Researcher
Analyst
Communicator

Ongoing and previous projects below.
You’ll also find some drafty project ideas there.

Ongoing

  • A Knowledge Graph of Public AI Data (blog, wikibase, repo)
  • Citation Location Needed, to be presented at Wikimania 2026’s research track (blog)
  • DemocraTree: An Inventory and Interactive Map of Democratic Forms, to be presented at the Democracy & Digital Citizenship 2026 conference,
  • Co-Modelling, an ongoing reflection about the idea and practice of collective modelling,
  • CommonGraph, a prototype platform builder for graph-based knowledge co-production and collaboration.

Previously

  • XR & AI app development for cognitive and visual impairment (Animorph Co-op & CrossSense)
  • Data analysis and modelling for environmental monitoring during Covid 19 crisis (UKHSA, DHSC):
    • Multivariate, spatiotemporal modelling of Covid 19 prevalence from wastewater data in England.1 (blog)
    • Lessons Learned from the United Kingdom National COVID-19 Surveillance Programmes in Monitoring Wastewater. 23
  • Modelling exoplanets with deep learning (UCL):
    • 2023 PhD thesis: time series analysis, deep learning for exoplanetary transit modelling 4
    • Ariel Machine Learning Data Challenges (ECML 20195, 2020, 2021, NeurIPS 202267)
    • XAI for astmospheric retrievals8
    • Direct imaging, deep learning, GANs 9
  • Detecting star clusters with clustering algorithms in Gaia (at University of Barcelona)
    • 2018 A&A follow-up paper10
    • 2017 Master’s thesis11

  1. Morvan, Mario, Anna Lo Jacomo, Celia Souque, et al. ‘An Analysis of 45 Large-Scale Wastewater Sites in England to Estimate SARS-CoV-2 Community Prevalence’. Nature Communications 13, no. 1 (2022): 1. https://doi.org/10.1038/s41467-022-31753-y. ↩

  2. Wade, Matthew, Davey Jones, Andrew Singer, et al. Wastewater COVID-19 Monitoring in the UK: Summary for SAGE – 19/11/20. 2020. https://assets.publishing.service.gov.uk/media/5fc8d6a2e90e07629f7fe1c6/S0908_Wastewater_C19_monitoring_SAGE.pdf. ↩

  3. Wade, Matthew, Anna Lo Jacomo, Elena Armenise, et al. ‘Understanding and Managing Uncertainty and Variability for Wastewater Monitoring beyond the Pandemic: Lessons Learned from the United Kingdom National COVID-19 Surveillance Programmes’. Environmental Sciences. Earth and Space Science Open Archive, ahead of print, 26 July 2021. World. https://doi.org/10.1002/essoar.10507606.2. ↩

  4. Morvan, Mario. ‘Deep Learning, Shallow Dips: Transit Light Curves Have Never Been So Trendy’. PhD thesis, University College London, 2023. https://discovery.ucl.ac.uk/id/eprint/10163203/1/Morvan_10163203_thesis_revised.pdf. ↩

  5. Nikolaou, Nikolaos, Ingo P. Waldmann, Angelos Tsiaras, et al. ‘Lessons Learned from the 1st Ariel Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots’. RAS Techniques and Instruments 2, no. 1 (2023): 695–709. https://doi.org/10.1093/rasti/rzad050. ↩

  6. Yip, Kai Hou, Ingo P. Waldmann, Quentin Changeat, et al. ‘ESA-Ariel Data Challenge NeurIPS 2022: Inferring Physical Properties of Exoplanets From Next-Generation Telescopes’. Paper presented at Neurips 2022 Competition track. 29 June 2022. https://doi.org/10.48550/arXiv.2206.14642. ↩

  7. Yip, Kai Hou, Quentin Changeat, Ingo Waldmann, et al. ‘Lessons Learned from Ariel Data Challenge 2022 - Inferring Physical Properties of Exoplanets From Next-Generation Telescopes’. Proceedings of the NeurIPS 2022 Competitions Track, 31 August 2023, 1–17. https://proceedings.mlr.press/v220/yip23a.html. ↩

  8. Yip, Kai Hou, Quentin Changeat, Nikolaos Nikolaou, et al. ‘Peeking inside the Black Box: Interpreting Deep-Learning Models for Exoplanet Atmospheric Retrievals’. The Astronomical Journal 162 (November 2021): 195. https://doi.org/10.3847/1538-3881/ac1744. ↩

  9. Yip, Kai Hou, Nikolaos Nikolaou, Piero Coronica, et al. ‘Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning’. In Machine Learning and Knowledge Discovery in Databases, edited by Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, and Céline Robardet. Lecture Notes in Computer Science. Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-46133-1_20. ↩

  10. Castro-Ginard, A., C. Jordi, X. Luri, et al. ‘A New Method for Unveiling Open Clusters in Gaia. New Nearby Open Clusters Confirmed by DR2’. Astronomy and Astrophysics 618 (October 2018): A59. https://doi.org/10.1051/0004-6361/201833390. ↩

  11. Morvan, Mario. ‘Searching for Open Clusters with Density-Based Clustering Algorithms in Gaia Era’. Master thesis, University of Barcelona, 2017. https://hdl.handle.net/2445/225126. ↩