talks.html

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<center><i>Tutorial in Bayesian optimization. Gaussian process summer school, Sheffield, 2016.</i> </center>
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  <h1> 2019 </h1>
  <li>  <font color="grey">March,</font> Gaussian Processes and the common ground of decision making under uncertainty. OxWaSP symposium, Warwick, UK. [<a href="./presentations/OxWaSP.pdf">Slides</a>] </li>
  <li>  <font color="grey">March,</font> Gaussian Processes and the common ground of decision making under uncertainty. KERMES meeting, Madrid, Spain.</li>
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  <h1> 2018 </h1>
  <li>  <font color="grey">November,</font> Gaussian Processes for optimization and quadrature. UCL, London, UK.</li>
  <li>  <font color="grey">November,</font> Gaussian Processes for decision making. University of Oxford, UK.</li>
  <li>  <font color="grey"> September,</font> Gaussian Processes for Global Optimization. Gaussian Process Summer School, Sheffield, UK.</li>
  <li>  <font color="grey"> July,</font> Gaussian processes emulators involving multivariate output in model optimisation problems, Lancaster, UK.</li>
  <li>  <font color="grey"> Jun,</font> Gaussian Processes for Uncertainty Quantification. Part I. MLSS, Buenos Aires, Argentina. [<a href="./presentations/mlss-l1gps.pdf">Slides</a>].</li>
  <li>  <font color="grey"> Jun,</font> Gaussian Processes for Uncertainty Quantification. Part II. MLSS, Buenos Aires, Argentina. [<a href="./presentations/mlss-l2uq.pdf">Slides</a>].</li>
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  <h1>2017</h1>
  <li>  <font color="grey"> September</font>, Gaussian Processes for Global Optimization. Gaussian Process Summer School, Sheffield, UK.</li>
  <li>  <font color="grey"> July</font>, Bayesian optimization of black-box functions with GPyOpt. European Statistical Meeting, Helsinki, Finland.</li>
  <li>  <font color="grey"> February, </font> Bayesian Optimization with Tree-structured Dependencies, GPA workshop, Berlin, Germany.</li>
  <li>  <font color="grey"> February, </font> Masterclass in Bayesian optimization. Lancaster, UK.</li>
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  <h1>2016</h1>
  <li>  <font color="grey"> September</font>, Gaussian Processes for Global Optimization. Gaussian Process Summer School, Sheffield, UK.</li>
  <li>  <font color="grey"> Jun</font>, GPyOpt, A tool for Bayesian optimization (tutorial), Aalto University, Helsinki, Finland.</li>
  <li>  <font color="grey"> May (spotlight)</font>, GLASSES, Relieving the Myopia of Bayesian Optimisation, AISTATS'16, Cadiz, Spain.</li>
  <li>  <font color="grey"> April</font>, Bayesian optimisation, a tool for automating Data Science pipelines, Lancaster University, UK.</li>
  <li>  <font color="grey"> April</font>, Bayesian optimisation for model configuration and experimental design, Lancaster University, UK.</li>
  <li>  <font color="grey"> April</font>, Scalable (and usable!) Bayesian Optimisation, SIAM-UQ, Lausanne, Switzerland [<a href="./presentations/talk_laussane_siamuq16.pdf">Slides</a>].</li>
  <li>  <font color="grey"> March</font>, Linking Recombinant Gene Sequence to Protein Product Manufacturability Using CHO cell Genomic Resources. BRIC Meeting, Kent, UK.</li>
  <li>  <font color="grey"> Jun</font>, GPyOpt, A tool for Bayesian optimization (tutorial), Oxford University, UK.</li>
  <li>  <font color="grey"> February</font>, Parallel Bayesian optimization with applications to synthetic gene design. Oxford University, UK [<a href="./presentations/talk_oxford2016.pdf">Slides</a>].</li>
  <li>  <font color="grey"> February</font>, Still optimizing in the dark? Bayesian optimization for model configuration and experimental design. University of Groningen, The Netherlands. [<a href="./presentations/talk_groningen2016.pdf">Slides</a>].</li>
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  <h1>2015</h1>
  <li>  <font color="grey"> December</font>, In Silico Design of Synthetic Genes for Total Cell Translation Control: a
Multi-output Gaussian Processes approach. NIPS workshop in Computational Biology, 2015.</li>
  <li>  <font color="grey"> November</font>, Bayesian Optimization for Synthetic Gene Design. DDHL workshop, Nottingham, UK [<a href="./presentations/slides_ddhl2015.pdf">Slides</a>].</li>
  <li>  <font color="grey"> October</font>, Bayesian Optimization, recent developments and applications. The University of Manizales, Colombia. [<a href="./presentations/talk_manizales2015.pdf">Slides</a>].</li>
  <li>  <font color="grey"> October</font>, Linking Recombinant Gene Sequence to Protein Product Manufacturability Using CHO cell Genomic Resources. BRIC Meeting, Manchester, UK.</li>
  <li>  <font color="grey"> September</font>, Gaussian Processes for Global Optimization. Gaussian Process Summer School, Sheffield, UK.</li>
  <li>  <font color="grey"> July</font>, Bayesian Optimization for synthetic gene design. ICML, workshop in constructive Learning, Lille, France.</li>
  <li>  <font color="grey"> May</font>, Batch Bayesian Optimization via Local Penalization. UCL, London, UK.</li>
  <li>  <font color="grey"> April</font>, Bayesian Optimization for Synthetic Gene Design. 25th Annual MASAMB Workshop, Helsinki, Finland.</li>
  <li>  <font color="grey"> March</font>, Linking Recombinant Gene Sequence to Protein Product Manufacturability Using CHO cell Genomic Resources. BRIC Meeting, Newcastle, UK.</li>
  <li>  <font color="grey"> March</font>, Linking recombinant gene sequence to protein
products.  Sheffield Institute for Translational Neuroscience, The University of Sheffield, UK. [<a href="./presentations/Sitran-feb2015.pdf">Slides</a>]</li>
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  <h1>2014</h1>
    <li>  <font color="grey"> September</font>, Linking Recombinant Gene Sequence to Protein Product Manufacturability Using CHO cell Genomic Resources. BRIC Meeting, Cardiff, UK.</li
    <li>  <font color="grey"> May</font>,  Rewriting the genetic code. Department of Computer Science. The University of Sheffield, Sheffield, UK.
