The science behind Cervest

Tackling the most complex and critical climate challenges using Earth Science AI

Cervest is powered by rigorous research led by some of the world’s sharpest academic minds. Our world-leading research team has published more than 80 peer-reviewed scientific papers, cited more than 3,000 times, in renowned publications including:

  • Nature Climate Change 
  • Proceedings of the National Academy of Sciences (PNAS)
  • Bulletin of the American Meteorological Society (BAMS) 
  • Journal of the Royal Statistical Society
  • Astrophysical Journal
  • Advances in Space Research

We also present at top-tier Machine Learning conferences NeurIPS and ICML, and work collaboratively with Imperial College and Oxford University. For our work on frontier machine learning, we partner with the esteemed Alan Turing Institute. Ethics is a frequent topic of research (and a core commitment for all our endeavours): we have presented on ethics in relation to AI, and the first foundational paper we published considered ethics and climate change.

Research Residents

Agnes Schim van der Loeff 

Research Resident

Shahine Bouabid

Research Resident

Ruiao Hu

Research Resident

Srshti Putcha

Research Resident

James Walsh

Research Resident

Mike Zotov

Research Resident

Research Residents: FAQs

Can I see an example of research residence projects?
You can learn more about recent projects, from work on the systemic impact of combining AI and Earth sciences at scale to a project on remote sensing denoising using deep latent variable generative models, on our blog.
What can I expect from a research residency?
Our research programme is very similar to an academic Masters or PhD, but with the addition of experiencing the open and engaging culture of a start-up. You will be reading papers, leading an independent research project, and will be supported and mentored by our science and engineering experts. Near the end of your residency, you will be expected to share your work in the best possible medium. This could be in a blog post, an academic publication, at a conference or in a workshop.
Who should apply?
The most important thing is that you are mission-driven, hungry to learn and have a strong interest in the role of science in climate and how it can shape future policies and societies. Candidates generally should have either a degree or equivalent experience in STEM fields. However, we strongly encourage candidates with non-STEM backgrounds and experience to apply.
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Our current research focus

Computer vision and Bayesian statistics to harmonise datasets across varying timescales, resolutions and geographies, and from different data sources

Machine learning and AI to tie together climate signals from multiple scientific models that previously worked in silos.

Research Partners & Collaborators

Ruiao Hu
Research Resident

A computer scientist by trade and a mathematician at heart, Ruiao graduated from Imperial College London with an MEng degree in Joint Mathematics and Computer Science.

With industry experience in data engineering gained with world-leading finance firms, Ruiao has joined Cervest for the summer to construct data pipelines for a number of our upcoming projects.

Just as he joined Cervest, Ruiao achieved a first class MEng degree with the Governor’s Prize and Corporate Partnership Programme award for his final year project. After his time at Cervest he will pursue a PhD in stochastic geometric mechanics under the supervision of Prof Darryl Holm at Imperial College London.

James Walsh
Research Resident

James is a Statistical Scientist with a bachelor’s degree in the subject awarded from the Statistics department at the University of Warwick.

James joins Cervest as part of his work with the Warwick Manufacturing Group and The Alan Turing Institute where he is a Research Assistant developing Physically Informed Hybrid models.

He will be spending his time at Cervest applying this expertise to simulating individual crop growth and constructing bulk processing technologies for modelling organic carbon content for soil quality monitoring. He has also worked in the risk team of financial consultants at Albourne Partners building simulations.

Mike Zotov
Research Resident

Mike has recently graduated with a first class honours degree in Mathematics from the University of Warwick.

Having studied Machine Learning as part of his degree, he has joined Cervest for the summer to work on deep generative models for de-noising remote-sensing data.

After his time at Cervest he will continue to pursue his passion for Machine Learning with a Master’s degree at Imperial College London.

Agnes Schim van der Loeff
Policy Researcher

Agnes joined Cervest after completing her BA in Arabic and Development Studies from SOAS University of London. She is interested in the intersection of the social, economic and political dimensions of climate change and passionate about climate justice. During her studies she was also engaged in environmental activism, including as environment officer in the SOAS students’ union.

Agnes started at Cervest as a research resident exploring the ethical implications of AI in relation to climate change, which resulted in a paper selected for an oral presentation at the NeurIPS 2019 workshop on AI for social good. Building on this, she is developing an ethical framework for Cervest. As policy researcher she now does research on the regulatory context of Cervest’s work and on emerging policies relating to climate change. 

Shahine Bouabid
Research Resident

Shahine has graduated from École Centrale Paris and ENS Cachan (MVA) majoring in Applied Mathematics.

Driven by his will to foster a science aware of the challenges of our society and human beings, he has joined Cervest to work on multiple remote-sensing imagery products fusion.

After his time at Cervest he will pursue a PhD in Statistics under supervision of Prof Dino Sejdinovic at the University of Oxford.

Srshti Putcha
Research Resident

Srshti is currently a second year PhD student at the STOR-i Centre for Doctoral Training, based at Lancaster University. Her PhD project seeks to develop new methodology for scalable Bayesian inference.

Prior to starting at STOR-i, Srshti studied at the London School of Economics and Political Science. She graduated with a BSc in Mathematics and Economics in 2016 and with an MSc in Statistics in 2017.

Srshti has joined Cervest as a Research Resident within the science team. During her time here, she will be working on designing and implementing models to infer land surface temperature.