Lucia Gordon

PhD Student in Computer Science

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About Me

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Hi, I'm Lucia! I'm a fourth-year PhD student in Computer Science at Harvard’s School for Engineering and Applied Sciences. I'm a researcher in Andrew Davies' lab at Harvard University and the Pioneer Centre for Artificial Intelligence in Denmark, affiliated with Christian Igel's and Serge Belongie's groups at the University of Copenhagen's Computer Science department. I work on machine learning for Earth observation data, in particular remotely sensed imagery. I'm interested in computer vision, active learning, multimodality, dataset imbalance, and machine learning software for ecology. I did my undergraduate studies in Physics and Mathematics at Harvard College, but my passion for protecting the natural world led me to the AI for Conservation community, where I've loved harnessing my quantitative skills for ecology and biodiversity monitoring.

Publications

  1. Esther Rolf, Lucia Gordon, Milind Tambe, and Andrew Davies. “Contrasting Local and Global Modeling with Machine Learning and Satellite Data: A Case Study Estimating Tree Canopy Height in African Savannas.” Journal of Machine Learning Research. Volume 27, Number 45, Pages 1-37. 2026.
  2. Lucia Gordon, Nico Lang, Catherine Ressijac, and Andrew Davies. “Multimodal Fusion Strategies for Mapping Biophysical Landscape Features.” In: Del Bue, A., Canton, C., Pont-Tuset, J., Tommasi, T. (eds) Computer Vision – ECCV 2024 Workshops. ECCV 2024. Lecture Notes in Computer Science, vol 15624. Springer, Cham. https://doi.org/10.1007/978-3-031-92387-6_16.
  3. Lucia Gordon, Esther Rolf, and Milind Tambe. 10/24. “Combining Diverse Information for Collective Action: Stochastic Bandit Algorithms for Heterogeneous Agents.” Frontiers in Artificial Intelligence and Applications. Volume 392: ECAI 2024. Pages 3284-3291. IOS Press.
  4. Lucia Gordon, Nikhil Behari, Samuel Collier, Elizabeth Bondi-Kelly, Jackson A. Killian, Catherine Ressijac, Peter Boucher, Andrew Davies, and Milind Tambe. 8/2023. “Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino Habitats.” Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence AI for Good. Pages 5977-5985. Macao, S.A.R.
  5. Fabio Pacucci, Adi Foord, Lucia Gordon, and Abraham Loeb, Lensing in the darkness: a Bayesian analysis of 22 Chandra sources at z≳6 shows no evidence of lensing, Monthly Notices of the Royal Astronomical Society, Volume 514, Issue 2, August 2022, Pages 2855-2863.
  6. Lucia Gordon, Bao-Fei Li, and Parampreet Singh, Quantum gravitational onset of Starobinsky inflation in a closed universe, Physical Review D 103, 046016 (2021).

Presentations

Highlighted Coursework

  1. Self-Supervised Learning for Earth Observation
  2. Deep Statistics: AI and Earth Observations for Sustainable Development
  3. Algorithms for Data Science
  4. Climate by Design
  5. Design, Technology, and Social Impact
  6. Topics in Machine Learning: Interpretability and Explainability
  7. Introduction to Reinforcement Learning
  8. AI for Social Impact
  9. Machine Learning
  10. Introduction to Probability
  11. Linear Algebra and Real Analysis

Education

Honors

Languages