Vahid Tarokh's Home Page

Contact Information:

Vahid Tarokh
The Rhodes Family Professor of Electrical and Computer Engineering
Bass Connections Endowed Professor
Professor of Mathematics
Professor of Computer Science
Microsoft Data Science Investigator
Rhodes Information Initiative at Duke
327 Gross Hall
140 Science Drive
Durham, NC 27708
(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

  • Overview of Some of My Group’s 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
  • Inferring the causality network of Abeta and Tau accumulation in the aging brain: a statistical inference approach
  • 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
  • Model Selection Techniques: An overview
  • Asymptotically Optimal Prediction for Time-Varying Data Generating Processes
  • Gradient Information for Representation and Modeling
  • Prediction in Online Convex Optimization for Parametrizable Objective Functions
  • Convergence Rate Of Empirical Spectral Distribution OF Random Matrices From Linear Codes