Academic Profile

Juan-Pablo Ortega holds a first degree in Theoretical Physics from the Universidad de Zaragoza (Spain), a Masters and a PhD in Mathematics from the University of California, Santa Cruz, and a Habilitation degree from the Université de Nice (France). After a postdoc at the Ecole Polytechnique Fédérale de Lausanne (Switzerland) he became a researcher at the Centre Nationale de la Recherche Scientifique (CNRS, France). Before joining the Division of Mathematical Sciences of NTU, he taught mathematics at the Université Bourgogne Franche-Comté (France) and the University of St. Gallen (Switzerland).
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Prof Juan-Pablo Ortega
Professor, School of Physical & Mathematical Sciences - Division of Mathematical Sciences

Prof. Ortega is a mathematician working on the learning and statistical modeling of dynamic processes like input/output systems, stochastic processes, dynamical and controlled systems, and time series. He is also interested in the applications of these topics to financial econometrics, mathematical finance, physiological signal treatment, and engineering. He has worked extensively in geometric mechanics, where he focuses on stability theory, symmetric systems, and their reduction.
 
  • Development of Lab-Based and Portable Dyadic-EEG Sociometrics Sensor Suites for Measurement of Social Interaction Effects on Infant Executive Function

  • Learning of Dynamic Processes and Applications
 
  • Grigoryeva, L. and Ortega, J.-P. [2018] Echo state networks are universal. Neural Networks, 108, 495-508.

  • Grigoryeva, L. and Ortega, J.-P. [2019] Differentiable reservoir computing. Journal of Machine Learning Research, 20(179), 1-62.

  • Audrino, F., Kostrov, A., and Ortega, J.-P. [2019] Predicting U.S. bank failures with MIDAS logit models. Journal of Financial and Quantitative Analysis, 54(6), 2575-2603.

  • Badescu, A., Cui, Z., and Ortega, J.-P. [2019] Closed-form variance swap prices under general affine GARCH models and their continuous-time limits. Annals of Operations Research, 282, 27-57.

  • Gonon, L. and Ortega, J.-P. [2020] Reservoir computing universality with stochastic inputs. IEEE Transactions on Neural Networks and Learning Systems, 31(1), 100-112.

  • Gonon, L., Grigoryeva, L., and Ortega, J.-P. [2020] Memory and forecasting capacities of nonlinear recurrent networks. Physica D, 414, 132721, 1-13.

  • Gonon, L., Grigoryeva, L., and Ortega, J.-P. [2020] Risk bounds for reservoir computing. Journal of Machine Learning Research, 21(240), 1-61.

  • Grigoryeva, L., Hart, A., and Ortega, J.-P. [2021] Chaos on compact manifolds: Differentiable synchronizations beyond the Takens theorem. Physical Review E, 103, 062204.

  • Cuchiero, C., Gonon, L., Grigoryeva, L., Ortega, J.-P., and Teichmann, J. [2021] Discrete-time signatures and randomness in reservoir computing. IEEE Transactions on Neural Networks and Learning Systems. doi: 10.1109/TNNLS.2021.3076777.

  • Gonon, L. and Ortega, J.-P. [2021] Fading memory echo state networks are universal. Neural Networks, 138, 10-13.