Books and Chapters

• Kernel-based Approximation Methods Using Matlab

  G. Fasshauer, M. McCourt
  World Scientific Press, 2015.
  [Link]

• Stable likelihood computation for Gaussian random fields

  M. McCourt, G. Fasshauer
  Applications of Harmonic Analysis to Function Spaces, Differential Equations, and Data Science, I. Pesenson, Q. T. Le Gia, A. Mayeli, H. Mhaskar, D.-X. Zhou, Eds., 917-943, 2017.
  [Link] [Preprint]

• Anisotropic Weights for RBF-PU Interpolation with Subdomains of Variable Shapes

  R. Cavoretto, A. De Rossi, G. Fasshauer, M. McCourt, E. Perracchione
  In: Radu F., Kumar K., Berre I., Nordbotten J., Pop I. (eds) Numerical Mathematics and Advanced Applications ENUMATH 2017. Lecture Notes in Computational Science and Engineering, 126:93-101, 2019. Springer, Cham.
  [Link] [Preprint]

Articles, Workshops and Proceedings

• Sample-Efficient Search for Satisfactory OLED Designs

  K. Mohamaddi, M. McCourt, P. Leu
  1st Annual AAAI Workshop on AI to Accelerate Science and Engineering, 2022
  [Link]

• Team Composition Revisited: A Team Member Attribute Alignment Approach

  K. Emich, L. Lu, A. Ferguson, R. Peterson, M. McCourt
  Organizational Research Methods, 2021
  [Link]

• Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design

  G. Malkomes, B. Cheng, E. Lee, M. McCourt
  Proceedings of the Thirty-Eight Annual International Conference on Machine Learning, PMLR 139:7423-7434, 2021
  [Link]

• Quasi-Monte Carlo Software

  S. C. T. Choi, F. J. Hickernell, R. Jagadeeswaran, M. McCourt, A. G. Sorokin
  Proceedings of the 14th International Conference in Monte Carlo & Quasi-Monte Carlo Methods in Scientific Computing, in press
  [Preprint]

• Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020

  R. Turner, D. Eriksson, M. McCourt, J. Kiili, E. Laaksonen, Z. Xu, I. Guyon
  Proceedings of Machine Learning Research, 2021.
  [Link]

• Bayesian optimization with approximate set kernels

  J. Kim, M. McCourt, T. You, S. Kim, S. Choi
  Machine Learning, 110(5):857-879, 2021.
  [Link] [Preprint]

• Discovering near-perfect broadband and broad angle antireflection surfaces for optoelectronics by machine learning

  S. Haghanifar, M. McCourt, B. Cheng, J. Wuenschell, P. Ohodnicki, P. Leu
  Optica, 7(7):784-789, 2020.
  [Link]

• Efficient rollout strategies for Bayesian optimization

  D. Bindel, E. Lee, B. Cheng, M. McCourt, D. Eriksson
  Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, ID-124, 2020.
  [Link]

• On variable and random shape Gaussian interpolations

  S. Chiu, L. Ling, M. McCourt
  Mathematics and Computation, 377:125159, 2020.
  [Link] [Preprint]

• Sampling humans for optimizing preferences in Coloring Artwork

  M. McCourt, I. Dewancker
  ICML Workshop on Human in the Loop Learning, 2019.
  [Link]

• Creating glasswing butterfly-inspired durable antifogging superomniphobic supertransmissive, superclear nanostructured glass through Bayesian learning and optimization

  S. Haghanifar, M. McCourt, B. Cheng, J. Wuenschell, P. Ohodnicki, P. Leu
  Materials Horizons, 6(8):1632-1642, 2019.
  [Link] [AI in Climate Change 2019 workshop] [Sequential Decision Making 2019 workshop]

• Practical Bayesian optimization in the presence of outliers

  R. Martinez-Cantin, K. Tee, M. McCourt
  Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:1722-1731, 2018.
  [Link]

• An augmented MFS approach for brain activity reconstruction

  G. Ala, G. Fasshauer, E. Francomano, S. Ganci, M. McCourt
  Mathematics and Computers in Simulation, 141:3-15, 2017.
  [Link] [Preprint]

