Aristeidis Panos

Aristeidis Panos

Research Associate in Machine Learning

Department of Engineering, University of Cambridge


Aristeidis (Ares) Panos is a Research Associate at the Computational and Biological Learning (CBL) Lab in the Department of Engineering at Cambridge University. He received a B.Sc. degree in computer science in 2014 (valedictorian) from Athens University of Economics and Business and then he was awarded a Ph.D. in Machine Learning in 2019 from the Department of Statistical Science at the University College London. In October 2020, he joined the Department of Statistics at the University of Warwick as a Postdoctoral Research Fellow while, at the same time, he was a member of the Trustworthy digital identity group at the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence.

  • Probabilistic Machine Learning
  • Computational Statistics
  • Bayesian Inference
  • Continual Learning
  • Large Vision-Language Models
  • Ph.D. in Machine Learning, 2019

    University College London

  • B.Sc. in Computer Science, 2014

    Athens University of Economics and Business


Department of Engineering, University of Cambridge
Research Associate
Feb 2022 – Present Cambridge, UK
Developing new fundamental tools for machine intelligence and machine learning.
Department of Statistical Science, University College London
Postdoctoral Research Fellow
Dec 2021 – Jan 2022 London, UK
Forecasting wind farm output using machine learning methods.
Department of Statistics, University of Warwick
Postdoctoral Research Fellow
Nov 2020 – Nov 2021 Warwick, UK
Mechanistic marked spatio-temporal point processes for large-scale data-analytic applications.
GlaxoSmithKline (GSK)
Machine Learning Consultant
Jun 2020 – Oct 2020 London, UK
Yield optimization of multi-stage chemical processes used for medicine production via machine learning techniques.
Data scientist
Sep 2018 – Nov 2019 Luton, UK
Implementation of a Bayesian pricing model; causal inference using machine learning algorithms; multi-echelon inventory optimization.
The Alan Turing Institute
Jun 2016 – Aug 2016 London, UK
Machine Classifiers and Similarity measures.
Department of Statistical Science, University College London
Ph.D. in Machine Learning
Sep 2015 – Oct 2019 London, UK
Extreme Multi-label Learning with Gaussian Processes.
Department of Informatics, Athens University of Economics and Business
B.Sc. in Computer Science
Sep 2009 – Jun 2014 Athens, Greece
First Class Honours (2014 Valedictorian).

Recent Publications

(2023). First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning. In the IEEE/CVF International Conference on Computer Vision.

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(2023). Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences. In the 26th International Conference on Artificial Intelligence and Statistics.

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(2022). How Good are Low-Rank Approximations in Gaussian Process Regression?. In 36th AAAI Conference on Artificial Intelligence (oral).

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(2021). Scalable and Interpretable Marked Point Processes. In arXiv preprint.

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(2021). Large Scale Multi-Label Learning using Gaussian Processes. In Machine Learning Journal.

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(2019). Measures of Neural Similarity. In Computational Brain & Behavior Journal.

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(2019). Extreme multi-label learning with Gaussian processes. In Ph.D. thesis.


(2018). Fully Scalable Gaussian Processes using Subspace Inducing Inputs. In arXiv preprint.

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  • University of Cambridge, Department of Engineering, Trumpington Street, Cambridge, CB2 1PZ