Assistant Professor


  • Grigoryeva, L. and Ortega, J.-P. Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems. Preprint.
  • Badescu, A., Grigoryeva, L., and Ortega, J.-P. Option pricing and hedging with one-step
    Kalman filtered factors in non-affine stochastic volatility model.
  • Grigoryeva, L., Ortega, J.-P. Ridge regression with homoscedastic residuals: generalization error with estimated parameters. Preprint.
  • Grigoryeva, L., Henriques, J., and Ortega, J.-P. Quantitative evaluation of the performance of discrete-time reservoir computers in the forecasting, filtering, and reconstruction of stochastic stationary signals. Preprint.
  • Bauwens, L., Grigoryeva, L., and Ortega, J.-P. Non-scalar GARCH models: Composite likelihood estimation and empirical model comparisons. Preprint.

Statistical Modeling

  • Grigoryeva, L., Ortega, J.-P., Peresetsky A. 2017. Volatility forecasting using global stochastic financial trends extracted from non-synchronous data. Econometrics and Statistics, Volume 5, P. 67–82. (with Supplementary material, 10 pp). doi: 10.1016/j.ecosta.2017.01.003
  • Grigoryeva, L., Henriques, J., Larger, L., Ortega, J.-P. 2016. Nonlinear memory capacity of parallel time-delay reservoir computers in the processing of multidimensional signals. Neural Computation. Volume 28 (7), P. 1411–1451. Paper.
  • Grigoryeva, L., Henriques, J., Ortega, J.-P. 2016. Reservoir computing: information processing of stationary signals. Proceedings of the 19th IEEE International Conference on Computational Science and Engineering.  doi 10.1109/CSE-EUC-DCABES.2016.231. Paper presented with the Best Paper Award. Paper. Award
  • Grigoryeva, L., Henriques, J., Larger, L., Ortega, J.-P. 2016. Time-delay reservoir computers and high-speed information processing capacity. Proceedings of the 19th IEEE International Conference on Computational Science and Engineering. doi 10.1109/CSE-EUC-DCABES.2016.230. Paper
  • Bauwens, L., Grigoryeva, L., Ortega, J.-P. 2016. Estimation and empirical performance of non-scalar dynamic conditional correlation models. Computational Statistics & Data Analysis. Volume 100, P. 17–36.
    Paper. Poster. Matlab Code.
  • Grigoryeva, L., Henriques, J., Larger, L., Ortega, J.-P. 2015. Optimal nonlinear information processing capacity in delay-based reservoir computers. 38 pages. Scientific Reports, 5(12858), 1-11; doi: 10.1038/ srep12858. Nature Publishing Group.
    Paper. Supplementary Material. Poster.
  • Grigoryeva, L., Ortega, J.-P. 2014. Hybrid forecasting with estimated temporally aggregated linear processes. Journal of Forecasting, Volume 33, P. 577–595.
  • Grigoryeva, L., Ortega, J.-P. 2015. Asymptotic forecasting error evaluation for estimated temporally aggregated linear processes. International Journal of Computational Economics and Econometrics (IJCEE), Volume 5, No. 3.
    Paper. Matlab Code.
  • Grigoryeva, L., Henriques, J., Larger, L., Ortega, J.-P. 2013. Stochastic nonlinear time series forecasting using time-delay reservoir computers: performance and universality. Neural Networks, Volume 55, P. 59–71.
    Paper. Poster. Included in the “Best of Computing” 2014 list of Computing Reviews. badge_ComputingReviews

Physiological Signal Treatment and Analysis

  • Henriques, J., Pazart, L., Grigoryeva, L., Muzart, E., Beaussant, Y., Haffen, E.,  Moulin, T., Aubry, R., Ortega, J.-P., and Gabriel, D. 2016. Bedside evaluation of the functional organization of the auditory cortex in patients with disorders of conciousness. PLOS ONE. DOI: 10.1371/journal.pone.0146788. Paper.
  • Gabriel, D., Henriques, J., Comte, A., Grigoryeva, L., Ortega, J.-P., Cretin, E., Haffen, E., Moulin, T.,Pazart, L., Aubry, R. 2015. Substitute or complement? Defining the relative place of EEG and fMRI in the detection of voluntary brain reactions. Neuroscience, 290, P. 435-444. Paper.
  • Henriques, J., Gabriel, D., Grigoryeva, L., Haffen, E., Moulin, T., Aubry, R., Pazart, L., Ortega, J.-P. 2014. Protocol design challenges in the detection of awareness in aware subjects using EEG signals. Clinical EEG & Neuroscience. DOI: 10.1177/1550059414560397. Paper.
  • Gabriel, D., Comte, A., Henriques, J., Magnin, E., Grigoryeva, L., Ortega, J.-P., Haffen, E., Moulin, T., Pazart, L., Aubry, R. 2013. EEG- and fMRI-based communication tools in disorders of consciousness: which is the most reliable method? Clinical EEG and Neuroscience, 44(4), E111.