Andreas Damianou

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Selected talks

Invited talks.

Conference/workshop talks.

Other.

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• "Towards graph foundation models for personalization."
  Edinburgh's AI Society, June. 2024


• "Introduction to Large Language Models and Prompt Engineering."
  Data Scienc Africa, Nyeri, Kenya (Remote), May. 2024


• "Graph learning and graph neural networks."
  DSAIL Nyeri, Keya (Remote), Apr. 2024


• "Deep Gaussian Processes - Test of Time Award."
  AISTATS, Valencia, Apr. 2023 [Slides]


• "Podcast Recommendations and Search Query suggestions using Graph neural networks at Spotify."
  Pinterest, Dec. 2022


• "Kickstart Your Career in Data - Panel."
  Zindi.africa & Pan African Women Empowerment Network, Oct. 2022


• "Podcast Recommendations and Search Query suggestions using Graph neural networks at Spotify."
  Stanford Graph Learning Workshop, Stanford University, Sep. 2022. [Video]


• "Working with Data in Industrial ML Applications."
  Univ. of Sapienza, Rome, 27 and 28 April 2022 [Slides]


• "The role of uncertainty in machine learning."
  Cambridge Science Accelerator Winter School, 02/02/2021 [Web] [Slides]


• "Deep Learning in the Function space."
  NeurIPS 2020 Nairobi Meetup, 10/12/2020 [PDF] [Video]


• "Deep Learning practical considerations."
  Data Science Africa - Kampala (Virtual), 24/07/2020 [PDF] [Notebook] [Video]


• "Fast Computation with Linearized Neural Networks for Domain Adaptation."
  Microsoft Research & Univ. Cambridge Meetup, 28/02/2020 [PDF]


• "From GP to deep learning and from deep learning to GP."
  ATI/Aalto Workshop on Deep Structures, Helsinki, 19/12/2019 [PDF]


• "Deep and Multi-fidelity learning with Gaussian processes."
  Advances in Data Science Seminar Series, Univ. of Manchester, 15/10/2019 [PDF] [GP tutorial]


• "Deep and Multi-fidelity learning with Gaussian processes."
  Uncertainty Propagation in Composite Models, Munich, 11/10/2019


• "Deep and Multi-fidelity learning with Gaussian processes."
  Alan Turing Institute workshop on Uncertainty Quantification, 06/08/2019 [PDF]


• "Deep learning, probability and uncertainty."
  Univ. of Warwick CS Colloquium, 06/06/2019 [PDF]


• "Introduction to deep transfer learning with Xfer."
  University of Leeds, 08/03/2019 [PDF] [notebook]


• "Deep transfer learning with Xfer."
  MXnet Deep Learning meetup, London, 06/03/2019 [PDF] [notebook]


• "Introduction to deep learning."
  Royal Statistical Society, London, 13/12/2018 [PDF] [notebook]


• [Blog post]
  "Xfer: an open-source library for neural network transfer learning." [LINK]


• "Inverse Reinforcement Learning with Deep Gaussian processes."
  Prowler.io, 03/07/2018


• "Probabilistic and Bayesian deep learning."
  Univ. of Sheffield, 19/03/2018


• "Variational inference in Deep Gaussian processes."
  Keynote at NIPS workshop on approximate Bayesian inference, Long Beach, USA, 08/12/2017 [PDF]


• "Probability & uncertainty in deep learning."
  Deep Learning summit, London, 21/09/2017 [PDF]


• "Introduction to Deep Learning."
  Data Science in Africa, Arusha, Tanzania, 19/06/2017 [PDF] [notebook]


• "Probabilistic and Bayesian deep learning."
  University of Bristol, UK, 17/05/2017


• "Variational constraints for training deep and recurrent Gaussian processes."
  Harvard University, USA, 23/02/2016


• "Representation and deep learning with Bayesian non-parametric models."
  Microsoft Research MA, USA, 22/02/2016


• "Latent variable and deep modeling with Gaussian processes; applications to system identification."
  Brown University, USA, 17/02/2016 [PDF]


• "Gaussian processes for data-driven modeling and uncertainty quantification: a hands-on tutorial."
  Brown University, USA, 16/02/2016 [PDF] [Jupyter notebook]


• "System identification and control with (deep) Gaussian processes."
  MIT, USA, 11/02/2016 [PDF]


• "Representation and deep learning with Bayesian non-parametric models"
  Athens University of Economics and Business, Greece, 14/10/2015 [PDF]


• "Bayesian latent variable modelling with GPs"
  Gaussian Process Summer School, Sheffield, UK, 14/09/2015 [PDF] [Video]


• "A top-down approach for a synthetic autobiographical memory system"
  4th International Conference on Biomimetic and Biohybrid Systems (Living Machines), Barcelona, 31/07/2015 [PDF]


• "Deep Gaussian Processes and Variational Propagation of Uncertainty"
  Department of Engineering, University of Cambridge, UK, 29/06/2015 [PDF]


• "Probabilistic Models for Learning Data Representations"
  IBM Research, Nairobi, Kenya, 23/06/2015 [PDF]


• "Dimensionality Reduction and Latent Variable Modelling"
  Data Science School and Data Science Workshop in Africa, Nyeri, Kenya, 17/06/2015 [PDF]


• "Deep non-parametric learning with Gaussian processes"
  School of Computing Science, Glasgow, Scotland, 10/06/2015 [PDF]


• "Deep probabilistic modelling with deep GPs"
  First Workshop on Deep Probabilistic Models, Sheffield, 02/10/2014 [PDF]


• "Feature representations with Deep Gaussian processes"
  Feature Extraction with Gaussian Processes Workshop, Sheffield, 18/09/2014 [PDF]


• "Deep Gaussian processes"
  Deep Learning meetup, London, 24/06/2014. [PDF] [video]


• "Deep Gaussian processes"
  Imperial College, London, 23/06/2014. [Link]


• "Modeling and consolidating complex data with Gaussian process models"
  ICS-FORTH, Heraklion, Greece, 10/06/2014. [PDF]


• "Deep Gaussian processes."
  University of Washington, USA, 28/01/2013.


• "Modeling complex data with deep Gaussian processes."
  Microsoft Research, Redmond, USA, 23/01/2013.


• "Modeling dynamical and multi-modal computer vision data via non-linear probabilistic dimensionality reduction."
  University of Surrey, UK, 14/06/2012. [PDF]


• "Tutorial on Gaussian Processes and the Gaussian Process Latent Variable Model (& discussion on the GPLVM tech. report by Prof. N. Lawrence, ’06)."
  University of Surrey, UK, 13/06/2012. [PDF]


• "Variational Gaussian process latent variable models for high dimensional image data."
  The Rank Prize Machine Learning and Vision Symposium, Cumbria, UK, 2012 [PDF]


• "Non-linear probabilistic dimensionality reduction for dynamical and multi-modal vision datasets."
  The School of Computer Science and Communication, KTH, Stockholm, Sweden, 2012. [PDF]


• "Manifold Relevance Determination."
  ICML 2012. [PDF] [Video]

 

 

 

 

 

*Thanks to J. Hensman & N. Fusi for lending me their nice latex templates!