Summary
Pedro Da Costa Avelar is a research-focused machine learning scientist with 11 years of experience, currently a Postdoctoral Research Associate at The University of Manchester after completing a PhD in Bioinformatics at King's College London. His work blends deep learning, graph neural networks and multimodal integration to tackle biomedical challenges such as cancer prognosis, single-cell cell-type identification and ALS patient stratification, and he has applied nucleotide LLMs to DNA sequences. He has a track record of translating cutting-edge research into practical tools—building core GNN libraries, optimizing COVID-19 forecasting pipelines to run 10× faster, and deploying radiomics workflows with U-Net segmentation. A seasoned educator and mentor, he has taught machine learning at LSE and KCL and co-supervised student projects on NP-complete problems, reflecting a rare mix of theoretical depth and applied systems engineering. Notably, his background spans industry and academia across Brazil, the UK and Singapore, giving him a global perspective on deploying ML in regulated and clinical contexts.
11 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Artificial Intelligence, Doctor of Philosophy - PhD Artificial Intelligence at Federal University of Rio Grande do Sul
Engineer's degree Electrical and Electronics Engineering, Engineer's degree Electrical and Electronics Engineering at Universidade Federal de Uberlândia - UFU
Doctor of Philosophy - PhD Bioinformatics, Doctor of Philosophy - PhD Bioinformatics at King's College London
Exchange Student Computer Science, Exchange Student Computer Science at University of Glasgow
Portuguese, English, Spanish, French, Chinese, Catalan, Italian