Necdet Aybat

Foundational AI:

  • design of new optimization algorithms
  • federated learning
  • distributed multi-agent optimization

Applied AI Research

  • matrix decomposition for image-video processing

Externally Funded AI Projects

  • ONR Grant N00014-21-1-2271, “Collaborative Proposal: Robust Primal-Dual Algorithms for Saddle Point Problems with Applications to Multi-Agent Systems”, N. S. Aybat (PI) and M. Gurbuzbalaban (PI), 04/01/2021 - 03/31/2024 ($652,648)
  • NSF Grant CMMI-1635106, “Decentralized power flow optimization on electricity grids via distributed consensus methods”, N. S. Aybat (PI), 09/01/2016 - 08/31/2020 ($235,852)
  • ARO Grant W911NF-17-1-0298, “Decentralized methods for multi-agent problems over networks,” N. S. Aybat (PI), 07/01/17 - 03/31/18 ($60,000)
  • NSF Grant CMMI-1400217, “Resolving Parametric Misspecification: Joint Schemes for Computation and Learning”, U. V. Shanbhag (PI), N. S. Aybat (co-PI), 08/01/2014 - 07/31/2017 ($300,000)

AI-related Courses

  • IE/EE 585 Convex Optimization
  • IE 597 Modern Data Analytics


Related Recent Publications

  1. E. Yazdandoost Hamedani and N. S. Aybat, “A Primal-Dual Algorithm with Line Search for General Convex-Concave Saddle Point Problems,” SIAM Journal on Optimization (SIOPT), 31(2), 1299–1329 (2021).
  2. S. Davanloo Tajbakhsh, N. S. Aybat, and Enrique del Castillo, “On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach” Journal of Machine Learning Research (JMLR), 21(217), 1−41 (2020).
  3. N. S. Aybat, A. Fallah, M. Gürbüzbalaban, A. Ozdaglar, “Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions,” SIAM Journal on Optimization, 30(1), 717-751 (2019).
  4. N. S. Aybat, E. Yazdandoost Hamedani, “A Distributed ADMM-like Method for Resource Sharing over Time-varying Directed Networks," SIAM Journal on Optimization, 29(4), 3036–3068 (2019).
  5. J. Wang, M. Ashour, C. M. Lagoa, N. S. Aybat, H. Che, “A fully distributed traffic allocation algorithm for nonconcave utility maximization in connectionless communication networks," Automatica, 109, 108506 (2019).
  6. N. S. Aybat, A. Fallah, M. Gürbüzbalaban, A. Ozdaglar, “A Universally Optimal Multistage Accelerated Stochastic Gradient Method,” Advances in Neural Information Processing Systems (NeurIPS), 32, pp. 8523-8534 (2019).
  7. M. Ashour, C. M. Lagoa, and N. S. Aybat. “Lp Quasi-norm Minimization,” 2019 53rd Asilomar Conference on Signals, Systems, and Computers, IEEE, pp. 726-730 (2019).

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Necdet Aybat
Associate Professor of Industrial and Manufacturing 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

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University Park, PA 16802