Neythen Treloar holds a PhD in Computational Synthetic Biology, an MSc in Scientific Computing, and a BSc in Natural Sciences from University College London. His PhD research focused on developing reinforcement learning methods for optimal bioreactor control and creating novel algorithms for the design of bacterial biosensors capable of information processing. His expertise spans computational biology, machine learning, and microbial community modelling.

During his undergraduate studies at UCL, Neythen was part of the 2016 iGEM team and later served as a Research Fellow, where he worked on modelling disease progression in infant gut microbiomes and neuronal learning dynamics in C. elegans. He also contributed to research on avian flu outbreaks during a Data Science Fellowship with Faculty AI and the UK Health Security Agency. Neythen previously worked as a Machine Learning Engineer at Carter Labs, where he led the fine-tuning and deployment of large language models and developed AI benchmarking and split-testing strategies for production environments.

At Bactobio, Neythen is a Senior Data Scientist within the Data Science team. He uses statistical techniques to plan efficient laboratory experiments and leads the research and development of machine-learning methodologies for modelling microbial communities and predicting the emergence of significant microorganisms. His work supports the exploration, cultivation, and metabolic stimulation of novel microbes.

Outside of work, Neythen enjoys reading, board games, and staying active through various sports.