Tutorial in Bayesian optimization. Gaussian process summer school, Sheffield, 2016.

2019

  • March, Gaussian Processes and the common ground of decision making under uncertainty. OxWaSP symposium, Warwick, UK. [Slides]
  • March, Gaussian Processes and the common ground of decision making under uncertainty. KERMES meeting, Madrid, Spain.

    • 2018

  • November, Gaussian Processes for optimization and quadrature. UCL, London, UK.
  • November, Gaussian Processes for decision making. University of Oxford, UK.
  • September, Gaussian Processes for Global Optimization. Gaussian Process Summer School, Sheffield, UK.
  • July, Gaussian processes emulators involving multivariate output in model optimisation problems, Lancaster, UK.
  • Jun, Gaussian Processes for Uncertainty Quantification. Part I. MLSS, Buenos Aires, Argentina. [Slides].
  • Jun, Gaussian Processes for Uncertainty Quantification. Part II. MLSS, Buenos Aires, Argentina. [Slides].

    • 2017

  • September, Gaussian Processes for Global Optimization. Gaussian Process Summer School, Sheffield, UK.
  • July, Bayesian optimization of black-box functions with GPyOpt. European Statistical Meeting, Helsinki, Finland.
  • February, Bayesian Optimization with Tree-structured Dependencies, GPA workshop, Berlin, Germany.
  • February, Masterclass in Bayesian optimization. Lancaster, UK.

    • 2016

  • September, Gaussian Processes for Global Optimization. Gaussian Process Summer School, Sheffield, UK.
  • Jun, GPyOpt, A tool for Bayesian optimization (tutorial), Aalto University, Helsinki, Finland.
  • May (spotlight), GLASSES, Relieving the Myopia of Bayesian Optimisation, AISTATS'16, Cadiz, Spain.
  • April, Bayesian optimisation, a tool for automating Data Science pipelines, Lancaster University, UK.
  • April, Bayesian optimisation for model configuration and experimental design, Lancaster University, UK.
  • April, Scalable (and usable!) Bayesian Optimisation, SIAM-UQ, Lausanne, Switzerland [Slides].
  • March, Linking Recombinant Gene Sequence to Protein Product Manufacturability Using CHO cell Genomic Resources. BRIC Meeting, Kent, UK.
  • Jun, GPyOpt, A tool for Bayesian optimization (tutorial), Oxford University, UK.
  • February, Parallel Bayesian optimization with applications to synthetic gene design. Oxford University, UK [Slides].
  • February, Still optimizing in the dark? Bayesian optimization for model configuration and experimental design. University of Groningen, The Netherlands. [Slides].

    • 2015

  • December, In Silico Design of Synthetic Genes for Total Cell Translation Control: a Multi-output Gaussian Processes approach. NIPS workshop in Computational Biology, 2015.
  • November, Bayesian Optimization for Synthetic Gene Design. DDHL workshop, Nottingham, UK [Slides].
  • October, Bayesian Optimization, recent developments and applications. The University of Manizales, Colombia. [Slides].
  • October, Linking Recombinant Gene Sequence to Protein Product Manufacturability Using CHO cell Genomic Resources. BRIC Meeting, Manchester, UK.
  • September, Gaussian Processes for Global Optimization. Gaussian Process Summer School, Sheffield, UK.
  • July, Bayesian Optimization for synthetic gene design. ICML, workshop in constructive Learning, Lille, France.
  • May, Batch Bayesian Optimization via Local Penalization. UCL, London, UK.
  • April, Bayesian Optimization for Synthetic Gene Design. 25th Annual MASAMB Workshop, Helsinki, Finland.
  • March, Linking Recombinant Gene Sequence to Protein Product Manufacturability Using CHO cell Genomic Resources. BRIC Meeting, Newcastle, UK.
  • March, Linking recombinant gene sequence to protein products. Sheffield Institute for Translational Neuroscience, The University of Sheffield, UK. [Slides]

    • 2014

  • September, Linking Recombinant Gene Sequence to Protein Product Manufacturability Using CHO cell Genomic Resources. BRIC Meeting, Cardiff, UK.
    • May, Rewriting the genetic code. Department of Computer Science. The University of Sheffield, Sheffield, UK.
  • May , Bayesian Optimization for synthetic gene design. Max Planck Institute for Intelligent Systems, Tubingen, Germany.
  • May, Optimization problems in molecular biology. 1st Braitenberg Round table in Probabilistic Geometries, Tubingen, Germany.

