Stratis Skoulakis
Scholar

Stratis Skoulakis

Google Scholar ID: Juo2Tk8AAAAJ
Aarhus University
Online LearningAlgorithmic Game TheoryMachine LearningOptimizationAlgorithms
Citations & Impact
All-time
Citations
633
 
H-index
13
 
i10-index
15
 
Publications
20
 
Co-authors
22
list available
Resume (English only)
Academic Achievements
  • Publications:
  • - ICML 2023: Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees
  • - NeurIPS 2022: Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update
  • - AFT 2021: Dynamical analysis of the EIP-1559 Ethereum fee market.
  • - COLT 2023: STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games
  • - STOC 2021: The complexity of constrained min-max optimization.
  • - ICLR 2024: Efficient Efficient Continual Finite-Sum Minimization
  • - NeurIPS 2023: Maximum independent set: Self-training through dynamic programming
  • - ICML 2024: Learning to Remove Cuts in Integer Linear Programming
  • Awards: Received the Villum Young Investigator Award (9 million DKK / 1.2 million euros) in January 2025 for the project 'Developing an online learning approach to multi-agent systems: Game dynamics and equilibria'.
Research Experience
  • Work Experience: Postdoctoral researcher at École Polytechnique Fédérale de Lausanne and Singapore University of Technology and Design. Research Projects: Combining ideas from game theory, machine learning, and optimization to understand dynamics in crucial settings such as auctions, markets, and blockchain systems (e.g., work on Ethereum transaction fees). Recent research directions include developing efficient optimization methods for training machine learning models and exploring the applications of deep learning techniques to combinatorial optimization.
Education
  • PhD: National Technical University of Athens, Advisor information not provided, Specialization: Algorithmic game theory.
Background
  • Research Interests: Game theory, machine learning, and optimization. Professional field: Algorithmic game theory. Brief Introduction: Assistant Professor in the Department of Computer Science at Aarhus University, and a member of the Computational Complexity and Game Theory group.