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talks.html
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talks.html
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<h1><span>Andreas Damianou</span></h1>
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<h3>Selected talks</h3>
<!-- <h5><i><span class="notbold">(<b>Conference/workshop</b> talks indicated with <img src="images/bullet_red.png"> Rest are <b>invited</b> talks. )</span></i></h5> -->
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<h5><span class="nobold"><li style="margin-left:50px"> Invited talks.</li></span><h5>
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<!--<ul title="Talks" class="navlist">-->
<ul title="Talks">
<li>
<a name="test_of_time_2023"></a>
<b><i>"Deep Gaussian Processes - Test of Time Award."</i></b>
<br>
AISTATS, Valencia, Apr. 2023
[<a href="http://adamian.github.io/talks/Damianou_DGP_test_of_time_2023.pdf">Slides</a>]
</li>
<br>
<li>
<a name="talk_pinterest_2022"></a>
<b><i>"Podcast Recommendations and Search Query suggestions using Graph neural networks at Spotify."</i></b>
<br>
Pinterest, Dec. 2022
</li>
<br>
<li>
<a name="talk_Zindi_panel"></a>
<b><i>"Kickstart Your Career in Data - Panel."</i></b>
<br>
Zindi.africa & Pan African Women Empowerment Network, Oct. 2022
</li>
<br>
<li>
<a name="talk_Stanford_2022"></a>
<b><i>"Podcast Recommendations and Search Query suggestions using Graph neural networks at Spotify."</i></b>
<br>
Stanford Graph Learning Workshop, Stanford University, Sep. 2022.
[<a href="https://www.youtube.com/watch?v=79MRwEB5AhA&list=PLqYw1C4YGfr0byz1XMD95YMDR-so4qy1e&index=7">Video</a>]
</li>
<br>
<li>
<a name="talk_ML_Data_Sapienza_2022"></a>
<b><i>"Working with Data in Industrial ML Applications."</i></b>
<br>
Univ. of Sapienza, Rome, 27 and 28 April 2022
[<a href="http://adamian.github.io/talks/2022_ML_Data_Sapienza.pdf">Slides</a>]
</li>
<br>
<li>
<a name="talk_CambridgeWinterSchool2021"></a>
<b><i>"The role of uncertainty in machine learning."</i></b>
<br>
Cambridge Science Accelerator Winter School, 02/02/2021
[<a href="https://mlatcl.github.io/mlaccelerate/talk/andreasdamianou/">Web</a>]
[<a href="http://adamian.github.io/talks/Damianou_Uncertainty_Cambridge_2021.pdf">Slides</a>]
</li>
<br>
<li>
<a name="talk_NeurIPS_Meetup_2020"></a>
<b><i>"Deep Learning in the Function space."</i></b>
<br>
NeurIPS 2020 Nairobi Meetup, 10/12/2020
[<a href="http://adamian.github.io/talks/Damianou_NeurIPS_Nairobi_2020.pdf">PDF</a>]
[<a href="https://youtu.be/um73FhxAjBs">Video</a>]
</li>
<br>
<li>
<a name="talk_DSA_2020"></a>
<b><i>"Deep Learning practical considerations."</i></b>
<br>
Data Science Africa - Kampala (Virtual), 24/07/2020
[<a href="http://adamian.github.io/talks/Damianou_DeepLearning-DSA2020.pdf">PDF</a>]
[<a href="https://github.com/datsciafrica/presentations/blob/master/kampala2020/DSA2020-DeepLearningPractical.ipynb">Notebook</a>]
[<a href="https://youtu.be/tBTzVR6hO08">Video</a>]
</li>
<br>
<li>
<a name="talk_MSR_2020"></a>
<b><i>"Fast Computation with Linearized Neural Networks for Domain Adaptation."</i></b>
<br>
Microsoft Research & Univ. Cambridge Meetup, 28/02/2020
[<a href="http://adamian.github.io/talks/Damianou_MS_LinearizedNN.pdf">PDF</a>]
</li>
<br>
<li>
<a name="talk_Aalto_2019"></a>
<b><i>"From GP to deep learning and from deep learning to GP."</i></b>
<br>
ATI/Aalto Workshop on Deep Structures, Helsinki, 19/12/2019
[<a href="http://adamian.github.io/talks/Damianou_Aalto_DGP_NN_19.pdf">PDF</a>]
</li>
<br>
<li>
<a name="talk_Manchester_2019"></a>
<b><i>"Deep and Multi-fidelity learning with Gaussian processes."</i></b>
<br>