• A strategy for ranking optimization methods using multiple criteria

  I. Dewancker, M. McCourt, S. Clark, P. Hayes, A. Johnson, G. Ke
  JMLR Workshop and Conference Proceedings, 64:11-20, 2016.
  [Link]

• A meshfree solver for the MEG forward problem

  G. Ala, G. Fasshauer, E. Francomano, S. Ganci, M. McCourt
  IEEE Transactions on Magnetics, 51(3):1-4, 2015.
  [Link] [Preprint]

• The method of fundamental solutions in solving coupled boundary value problems for M/EEG

  G. Ala, G. Fasshauer, E. Francomano, S. Ganci, M. McCourt
  SIAM Journal on Scientific Computing, 37(4):B570-B590, 2015.
  [Link] [Preprint]

• Sparse matrix-matrix products executed through colorings

  M. McCourt, B. Smith, H. Zhang
  SIAM Journal on Matrix Analysis and Applications, 36(1):90-109, 2015.
  [Link] [Preprint]

• An introduction to the Hilbert-Schmidt SVD using iterated Brownian bridge kernels

  R. Cavoretto, G. Fasshauer, M. McCourt
  Numerical Algorithms, 68(2):393-422, 2015.
  [Link] [Preprint]

• Multiphysics simulations: Challenges and opportunities

  D. Keyes et al
  The International Journal of High Performance Computing Applications, 27(1), 4-83. 2013.
  [Link]

• Using Gaussian eigenfunctions to solve boundary value problems

  M. McCourt
  Advances in Applied Mathematics and Mechanics, 5:569-594, 2013.
  [Link] [Preprint]

• Improving parallel scalability for edge plasma transport simulations with neutral gas species

  M. McCourt, T. D. Rognlien, H. Zhang, L. C. McInnes
  Computational Science and Discovery, 5:014012, 2012.
  [Link]

• Stable evaluation of Gaussian RBF interpolants

  G. Fasshauer, M. McCourt
  SIAM Journal on Scientific Computing, 34(2):A737-A762, 2012.
  [Link] [Preprint]

• Pseudorandom numbers for conformal measures

  G. Fasshauer, M. McCourt
  SIAM Journal on Scientific Computing, 34(2):A737-A762, 2012.
  [Link] [Preprint]

• Spectral methods for resolving spike dynamics in the Gierer-Meinhardt model

  M. McCourt, N. Dovidio, M. Gilbert
  Communications in Computational Physics, 3:659-678, 2008.
  [Link] [Reprint]

Patents

• Systems and methods for tuning hyperparameters of a model and advanced curtailment of a training of the model

  M. McCourt, T. Jackle-Spriggs, B. Hsu, S. Howey, N. Vance, J. Blomo, P. Hayes, S. Clark
  [11157812]

• Systems and methods for an accelerated and enhanced tuning of a model based on prior model tuning data

  M. McCourt, B. Hsu, P. Hayes, S. Clark
  US 2021/0142224 A1, 2021-05-21, Pending

• Systems and methods for implementing an intelligent machine learning optimization platform for multiple tuning criteria

  B. Cheng, O. Kim, M. McCourt, P. Hayes, S. Clark
  [10528891] [10558934] [10621514] [10740695]

• Systems and methods implementing an intelligent machine learning tuning system providing multiple tuned hyperparameter solutions

  K. Tee, M. McCourt, P. Hayes, S. Clark
  US 2019/0156229 A1, 2019-05-23, Pending

• Systems and methods implementing an intelligent optimization platform

  P. Hayes, M. McCourt, A. Johnson, G. Ke, S. Clark
  [10217061] [10607159]

• Systems and methods for an accelerated tuning of hyperparameters of a model using a machine learning-based tuning service

  M. McCourt, B. Hsu, P. Hayes, S. Clark
  US 2020/0019888 A1, 2020-01-16, Pending