoes
  • Region Detection in Markov Random Fields: Gaussian Case
  • Bayesian Model Comparison With the Hyvärinen Score: Computation and Consistency

  • On Optimal Generalizability in Parametric Learning
  • On Data-Dependent Random Features for Improved Generalization in Supervised Learning
  • Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels
  • Data Driven Optimization with Unknown Time-Varying Objective Functions