Advances in Data Science Seminar Series, Univ. of Manchester, 15/10/2019
[<a href="http://adamian.github.io/talks/Damianou_Manchester_multifidelity19.pdf">PDF</a>] [<a href="http://adamian.github.io/talks/Damianou_GP_tutorial.html">GP tutorial</a>]
</li>
<br>
<li>
<a name="talk_Munich_2019"></a>
<b><i>"Deep and Multi-fidelity learning with Gaussian processes."</i></b>
<br>
Uncertainty Propagation in Composite Models, Munich, 11/10/2019
</li>
<br>
<li>
<a name="talk_ATI_2019"></a>
<b><i>"Deep and Multi-fidelity learning with Gaussian processes."</i></b>
<br>
Alan Turing Institute workshop on Uncertainty Quantification, 06/08/2019
[<a href="http://adamian.github.io/talks/Damianou_ATI_multifidelity19.pdf">PDF</a>]
<!-- [<a href="video: https://youtu.be/Y9SIpZGzeyA">Video</a>] -->
</li>
<br>
<li>
<a name="talk_Warwick_2019"></a>
<b><i>"Deep learning, probability and uncertainty."</i></b>
<br>
Univ. of Warwick CS Colloquium, 06/06/2019
[<a href="http://adamian.github.io/talks/Damianou_warwick_2019.pdf">PDF</a>]
</li>
<br>
<li>
<a name="talk_Leeds_2019"></a>
<b><i>"Introduction to deep transfer learning with Xfer."</i></b>
<br>
University of Leeds, 08/03/2019
[<a href="http://adamian.github.io/talks/Damianou_Leeds_xfer.pdf">PDF</a>] [<a href="https://adamian.github.io/talks/Damianou_DL_tutorial_19.ipynb">notebook</a>]
</li>
<br>
<li>
<a name="talk_MXnet_2019"></a>
<b><i>"Deep transfer learning with Xfer."</i></b>
<br>
MXnet Deep Learning meetup, London, 06/03/2019
[<a href="http://adamian.github.io/talks/Damianou_Mxnet_Xfer.pdf">PDF</a>] [<a href="https://adamian.github.io/talks/Damianou_DL_Xfer.ipynb">notebook</a>]
</li>
<br>
<li>
<a name="talk_RSS_2018"></a>
<b><i>"Introduction to deep learning."</i></b>
<br>
Royal Statistical Society, London, 13/12/2018
[<a href="http://adamian.github.io/talks/Damianou_deep_learning_rss_2018.pdf">PDF</a>] [<a href="https://nbviewer.jupyter.org/url/adamian.github.io/talks/Damianou_DL_tutorial_18.ipynb">notebook</a>]
</li>
<br>
<li class="bullet_green">
[Blog post] <br>
<b><i>"Xfer: an open-source library for neural network transfer learning."</i></b>
[<a href="https://medium.com/apache-mxnet/xfer-an-open-source-library-for-neural-network-transfer-learning-cd5eac4accf0">LINK</a>]
</li>
<br>
<li>
<b><i>"Inverse Reinforcement Learning with Deep Gaussian processes."</i></b>
<br>
Prowler.io, 03/07/2018
</li>
<br>
<li>
<b><i>"Probabilistic and Bayesian deep learning."</i></b>
<br>
Univ. of Sheffield, 19/03/2018
</li>
<br>
<li class="bullet_red">
<b><i>"Variational inference in Deep Gaussian processes."</i></b>
<br>
Keynote at NIPS workshop on approximate Bayesian inference, Long Beach, USA, 08/12/2017
[<a href="http://adamian.github.io/talks/Damianou_NIPS17.pdf">PDF</a>]
</li>
<br>
<li>
<b><i>"Probability & uncertainty in deep learning."</i></b>
<br>
Deep Learning summit, London, 21/09/2017
[<a href="http://adamian.github.io/talks/Damianou_DL_summit.pdf">PDF</a>]
</li>
<br>
<li>
<a name="talk_Arusha_2017"></a>
<b><i>"Introduction to Deep Learning."</i></b>
<br>
Data Science in Africa, Arusha, Tanzania, 19/06/2017
[<a href="http://www.datascienceafrica.org/dsa2017/assets/deep_learning_tutorial.pdf">PDF</a>] [<a href="https://nbviewer.jupyter.org/url/adamian.github.io/talks/Damianou_DL_tutorial_18.ipynb">notebook</a>]
</li>
<br>
<li>
<b><i>"Probabilistic and Bayesian deep learning."</i></b>
<br>
University of Bristol, UK, 17/05/2017
<!-- [<a href="">PDF</a>] -->
</li>
<br>
<li>
<b><i>"Variational constraints for training deep and recurrent Gaussian processes."</i></b>
<br>
Harvard University, USA, 23/02/2016
<!-- [<a href="">PDF</a>] -->
</li>
<br>
<li>
