Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Distributed Subgradient Methods for Multi-Agent Optimization
Countries citing papers authored by Asuman Ozdaglar
Since
Specialization
Citations
This map shows the geographic impact of Asuman Ozdaglar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Asuman Ozdaglar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Asuman Ozdaglar more than expected).
This network shows the impact of papers produced by Asuman Ozdaglar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Asuman Ozdaglar. The network helps show where Asuman Ozdaglar may publish in the future.
Co-authorship network of co-authors of Asuman Ozdaglar
This figure shows the co-authorship network connecting the top 25 collaborators of Asuman Ozdaglar.
A scholar is included among the top collaborators of Asuman Ozdaglar based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Asuman Ozdaglar. Asuman Ozdaglar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Acemoğlu, Daron, Ali Makhdoumi, Azarakhsh Malekian, & Asuman Ozdaglar. (2025). When Big Data Enables Behavioral Manipulation. RePEc: Research Papers in Economics. 7(1). 19–38.1 indexed citations
Mokhtari, Aryan, et al.. (2020). A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach.. International Conference on Artificial Intelligence and Statistics. 1497–1507.18 indexed citations
4.
Farnia, Farzan & Asuman Ozdaglar. (2020). Do GANs always have Nash equilibria. International Conference on Machine Learning. 3029–3039.13 indexed citations
Fallah, Alireza, Aryan Mokhtari, & Asuman Ozdaglar. (2020). On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms. International Conference on Artificial Intelligence and Statistics. 1082–1092.12 indexed citations
Mokhtari, Aryan, et al.. (2019). Proximal Point Approximations Achieving a Convergence Rate of O(1/k) for Smooth Convex-Concave Saddle Point Problems: Optimistic Gradient and Extra-gradient Methods.. arXiv (Cornell University).2 indexed citations
Gürbüzbalaban, Mert, Asuman Ozdaglar, Pablo A. Parrilo, & N. Denizcan Vanli. (2017). When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent. DSpace@MIT (Massachusetts Institute of Technology). 30. 6999–7007.4 indexed citations
13.
Makhdoumi, Ali & Asuman Ozdaglar. (2017). Convergence Rate of Distributed ADMM over Networks. DSpace@MIT (Massachusetts Institute of Technology).126 indexed citations
Lee, Christina, Asuman Ozdaglar, & Devavrat Shah. (2014). Solving for a Single Component of the Solution to a Linear System, Asynchronously. arXiv (Cornell University).1 indexed citations
16.
Lee, Christina, Asuman Ozdaglar, & Devavrat Shah. (2013). Computing the Stationary Distribution Locally. DSpace@MIT (Massachusetts Institute of Technology). 26. 1376–1384.8 indexed citations
Acemoğlu, Daron & Asuman Ozdaglar. (2003). Flow Control, Routing, and Performance from Service Provider Viewpoint.14 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.