    • 2013

  • November, DgCox, A differential geometric approach for high dimensional relative risk regression models. Institute of Mathematics and Computer Science. Unit of Probability and Statistics. The University of Groningen, Groningen, The Netherlands.
  • May 2013, Network-based statistical inference for replicatively aging in yeast. Center for Systems Biology and Aging. The Univerisity of Groningen, Groningen, The Netherlands.
  • May 2013, Calorie restriction does not elicit a robust extension of replicative lifespan in Saccharomyces cerevisiae. Center for Systems Biology and Aging. The Univerisity of Groningen, Groningen, The Netherlands.

    • 2012

  • November 2012, Reproducing kernel Hilbert spaces based estimation of ordinary differential equations. BioMaths group, Imperial College of London [Slides].
  • November 2012, Reproducing kernel Hilbert spaces based estimation of ordinary differential equations. Dynamical Systems group, Imperial College of London [Slides].
  • September 2012, Estimation of Systems of differential equations with applications in Systems Biology. International Study Group on Systems Biology, Groningen.
  • September 2012 (spotlight), Estimating structured networks using iterative l1-penalty approaches. High dimensional and dependent functional data. Bristol, UK.
  • Jun 2012, Reproducing kernel Hilbert spaces based estimation of systems of ordinary differential equations. Workshop Parameter Estimation for Dynamical Systems, Eindhoven [Slides].
  • March 2012, YISB workshop Bridging the communication gap in systems biology. How to reconstruct biological networks? Papendal, The Netherlands.

    • 2011

  • December 2011, 4th International Conference of the ERCIM working group on computing & statistics. Senate House, University of London, UK. Time series classification via the combination of functional data projections.
  • September 2011, Workshop Statistics for Biological Networks, Groningen, The Netherlands. Reproducing Kernel Hilbert Space approach for ODE inference.

    • 2010 and before

  • December 2010, Bask Center of Applied Maths. Bilbao, Spain. Recent Advances in Kernel Methods for classification problems. [Slides]
  • June 2010, Department of Statistics, Carlos III university of Madrid. Representing Functional Data in Reproducing Kernel Hilbert Spaces with applications to Clustering, Classification and Time Series Problems.
  • June 2009, Joint Statistical Meeting, Washington, USA. Spatial Temporal Data Analysis via Reproducing Kernel Regularization.
  • March 2009, 11th Conference of the International Federation of classification Societies (IFCS 2009). Kernel Function Learning from Several Information Sources.
  • June 2008, Workshop in Nonparametric Inference, Coimbra, Portugal. Functional Data Classification based on Reproducing Kernel Regularization.
  • May 2008, Department of Statistics, Carlos III university of Madrid. Spectral Measures for kernel matrices comparison.
  • July 2007, Institute for Mathematics Applied to Geosciences. National Center for Atmospheric Research. Colorado, USA. Support Vector Machines for Classification Purposes. Recent Advances in Kernel Combination. [Slides]
  • October 2006, 28th Fall Meeting of the AG-DANK. Dortmund. Germany. Kernel Combination in Support Vector Machines for Classification Purposes. [Slides]
  • November 2006, 11th Iberoamerican Congress in Pattern Recognition. Local Linear Approximation for Kernel Methods, The Railway Kernel.
  • September 2005, 4th Symposium of PLS and Related Methods. Barcelona, Spain. A New Robust Partial Least Squares Regression Method. [Slides]