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Rising Stars
2025

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Rising Stars

D-Index
36
Citations
5745
World Ranking
791
National Ranking
130

Computer Science

D-Index
31
Citations
4896
World Ranking
13539
National Ranking
5405

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Aryan Mokhtari is affiliated with The University of Texas at Austin in the United States. Their research primarily spans the field of computer science, with a focus on artificial intelligence, numerical analysis, computational mechanics, computational theory and mathematics, and computer networks and communications.

The scientist has contributed extensively to various research topics, including:

  • Stochastic Gradient Optimization Techniques
  • Sparse and Compressive Sensing Techniques
  • Advanced Optimization Algorithms Research
  • Privacy-Preserving Technologies in Data
  • Domain Adaptation and Few-Shot Learning
  • Iterative Methods for Nonlinear Equations
  • Machine Learning and Data Classification

They have authored numerous papers, with recent publications including:

  • Personalized Federated Learning: A Meta-Learning Approach (2020, arXiv (Cornell University))
  • Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity (2022, IEEE Journal on Selected Areas in Information Theory)
  • Exploiting Shared Representations for Personalized Federated Learning (2021, arXiv (Cornell University))
  • FedAvg with Fine Tuning: Local Updates Lead to Representation Learning (2022, arXiv (Cornell University))
  • Federated Learning with Compression: Unified Analysis and Sharp Guarantees (2020, arXiv (Cornell University))

Mokhtari has collaborated with several frequent coauthors, such as Ruichen Jiang, Hamed Hassani, Sanjay Shakkottai, Qiujiang Jin, and Liam Collins.

Their work appears regularly in notable publication venues, including:

  • arXiv (Cornell University)
  • SIAM Journal on Optimization
  • Mathematical Programming
  • IEEE Journal on Selected Areas in Information Theory
  • Proceedings of the IEEE

In addition to journal articles, Aryan Mokhtari has contributed to book publications. They have a forthcoming book titled Conditional Gradient Methods to be published by the Society for Industrial and Applied Mathematics in 2025.

Best Publications

  • Personalized Federated Learning: A Meta-Learning Approach

    Alireza Fallah;Aryan Mokhtari;Asuman E. Ozdaglar

  • Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach

    Alireza Fallah;Aryan Mokhtari;Asuman E. Ozdaglar

  • FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization.

    Amirhossein Reisizadeh;Aryan Mokhtari;Hamed Hassani;Ali Jadbabaie

  • FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

    Amirhossein Reisizadeh;Aryan Mokhtari;Hamed Hassani;Ali Jadbabaie

  • RES: Regularized Stochastic BFGS Algorithm

    Aryan Mokhtari;Alejandro Ribeiro

  • Network Newton Distributed Optimization Methods

    Aryan Mokhtari;Qing Ling;Alejandro Ribeiro

  • Online optimization in dynamic environments: Improved regret rates for strongly convex problems

    Aryan Mokhtari;Shahin Shahrampour;Ali Jadbabaie;Alejandro Ribeiro

  • Global convergence of online limited memory BFGS

    Aryan Mokhtari;Alejandro Ribeiro

  • DSA: decentralized double stochastic averaging gradient algorithm

    Aryan Mokhtari;Alejandro Ribeiro

  • A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach.

    Aryan Mokhtari;Asuman E. Ozdaglar;Sarath Pattathil

  • A Class of Prediction-Correction Methods for Time-Varying Convex Optimization

    Andrea Simonetto;Aryan Mokhtari;Alec Koppel;Geert Leus

  • DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers

    Aryan Mokhtari;Wei Shi;Qing Ling;Alejandro Ribeiro

  • An Exact Quantized Decentralized Gradient Descent Algorithm

    Amirhossein Reisizadeh;Aryan Mokhtari;Hamed Hassani;Ramtin Pedarsani

  • Decentralized Quasi-Newton Methods

    Mark Eisen;Aryan Mokhtari;Alejandro Ribeiro

  • A Decentralized Second-Order Method with Exact Linear Convergence Rate for Consensus Optimization

    Aryan Mokhtari;Wei Shi;Qing Ling;Alejandro Ribeiro

  • On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms

    Alireza Fallah;Aryan Mokhtari;Asuman E. Ozdaglar

  • Decentralized quadratically approximated alternating direction method of multipliers

    Aryan Mokhtari;Wei Shi;Qing Ling;Alejandro Ribeiro

  • Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity.

    Amirhossein Reisizadeh;Isidoros Tziotis;Hamed Hassani;Aryan Mokhtari

  • FedAvg with Fine Tuning: Local Updates Lead to Representation Learning

    Unknown

  • Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization

    Andrea Simonetto;Alec Koppel;Aryan Mokhtari;Geert Leus

  • A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach

    Aryan Mokhtari;Asuman Ozdaglar;Sarath Pattathil

  • Direct runge-kutta discretization achieves acceleration

    Jingzhao Zhang;Aryan Mokhtari;Suvrit Sra;Ali Jadbabaie

  • Exploiting Shared Representations for Personalized Federated Learning

    Liam Collins;Hamed Hassani;Aryan Mokhtari;Sanjay Shakkottai

  • Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap

    Aryan Mokhtari;S. Hamed Hassani;Amin Karbasi

Frequent Co-Authors

Alejandro Ribeiro
Alejandro Ribeiro University of Pennsylvania
Qing Ling
Qing Ling Sun Yat-sen University
Sanjay Shakkottai
Sanjay Shakkottai The University of Texas at Austin
Geert Leus
Geert Leus Delft University of Technology
Xin Wang
Xin Wang Fudan University
Georgios B. Giannakis
Georgios B. Giannakis University of Minnesota
Peilin Zhao
Peilin Zhao Tencent (China)

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