<b><i>"Representation and deep learning with Bayesian non-parametric models."</i></b>
<br>
Microsoft Research MA, USA, 22/02/2016
<!-- [<a href="">PDF</a>] -->
</li>
<br>
<li>
<b><i>"Latent variable and deep modeling with Gaussian processes; applications to system identification."</i></b>
<br>
Brown University, USA, 17/02/2016
[<a href="http://adamian.github.io/talks/Damianou_16a_Brown.pdf">PDF</a>]
</li>
<br>
<li>
<b><i>"Gaussian processes for data-driven modeling and uncertainty quantification: a hands-on tutorial."</i></b>
<br>
Brown University, USA, 16/02/2016
[<a href="http://adamian.github.io/talks/Damianou_16b_Brown.pdf">PDF</a>] [<a href="http://nbviewer.jupyter.org/github/adamian/adamian.github.io/blob/master/talks/Brown2016.ipynb">Jupyter notebook</a>]
</li>
<br>
<li>
<b><i>"System identification and control with (deep) Gaussian processes."</i></b>
<br>
MIT, USA, 11/02/2016
[<a href="http://adamian.github.io/talks/Damianou_16_MIT.pdf">PDF</a>]
</li>
<br>
<li>
<b><i>"Representation and deep learning with Bayesian non-parametric models"</i></b>
<br>
Athens University of Economics and Business, Greece, 14/10/2015
[<a href="http://adamian.github.io/talks/Damianou_asoeeTalk15.pdf">PDF</a>]
</li>
<br>
<li>
<b><i>"Bayesian latent variable modelling with GPs"</i></b>
<br>
Gaussian Process Summer School, Sheffield, UK, 14/09/2015
[<a href="http://adamian.github.io/talks/Damianou_gpss_BayesianLVMs15.pdf">PDF</a>]
[<a href="https://youtu.be/H9FTGBW4o2g?t=44m15s">Video</a>]
</li>
<br>
<li class="bullet_red">
<b><i>"A top-down approach for a synthetic autobiographical memory system"</i></b>
<br>
4th International Conference on Biomimetic and Biohybrid Systems (Living Machines), Barcelona, 31/07/2015
[<a href="http://adamian.github.io/talks/Damianou_LivMachines15.pdf">PDF</a>]
</li>
<br>
<li>
<b><i>"Deep Gaussian Processes and Variational Propagation of Uncertainty"</i></b>
<br>
Department of Engineering, University of Cambridge, UK, 29/06/2015
[<a href="http://adamian.github.io/talks/Damianou_Cambridge16.pdf">PDF</a>]
</li>
<br>
<li>
<a name="talk_IBM_2015"></a>
<b><i>"Probabilistic Models for Learning Data Representations"</i></b>
<br>
IBM Research, Nairobi, Kenya, 23/06/2015
[<a href="http://adamian.github.io/talks/Damianou_probModelsIBM15.pdf">PDF</a>]
</li>
<br>
<li>
<a name="talk_Nyeri_2015"></a>
<b><i>"Dimensionality Reduction and Latent Variable Modelling"</i></b>
<br>
<a href="http://gpss.cc/dss15/">Data Science School</a> and <a href="http://gpss.cc/dsa15/">Data Science Workshop in Africa</a>, Nyeri, Kenya, 17/06/2015
[<a href="http://adamian.github.io/talks/Damianou_dimRedKenya15.pdf">PDF</a>]
<!-- [<a href="">Video</a>] -->
</li>
<br>
<li>
<b><i>"Deep non-parametric learning with Gaussian processes"</i></b>
<br>
School of Computing Science, Glasgow, Scotland, 10/06/2015
[<a href="http://adamian.github.io/talks/Damianou_deepGPGlasgow15.pdf">PDF</a>]
</li>
<br>
<li>
<b><i>"Deep probabilistic modelling with deep GPs"</i></b>
<br>
<a href="http://ml.dcs.shef.ac.uk/DeepWorkshop/">First Workshop on Deep Probabilistic Models</a>, Sheffield, 02/10/2014
[<a href="http://adamian.github.io/talks/Damianou_DeepProbModels14.pdf">PDF</a>]
</li>
<br>
<li>
<b><i>"Feature representations with Deep Gaussian processes"</i></b>
<br>
<a href="http://ml.dcs.shef.ac.uk/gpss/gpfe14/">Feature Extraction with Gaussian Processes Workshop</a>, Sheffield, 18/09/2014
[<a href="http://adamian.github.io/talks/gpss_deepGPs14.pdf">PDF</a>]
</li>
<br>
<li>
<b><i>"Deep Gaussian processes"</i></b>
<br>
<a href="http://www.meetup.com/Deep-Learning-London/events/187693712/">Deep Learning meetup</a>, London, 24/06/2014.
