I am a PhD student at ETH Zurich in the EcoVision Lab, Photogrammetry and Remote Sensing Group under the supervision of Prof. Jan Wegner and Prof. Konrad Schindler. My research interests are super-resolution, depth upsampling/completion, fine-grained classification, biodiversity monitoring, and environmental data.

After completing my master studies, I joined the Machine Learning and Optimisation Lab at EPFL to work on a project which aim was to create an algorithm that could help clinicians better predict the causes of pediatric fever in Africa. Following this experience, I decided to continue to focus my research on using Machine Learning to tackle important global issues. My main work at EcoVision is on estimating biodiversity in Switzerland using satellite images.

Download my CV.

  • Machine Learning
  • Deep Learning
  • Computer Vision
  • Super-Resolution
  • Biodiversity Monitoring
  • PhD in Computer Vision, 2022 (expected)


  • MSc in Machine Learning, 2017

    Imperial College London

  • BSc in Physics, 2016



(2022). Learning Graph Regularisation for Guided Super-Resolution. In CVPR.

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(2022). The Herbarium 2021 Half–Earth Challenge Dataset and Machine Learning Competition. In Frontiers in Plant Science.

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(2021). Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs. In ISPRS.

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(2019). Guided Super-Resolution as Pixel-to-Pixel Transformation. In ICCV.

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NVIDIA Toronto AI Lab
Research Scientist Intern
Jul 2022 – Dec 2022 Zurich
Working in Prof. Sanja Fidler’s team.
Machine Learning and Optimization Lab at EPFL
Scientific Assistant
Oct 2017 – Jun 2018 Lausanne

Working on A Machine Learning Platform for Emerging Viral Diseases in Africa (part of SAFIA project) with Prof. Martin Jaggi and Dr. Mary-Anne Hartley

  • Designing an algorithm that helps clinicians better predict the cause of paediatric fever
  • Potentially helps detecting epidemic outbursts
Argelander-Institut für Astronomie at Bonn University
Research Intern
Jun 2016 – Jul 2016 Bonn

Working on Artificial neural networks handling noisy features with Dr. Malte Tewes

  • Noisy multivariate data, which depend on some physical explanatory parameters
  • Creating a model neural network architecture that finds accurate estimates for parameters


PhD Computer Vision
Oct 2018 – Present Zurich
Ph.D. in the EcoVision Lab, Photogrammetry and Remote Sensing Group, supervised by Prof. Jan Dirk Wegner and Prof. Konrad Schindler.
Imperial College London
MSc Computing (Machine Learning)
Sep 2016 – Aug 2017 London

First Class Honours. Master Project, F1 Overtaking Model with Mercedes F1 Team and Prof. Marc Deisenroth.

  • Creating a probabilistic model for overtaking during a race, key aspect of race simulation engine.
Imperial College London
BSc Physics - Exchange Program
Sep 2015 – Jun 2016 London

Bachelor Project, Machine Learning and Galaxy Surveys with Dr. David Clements.

  • Finding clusters in an unsupervised way and comparing them with the astronomical classification of the galaxies.
BSc Physics
Sep 2013 – Jun 2016 Lausanne



  • Introduction to object-oriented programming (C++) - Fall 2014, Spring 2015
  • Image Interpretation - Fall 2018 - 2020
  • Geodetic Project Course: 3D modeling using different sensors - Summer 2019, Summer 2021


  • Monitoring vitality of urban trees combining airborne imagery and deep learning, Luca Gaia
  • Using machine learning to identify plant species in images acquired by citizen scientists, Yihang She
  • Curriculum learning - helping computers to gradually learn new things, Luca Gaia
  • Detektion von Füssgängerstreifen in Luftbildern, Elie Vuadens

Additional Activities

Organiser of the Herbarium Competition,
Organiser of the Herbarium Competition, Fine-Grained Visual Categorization, CVPR.
Co-Organiser of the tutorial session at MLEG.
Co-Organiser of the tutorial session at the 1st Swiss Workshop on Machine Learning for Environmental and Geosciences, MLEG.
Volunteer Responsible for Speakers
Volunteer responsible for speakers at 2nd Applied Machine Learning Days, AMLD.