H-Index & Metrics Top Publications
Asuman Ozdaglar

Asuman Ozdaglar

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology H-index 56 Citations 20,298 175 World Ranking 898 National Ranking 388

Research.com Recognitions

Awards & Achievements

The Science Academy Society of Turkey - Bilim Akademisi Electrical-Electronic Engineering

Overview

What is she best known for?

The fields of study she is best known for:

  • Statistics
  • Computer network
  • Mathematical optimization

Asuman Ozdaglar mostly deals with Mathematical optimization, Subgradient method, Rate of convergence, Convergence and Optimization problem. Asuman Ozdaglar has included themes like Distributed algorithm, Multi-agent system and Convex optimization in her Mathematical optimization study. Her Subgradient method research is multidisciplinary, incorporating elements of Duality, Unconstrained optimization, Regular polygon, Slater's condition and Constraint.

Her research in Rate of convergence tackles topics such as Computation which are related to areas like Acceleration, Proximal Gradient Methods and Differentiable function. Her Convergence research incorporates elements of Voter model, Asynchronous communication and Artificial intelligence. Her research investigates the connection with Optimization problem and areas like Stochastic process which intersect with concerns in Sequence learning, Bounded function, Information cascade, Path and Integer programming.

Her most cited work include:

  • Distributed Subgradient Methods for Multi-Agent Optimization (1754 citations)
  • Convex Analysis and Optimization (1651 citations)
  • Constrained Consensus and Optimization in Multi-Agent Networks (1386 citations)

What are the main themes of her work throughout her whole career to date?

The scientist’s investigation covers issues in Mathematical optimization, Rate of convergence, Mathematical economics, Convergence and Microeconomics. Her study in Mathematical optimization is interdisciplinary in nature, drawing from both Distributed algorithm, Function and Convex optimization. Her Rate of convergence study integrates concerns from other disciplines, such as Convex function, Regular polygon, Computation and Applied mathematics.

Her work deals with themes such as Class, Bounded function and Artificial intelligence, which intersect with Mathematical economics. Her study connects Algorithm and Convergence. Asuman Ozdaglar has researched Oligopoly in several fields, including Telecommunications network and Arbitrarily large.

She most often published in these fields:

  • Mathematical optimization (30.99%)
  • Rate of convergence (17.46%)
  • Mathematical economics (12.96%)

What were the highlights of her more recent work (between 2017-2021)?

  • Applied mathematics (8.45%)
  • Rate of convergence (17.46%)
  • Mathematical optimization (30.99%)

In recent papers she was focusing on the following fields of study:

Her primary areas of investigation include Applied mathematics, Rate of convergence, Mathematical optimization, Saddle point and Nash equilibrium. The study incorporates disciplines such as Proximal point method, Convergence, Quadratic equation, Condition number and Robustness in addition to Applied mathematics. Her Convergence study which covers Function that intersects with Empirical risk minimization.

The Rate of convergence study combines topics in areas such as Optimization problem, Convex function, System of linear equations and Convex optimization. Her Mathematical optimization research is multidisciplinary, incorporating perspectives in Stability, Enhanced Data Rates for GSM Evolution and Network formation. Her Saddle point study also includes fields such as

  • Iterated function that intertwine with fields like Combinatorics and Discrete mathematics,
  • Regular polygon together with Upper and lower bounds, Ergodic theory, Bounded set and Compact space.

Between 2017 and 2021, her most popular works were:

  • A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach (60 citations)
  • Personalized Federated Learning: A Meta-Learning Approach (59 citations)
  • Informational Braess' Paradox: The Effect of Information on Traffic Congestion (35 citations)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Distributed Subgradient Methods for Multi-Agent Optimization

A. Nedic;A. Ozdaglar.
IEEE Transactions on Automatic Control (2009)

2645 Citations

Convex Analysis and Optimization

Dimitri P. Bertsekas;Angelia Nedić;Asuman E. Ozdaglar.
(2003)

2042 Citations

The Network Origins of Aggregate Fluctuations

Daron Acemoglu;Vasco M. Carvalho;Asuman E. Ozdaglar;Alireza Tahbaz-Salehi.
Econometrica (2012)

1781 Citations

Systemic risk and stability in financial networks

Daron Acemoglu;Asuman Ozdaglar;Alireza Tahbaz-Salehi.
The American Economic Review (2015)

1595 Citations

Constrained Consensus and Optimization in Multi-Agent Networks

A. Nedic;A. Ozdaglar;P.A. Parrilo.
IEEE Transactions on Automatic Control (2010)

1587 Citations

Bayesian Learning in Social Networks

Daron Acemoglu;Munther A. Dahleh;Ilan Lobel;Asuman Ozdaglar.
The Review of Economic Studies (2011)

847 Citations

On distributed averaging algorithms and quantization effects

A. Nedic;A. Olshevsky;A. Ozdaglar;J.N. Tsitsiklis.
conference on decision and control (2008)

627 Citations

Opinion Dynamics and Learning in Social Networks

Daron Acemoglu;Asuman E. Ozdaglar.
Dynamic Games and Applications (2011)

594 Citations

Spread of (mis)information in social networks

Daron Acemoglu;Asuman E. Ozdaglar;Ali Parandehgheibi.
Games and Economic Behavior (2010)

472 Citations

Opinion Fluctuations and Disagreement in Social Networks

Daron Acemoğlu;Giacomo Como;Fabio Fagnani;Asuman Ozdaglar.
Mathematics of Operations Research (2013)

371 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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