Jason M. Altschuler
- Artificial Intelligence
- Statistics and Probability top 10%
- Computational Theory and Mathematics
- Applied Mathematics
- Computational Mechanics
- Co-authors
- Philippe RigolletJonathan WeedJonathan Niles‐WeedPablo A. ParriloGang FuAfshin RostamizadehVahab MirrokniMorteza Zadimoghaddam
- Topics
- Sparse and Compressive Sensing Techniques (4 papers)Advanced Optimization Algorithms Research (3 papers)Complexity and Algorithms in Graphs (3 papers)
- Partner nations
- United States
In The Last Decade
Jason M. Altschuler
14 papers receiving 88 citations
Peers
Comparison fields: 5 of 46
- Artificial Intelligence 35
- Statistics and Probability 25
- Computational Theory and Mathematics 22
- Applied Mathematics 22
- Computational Mechanics 16
Countries citing papers authored by Jason M. Altschuler
This map shows the geographic impact of Jason M. Altschuler'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 Jason M. Altschuler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason M. Altschuler more than expected).
Fields of papers citing papers by Jason M. Altschuler
This network shows the impact of papers produced by Jason M. Altschuler. 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 Jason M. Altschuler. The network helps show where Jason M. Altschuler may publish in the future.
Co-authorship network of co-authors of Jason M. Altschuler
This figure shows the co-authorship network connecting the top 25 collaborators of Jason M. Altschuler. A scholar is included among the top collaborators of Jason M. Altschuler 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 Jason M. Altschuler. Jason M. Altschuler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 5 | |
| 4 | 4 | |
| 5 | 0 | |
| 6 | 4 | |
| 7 | 1 | |
| 8 | 2 | |
| 9 | 14 | |
| 10 | 17 | |
| 11 | 2 | |
| 12 | 1 | |
| 13 | Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration | 28 |
| 14 | 12 | |
| 15 | 1 | |
| 16 | 1 |
About Jason M. Altschuler
Jason M. Altschuler is a scholar working on Computational Mathematics, Numerical Analysis and Statistics and Probability, having authored 16 papers that have together received 93 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (4 papers), Advanced Optimization Algorithms Research (3 papers) and Complexity and Algorithms in Graphs (3 papers). The work is most often cited by research in Computational Mathematics (2 citations), Statistics and Probability (25 citations) and Applied Mathematics (22 citations). Jason M. Altschuler has collaborated with scholars based in United States. Frequent co-authors include Philippe Rigollet, Jonathan Weed, Jonathan Niles‐Weed, Pablo A. Parrilo, Gang Fu, Afshin Rostamizadeh, Vahab Mirrokni, Morteza Zadimoghaddam, Aditya Bhaskara and Lani F. Wu. Their work appears in journals such as IEEE Transactions on Information Theory, Journal of the ACM and Mathematical Programming.
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.