    <li>  <font color="grey"> May </font>, Bayesian Optimization for synthetic gene design. Max Planck Institute for Intelligent Systems, Tubingen, Germany.</li>
    <li>  <font color="grey"> May</font>, Optimization problems in molecular biology. 1st Braitenberg Round table in Probabilistic Geometries, Tubingen, Germany.</li>

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  <h1>2013</h1>
  <li>  <font color="grey"> November</font>, 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.
  <li>  <font color="grey"> May 2013</font>, Network-based  statistical inference for replicatively aging in yeast. Center for Systems Biology and Aging. The Univerisity of Groningen, Groningen, The Netherlands.</li>
  <li>  <font color="grey"> May 2013</font>, 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.</li>
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  <h1>2012</h1>
  <li>  <font color="grey"> November 2012</font>, Reproducing kernel Hilbert spaces based estimation of ordinary differential equations. BioMaths group, Imperial College of London [<a href="./presentations/Imperial-2012.pdf">Slides</a>]. </li>
  <li>  <font color="grey"> November 2012</font>, Reproducing kernel Hilbert spaces based estimation of ordinary differential equations. Dynamical Systems group, Imperial College of London [<a href="./presentations/Imperial-2012.pdf">Slides</a>]. </li>
  <li>  <font color="grey"> September 2012</font>, Estimation of Systems of differential equations with applications in Systems Biology. International Study Group on Systems Biology, Groningen.</li>
  <li>  <font color="grey"> September 2012 (spotlight)</font>, Estimating structured networks using iterative l1-penalty approaches. High dimensional and dependent functional data. Bristol, UK.
  <li>  <font color="grey"> Jun 2012</font>, Reproducing kernel Hilbert spaces based estimation of systems of ordinary differential equations. Workshop Parameter Estimation for Dynamical Systems, Eindhoven [<a href="http,//www.eurandom.nl/events/workshops/2012/PEDSII/Presentations/Gonzalez.pdf">Slides</a>]. </li>
  <li>  <font color="grey"> March 2012</font>, YISB workshop Bridging the communication gap in systems biology.  How to reconstruct biological networks? Papendal, The Netherlands.</li>
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  <h1>2011</h1>
  <li>  <font color="grey"> December 2011</font>, 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.</li>
  <li>  <font color="grey"> September 2011</font>, Workshop Statistics for Biological Networks, Groningen, The Netherlands. Reproducing Kernel Hilbert Space approach for ODE inference.</li>
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  <h1>2010 and before</h1>
  <li>  <font color="grey"> December 2010</font>, Bask Center of Applied Maths. Bilbao, Spain. Recent Advances in Kernel Methods for classification problems. [<a href="http,//www.bcamath.org/documentos_public/archivos/actividades_cientificas/TalkBCAM20101216JG.pdf">Slides</a>]</li>
  <li>  <font color="grey"> June 2010</font>, 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.</li>
  <li>  <font color="grey"> June 2009</font>, Joint Statistical Meeting, Washington, USA. Spatial Temporal Data Analysis via Reproducing Kernel Regularization.</li>
  <li>  <font color="grey"> March 2009</font>, 11th Conference of the International Federation of classification Societies (IFCS 2009). Kernel Function Learning from Several Information Sources.</li>
  <li>  <font color="grey"> June 2008</font>, Workshop in Nonparametric Inference, Coimbra, Portugal. Functional Data Classification based on Reproducing Kernel Regularization.</li>
  <li>  <font color="grey"> May 2008</font>, Department of Statistics, Carlos III university of Madrid. Spectral Measures for kernel matrices comparison.</li>
  <li>  <font color="grey"> July 2007</font>, Institute for Mathematics Applied to Geosciences. National Center for Atmospheric Research. Colorado, USA. Support Vector Machines for Classification Purposes. Recent Advances in Kernel Combination.  [<a href="./presentations/NCAR-2007.pdf">Slides</a>]</li>
  <li>  <font color="grey"> October 2006</font>, 28th Fall Meeting of the AG-DANK. Dortmund. Germany. Kernel Combination in Support Vector Machines for Classification Purposes. [<a href="./presentations/AGDANK-2006.pdf">Slides</a>]</li>
  <li>  <font color="grey"> November 2006</font>, 11th Iberoamerican Congress in Pattern Recognition. Local Linear Approximation for Kernel Methods, The Railway Kernel.</li>
  <li>  <font color="grey"> September 2005</font>, 4th Symposium of PLS and Related Methods. Barcelona, Spain. A New Robust Partial Least Squares Regression Method. [<a href="./presentations/PLS-2005.pdf">Slides</a>] </li>
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