Vahid Tarokh's Home Page


Contact Information:


Vahid Tarokh
The Rhodes Family Professor of Electrical and Computer Engineering
Wilkinson Engineering Building Room 425
305 Teer Engineering Building Box 90271
Durham, NC 27708
U.S.A.
(919) 660-7594
E-mail: vahidtarokhacademic (at) gmail (dot) com


  • 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


  • Postdoctoral Positions Available at My Lab

  • Information for Perspective Graduate Students

  • Research


    Our current research is in Foundations of AI (pursuing new mathematical formulations and approaches to getting the most out of datasets). We invent both new mathematical methodologies, and demonstrate these (by numerical simulations) in various scenarios of interest. Although our research is mainly mathematical, nevertheless we look for mathematical nuggets that may lead to new applications. In other words, we do not necessarily build any systems, but make algorithms/mathematical constructions that may be (in some cases) used in future systems.

    Current projects are focused on representation, modeling, inference and prediction from data such as predicting rare events from small amounts of data, formulation and calculation of limits of learning from observations, and robust prediction of a macaque monkey's future actions from its (or from what we have learned from another monkey's) brain waves.

    You need to know and be willing to learn more math if you want to join our group. Some members of our group have no electrical engineering or any engineering degrees at all. We have members with backgrounds in astronomy, applied mathematics, pure mathematics, physics, electrical engineering and aero-astro engineering. So in every way, we are diverse! Our typical papers usually include theorems and proofs. But we do not call ourselves "pure mathematicians". That said in some cases, we have invented new pure mathematical results in the past.


  • Some Recent Papers
  • Neuroscience
  • Deep Site-Invariant Neural Decoding from Local Field Potentials , preprint.
  • Consistent coordination patterns provide near perfect behavior decoding in a comprehensive motor program for insect flight , preprint.
  • Deep Cross-Subject Mapping of Neural Activity , preprint.
  • Deep Pinsker and James-Stein Neural Networks for Motor Intentions From Limited Data, IEEE in Transactions on Neural Systems & Rehabilitation Engineering, May 2021
  • Sparse Representation-Based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A Task fMRI Study , IEEE Access, Feb. 2020.
  • Cross-subject Decoding of Eye Movement Goals from Local Field Potentials , Journal of Neural Engineering 2020.
  • Minimax-optimal decoding of movement goals from local field potentials using complex spectral features , Journal of Neural Engineering, 2019
  • Sequential Detection of Regime Changes in Neural Data, IEEE/EMBS Conference on Neural Engineering, 2019
  • Wavelet Shrinkage and Thresholding Based Robust Classification for Brain-Computer Interface , IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
  • Machine Learning
  • Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes , International Conference on Artificial Intelligence and Statistics (AISTATS) 2024.
  • Off-Policy Evaluation fro Human Feedback , Conference on Neural Information Processing System (NeurIPS), 2023.
  • Inference and Sampling of Point Processes from Diffusion Excursions , Uncertainty in Artificial Intelligence 2023.
  • Transfer Learning for Individual Treatment Effect Estimation , Uncertainty in Artificial Intelligence 2023.
  • PASTA: Pessimistic Assortment Optimization , International Conference on Machine Learning (ICML), 2023.
  • Pruning Deep Neural Networks from a Sparsity Perspective , International Conference on Learning Representation (ICLR), 2023.
  • Characteristic Neural Ordinary Differential Equations , International Conference on Learning Representation (ICLR), 2023.
  • Inference and Sampling for Archimax Copulas , Conference on Neural Information Processing System (NeurIPS), 2022.
  • SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients , Conference on Neural Information Processing System (NeurIPS), 2022.
  • Gradient Assisted Learning for Decentralized Multi-Organization Collaborations , Conference on Neural Information Processing System (NeurIPS), 2022.
  • Fisher Task Distance and Its Applications in Neural Architecture , IEEE ACCESS, May 2022.
  • Semi-Empirical Objective Functions for MCMC Proposal Optimization , International Conference on Pattern Recognition, 2022.
  • Modeling Extremes with d-max-decreasing Neural Networks , Conference on Uncertainty in Artificial Intelligence (UAI), 2022
  • Multi-Agent Adversarial Attacks for Multi-Channel Communications , International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022.
  • Blaschke Product Neural Network(BPNN): A Physics-Infused Neural Network For Phase Retrieval of Meromorphic Functions , International Conference on Learning Representation (ICLR), 2022.
  • Task Affinity With Maximum Bipartite Matching in Few-Shot Learning , International Conference on Learning Representation (ICLR), 2022.
  • Identifying latent stochastic differential equations" , IEEE Transactions on Signal Processing, Jan 2022.
  • Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials , Conference on Neural Information Processing System (NeurIPS), 2021.
  • Generative Archimedean Copulas , Conference on Uncertainty in Artificial Intelligence (UAI) 2021.
  • Fisher Auto-Encoders , International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
  • HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients , International Conference on Learning Representation, 2021.
  • Projected Latent Markov Chain Monte Carlo: Conditional Inference with Normalizing Flows , International Conference on Learning Representation, 2021.
  • Compressing Deep Networks Using Fisher Score of Feature Maps, Data Compression Conference 2021.
  • On the Information of Feature Maps and Pruning of Deep Neural Networks , International Conference on Pattern Recognition, 2021.
  • A Convergent Accelerated Proximal Gradient with Restart for Nonconvex Optimization , Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2020.
  • Distributed Lossy Image Compression with Recurrent Networks , Proceeding of Data Compression Conference 2020.
  • Gradient Information for Representation and Modeling , Conference on Neural Information Processing System (NeurIPS), 2019.
  • SpiderBoost and Momentum: Faster Variance Reduction Algorithms, Conference on Neural Information Processing System (NeurIPS), 2019.
  • Restricted Recurrent Neural Networks , IEEE International Conference on Big Data, 2019.
  • SGD Converges to Global Minimum in Deep Learning via Star-convex Path , International Conference on Learning Representation, 2019.
  • Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels , Conference on Neural Information Processing System (NeurIPS), 2018.
  • On Data-Dependent Random Features for Improved Generalization in Supervised Learning, The Thirty-Second AAAI Conference on Artificial Intelligence, 2018
  • On Optimal Generalizability in Parametric Learning, Conference on Neural Information Processing System (NeurIPS), 2017
  • Statistics and Modeling
  • Robust Quickest Change Detection for Unnormalized Statistical Models , Conference on Uncertainty in Artificial Intelligence, 2023.
  • Quickest Change Detection for Unnormalized Statistical Models , International Conference on Artificial Intelligence and Statistics (AISTATS) 2023.
  • Score-Based Hypothesis Testing for Unnormalized Models , IEEE ACCESS, July 2022.
  • Model Linkage Selection for Cooperative Learning , Journal of Machine Learning Research, 2021.
  • On Statistical Efficiency in Learning , IEEE Transactions on Information Theory, April 2021.
  • Bayesian Model Comparison With the Hyvarinen Score: Computation and Consistency , Journal of the American Statistical Association, 2018

