Qiulin Li
PhD Researcher · Budding Ecologist · Machine Learning & Ecology · University College London
About
I am a PhD researcher at University College London in PNL! (People and Nature Lab). Originally from South Africa I completed my undergrad in mathematics before transitioning to computational ecology at UCL. I'm funded throgh the UKRI AI-INTERVENE DFA to look at forest resilience under Prof. Daniel Maynard. Broadly speaking, I'm interested in applying machine learning models to different ecological situations. Some things I like to think about:
- Causality and causal inference
- Climate change
- Forests and biodiversity
- Functional ecology
- XAI (Explainable (perhaps interpretable!?) AI)
- Graph Neural Networks
- Deep learning
Currently I'm looking at how to can use graph-based algorithms to find drivers of forest resilience on a global scale. Outside of my research my ever growing list of hobbies consists of mma (kickboxing, muay thai, bjj, all the fun stuff!), running, yoga, hiking and travelling. I also enjoy playing guitar and reading when I'm not too busy exercising or outside enjoying nature!
I have always been big on community outreach and giving back to the community that raised me. As one of the organisers of IndabaX South Africa, I’ve seen first-hand the impact and importance that creating opportunities for underrepresented communities to learn, connect, and grow has. If you have ideas, collaboration opportunities, or initiatives where I could contribute, please don’t hesitate to reach out.Research
Functional diversity and forest stability
Investigating how functional diversity influences the stability of forest productivity under changing environmental conditions.
GNN-based Functional trait network
Can GNNs produce better representations of n-dimensional functional trait networks for downstream tasks?
Machine learning for ecology
Applying interpretable machine learning, graph-based methods and causal approaches to investigate ecological relationships at scale.
Publications
Generative Model for Small Molecules with Latent Space RL Fine-Tuning to Protein Targets
U Mbou Sob, Q Li, M Arbesú, O Bent, AP Smit, A Pretorius (ICML Workshop, 2024) https://doi.org/10.48550/arXiv.2407.13780
How to use vehicle sensors to make cities more sustainable
Miltenburg, A., Gabriëls, J. and Li, Q, (FruitPunch AI, 2022) DOI
Academic Experience
PhD Candidate
University College London · Genetics, Evolution & Environment
Data Scientist
Industry
Research Intern (BioRL)
Instadeep
Bachelor of Science in Applied Mathematics (Honours)
University of Cape Town
Contact
I am interested in collaborations around ecology, biodiversity, remote sensing and machine learning.