[<a href="http://adamian.github.io/talks/Damianou_DeepGPsMeetupLondon.pdf">PDF</a>]
[<a href="https://skillsmatter.com/skillscasts/5379-deep-gaussian-processes">video</a>]
</li>
<br>
<li>
<b><i>"Deep Gaussian processes"</i></b>
<br>
Imperial College, London, 23/06/2014.
[<a href="http://talks.ee.ic.ac.uk/talk/index/668">Link</a>]
</li>
<br>
<li>
<b><i>"Modeling and consolidating complex data with Gaussian process models"</i></b>
<br>
ICS-FORTH, Heraklion, Greece, 10/06/2014.
[<a href="http://adamian.github.io/talks/GPModelling_FORTH_2014.pdf">PDF</a>]
</li>
<br>
<li>
<b><i> "Deep Gaussian processes."</i></b>
<br>
University of Washington, USA, 28/01/2013.
</li>
<br>
<li>
<b><i> "Modeling complex data with deep Gaussian processes."</i></b>
<br>
Microsoft Research, Redmond, USA, 23/01/2013.
</li>
<br>
<li>
<b><i> "Modeling dynamical and multi-modal computer vision data via non-linear probabilistic dimensionality reduction."</i></b>
<br>
University of Surrey, UK, 14/06/2012.
[<a href="http://adamian.github.io/talks/talkSurrey2012.pdf">PDF<span></span></a>]
</li>
<br>
<li>
<b><i> "Tutorial on Gaussian Processes and the Gaussian Process Latent Variable Model (& discussion on the GPLVM tech. report by Prof. N. Lawrence, ’06)."</i></b>
<br>
University of Surrey, UK, 13/06/2012.
[<a href="http://adamian.github.io/talks/tutorialGP_GPLVM_Surrey2012.pdf">PDF<span></span></a>]
</li>
<br>
<li>
<b><i> "Variational Gaussian process latent variable models for high dimensional image data."</i></b>
<br>
The Rank Prize Machine Learning and Vision Symposium, Cumbria, UK, 2012
[<a href="http://adamian.github.io/talks/talkRankPrize.pdf">PDF<span></span></a>]
</li>
<br>
<li>
<b><i> "Non-linear probabilistic dimensionality reduction for dynamical and multi-modal vision datasets."</i></b>
<br>
The School of Computer Science and Communication, KTH, Stockholm, Sweden, 2012.
[<a href="http://adamian.github.io/talks/KTHtalk.pdf">PDF<span></span></a>]
</li>
<br>
<li class="bullet_red">
<b><i> "Manifold Relevance Determination."</i></b>
<br>
ICML 2012.
[<a href="http://adamian.github.io/talks/Damianou_MRDtalkICML.pdf">PDF<span></span></a>]
[<a href="http://techtalks.tv/talks/manifold-relevance-determination/57331/">Video<span></span></a>]
</li>
</ul>
<!--
<br>
<h3>Other selected talks </h3>
<ul title="Other talks">
<li>
<b><i> "Manifold Relevance Determination."</i></b>
<br>
ICML 2012.
[<a href="http://staffwww.dcs.shef.ac.uk/people/A.Damianou/talksAndPosters/Damianou_MRDtalkICML.pdf">PDF<span></span></a>]
[<a href="http://techtalks.tv/talks/manifold-relevance-determination/57331/">Video<span></span></a>]
</li>
<br>
</ul>
-->
<br>
<br>
<br>
</p>
<p> </p>
<p> </p>
<p> </p>
<p> </p>
<p> </p>
<font size="1", color="999999"><i>*Thanks to J. Hensman & N. Fusi for lending me their nice latex templates!</i></font>
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