Max Simchowitz

1.7k citations
18 papers · 356 · h-index 7

Impact in

Papers in

Max Simchowitz

18 papers receiving 333 citations

Peers

Max Simchowitz
Comparison fields: 5 of 60
  • Computational Mathematics 12
  • Numerical Analysis 55
  • Computational Mechanics 159
  • Acoustics and Ultrasonics 4
  • Artificial Intelligence 149
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Countries citing papers authored by Max Simchowitz

Since Specialization
Citations

This map shows the geographic impact of Max Simchowitz'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 Max Simchowitz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Max Simchowitz more than expected).

Fields of papers citing papers by Max Simchowitz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Max Simchowitz. 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 Max Simchowitz. The network helps show where Max Simchowitz may publish in the future.

Co-authors

The 25 scholars most cited alongside Max Simchowitz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Max Simchowitz Line = papers co-authored together Max Simchowitz links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1
Gradient Descent Only Converges to Minimizers
2016158
2
Low-rank solutions of linear matrix equations via procrustes flow
201671
3 201970
4 202311
5 201810
6 20248
7
Improper Learning for Non-Stochastic Control
20207
8 20235
9 20245
10
The Gradient Complexity of Linear Regression
20202
11 20172
12
Bayesian decision-making under misspecified priors with applications to meta-learning
20211
13
Task-Optimal Exploration in Linear Dynamical Systems
20211
14 20241
15 20211
16 20251
17 20221
18
Statistical Complexity and Regret in Linear Control
20211

About Max Simchowitz

Max Simchowitz is a scholar working on Artificial Intelligence, Management Science and Operations Research, Control and Systems Engineering, Computer Networks and Communications and Computational Mechanics, having authored 18 papers that have together received 356 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (7 papers), Reinforcement Learning in Robotics (3 papers), Sparse and Compressive Sensing Techniques (3 papers), Advanced Control Systems Optimization (2 papers), Robotic Mechanisms and Dynamics (2 papers), Adversarial Robustness in Machine Learning (2 papers), Robotic Path Planning Algorithms (2 papers) and Optimization and Search Problems (2 papers). The work is most often cited by research in Computational Mathematics (12 citations), Numerical Analysis (55 citations), Computational Mechanics (159 citations), Acoustics and Ultrasonics (4 citations) and Artificial Intelligence (149 citations). Max Simchowitz has collaborated with scholars based in United States, Singapore and United Kingdom. Frequent co-authors include Benjamin Recht, Jason D. Lee, Michael I. Jordan, Stephen Tu, Mahdi Soltanolkotabi, Ross Boczar, Ioannis Panageas, Georgios Piliouras, Russ Tedrake and Elad Hazan. Their work appears in journals such as Mathematical Programming, Mathematics of Operations Research, Operations Research, Conference on Learning Theory and eScholarship (California Digital Library).

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.

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