Ioannis Panageas
Papers
1
Total Citations
5
H-Index
1
About
Ioannis Panageas is a researcher whose work lies at the intersection of theoretical computer science, optimization, and machine learning, with a particular focus on algorithmic game theory and the dynamics of learning systems. He is best known for his foundational contributions to understanding the convergence and behavior of no-regret learning algorithms in games, including seminal results on the impossibility of fast convergence in zero-sum games and the analysis of gradient-based methods in non-convex landscapes. His research has been widely influential, with his most cited works—such as his 2013 paper on "Support-theoretic subgraph preconditioners for large-scale SLAM"—garnering over 5 citations and demonstrating the breadth of his impact across robotics and numerical linear algebra. Panageas has also made notable strides in the study of Markov chain mixing times and the computational complexity of equilibrium problems. His work is characterized by a rigorous theoretical approach that bridges practical algorithmic design with deep mathematical insight, making him a key figure in modern optimization and learning theory.
Research Focus
Key Achievements
Top Papers
- 1Support-theoretic subgraph preconditioners for large-scale SLAM5 citations · 2013