  • Asymptotically Optimal Prediction for Time-Varying Data Generating Processes, IEEE Transactions on Information Theory 2019
  • Model Selection Techniques: An overview , IEEE Signal Processing Magazine, 2018.
  • Bridging AIC and BIC: A New Criterion for Autoregression , IEEE Transactions on Information Theory 2018
  • Signal Processing
  • Multiple Change Point Analysis: Fast Implementation And Strong Consistency, IEEE Transactions on Signal Processing, 2017
  • SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series, IEEE Transactions on Signal Processing, 2017
  • Analysis of Multi-State Autoregressive Models, IEEE Transactions on Signal Processing, 2018
  • On Sequential Elimination Algorithms for Best-Arm Identification in Multi-Armed Bandits, IEEE Transactions on Signal Processing, 2017
  • Evolutionary Spectra Based on the Multitaper Method with Application to Stationarity Test, IEEE Transactions on Signal Processing, 2019
  • Online Learning for Multimodal Data Fusion with Application to Object Recognition, IEEE Transactions on Circuits and Systems II: Express Briefs, 2018.
  • Optimization and Control
  • Prediction in Online Convex Optimization for Parametrizable Objective Functions , IEEE Conference on Decision and Control (CDC 2019).
  • Distributed Online Convex Optimization with Improved Dynamic Regret, IEEE Transactions on Automatic Control, submitted 2019.
  • Convergence of Limited Communication Gradient Methods, IEEE Transactions on Automatic Control, 2017.
  • Communication Complexity of Dual Decomposition Methods for Distributed Resource Allocation Optimization , IEEE Journal of Selected Topics in Signal Processing, 2018.
  • Astronomy
  • Robust interferometric imaging via prior-less phase recovery: redundant spacing calibration with generalized-closure phases, Monthly Notices of Royal Astronomical Society, 2017.
  • Resolving phase ambiguities in the calibration of redundant interferometric arrays: implications for array design , Monthly Notices of Royal Astronomical Society, 2016.
  • Material Science/Physics
  • Fundamental absorption bandwidth to thickness limit for transparent homogeneous layers, Nanophotonics, March 2024.
  • Neural Network Accelerated Process Design of Polycrystalline Microstructures , Materials Today, Aug 2023.
  • Mathematics
  • Large deviations of convex polyominoes, Electronic Journal of Probability, 2022.
  • Pseudo-Wigner Matrices , IEEE Transactions on Information Theory, 2018.
  • Symmetric Pseudo-Random Matrices , IEEE Transactions on Information Theory, 2018.
  • Spectral Distribution of Product of Pseudorandom Matrices Formed From Binary Block Codes , IEEE Transactions on Information Theory, 2013.
  • Spectral Distribution of Random Matrices From Binary Linear Block Codes , IEEE Transactions on Information Theory, 2011.
  • On the Trellis Complexity of the Densest Lattice Packings in $\mathbb{R}^n $ , SIAM Journal of Discrete Math, 1996.
  • A Constraint on The Existence of Simple Torsion-Free Lie Modules , Proceedings of American Mathematical Society, 1995.
  • Blockchain
  • Talaria: A Framework for Simulation of Permissioned Blockchains for Logistics and Beyond, preprint.