Kostas Papakonstantinou

AI Foundations

  • Uncertainty quantification
  • Decision making under uncertainty
  • Stochastic control
  • Partially Observable Markov Decision Processes
  • Deep reinforcement learning
  • Bayesian analysis
  • MCMC and other sampling methods
  • Optimization techniques
  • Nonlinear filtering

Applied AI Research Thrusts

  • Computational mechanics
  • Asset management
  • Infrastructure management
  • Rare events quantification
  • Damage detection
  • Autonomous operations
  • Model updating
  • Dimensionality reduction

Externally Funded AI Projects

  • CAREER: Optimal engineering decision-making under uncertainties for enhanced structural life-cycle, Funding Agency: NSF
  • AI-enabled fiscally constrained life-cycle asset management for infrastructure systems, Funding Agency: USDOT-CIAMTIS
  • Deep reinforcement learning for multi-asset infrastructure management incorporating traffic operations adaptations and control: Funding Agency: USDOT-CIAMTIS
  • Strategic Prioritization and Planning for Multi-Asset Transportation Infrastructure Maintenance, Rehabilitation, and Improvements: Phase 2 - Data-driven Decisions from Continuous Monitoring: Funding Agency: USDOT-CIAMTIS

AI-Related Courses

  • CE 566: Uncertainty and Reliability in Civil Engineering
  • CE 597: Computational Analysis of Randomness in Engineering



Selected Recent Publications

  1. A Scaled Spherical Simplex Filter (S3F) with a decreased n+2 sigma points set size and equivalent 2n+1 Unscented Kalman Filter (UKF) accuracy
    KG Papakonstantinou, M Amir, GP Warn
    Mechanical Systems and Signal Processing 163, 107433, 2022
  2. Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints
    CP Andriotis, KG Papakonstantinou
    Reliability Engineering & System Safety 212, 107551, 2021
  3. Geometrically exact hybrid beam element based on nonlinear programming
    CM Lyritsakis, CP Andriotis, KG Papakonstantinou
    International Journal for Numerical Methods in Engineering 122 (13), 3273-3299, 2021
  4. Optimal inspection and maintenance planning for deteriorating structures through dynamic Bayesian networks and Markov decision processes
    PG Morato, KG Papakonstantinou, CP Andriotis, JS Nielsen, P Rigo
    arXiv preprint arXiv:2009.04547
  5. Hamiltonian MCMC methods for estimating rare events probabilities in high-dimensional problems
    KG Papakonstantinou, H Nikbakht
    arXiv preprint arXiv:2007.00180
  6. Value of structural health information in partially observable stochastic environments
    CP Andriotis, KG Papakonstantinou, EN Chatzi
    arXiv preprint arXiv:1912.12534
  7. Managing engineering systems with large state and action spaces through deep reinforcement learning
    CP Andriotis, KG Papakonstantinou
    Reliability Engineering & System Safety 191, 106483, 2019

headshot of a man

Kostas Papakonstantinou
Associate Professor of Civil Engineering



The Center for Artificial Intelligence Foundations and Engineered Systems (CAFE), pronounced café, brings together expertise from 75 researchers representing 24 academic units across Penn State with the goal of developing cross-disciplinary interactions. The center’s focus is on accelerating advances by synergistically advancing AI foundations and the techniques to deploy them efficiently toward applications focused on engineered and defense systems. CAFE provides opportunities for research partnerships, faculty/student recruitment, and technology transition to practice.

Center for Artificial Intelligence Foundations and Engineered Systems

The Pennsylvania State University

W323 Westgate Building

University Park, PA 16802