https://www.linkedin.com/in/milestidmarsh/

Miles is a data scientist and economist who applies statistical techniques to improve the value-alignment of artificial intelligence. Active in the Effective Altruism field for 10 years, he has co-founded multiple organizations including Compassion in Machine Learning (CaML), Modeling Cooperation (researching AI competition dynamics), and the Centre for AI Responsibility and Education (CAIRE). He also helped organize EAGxAustralia 2023 and has presented work including 'Making AIs Actually Care About Animals' at Sentient Futures (2025) and 'Modelling Cooperation' (2021).

Miles holds a Masters in Economics from the University of Melbourne and has completed advanced AI training through FastAI's 'Practical Deep Learning for Coders' and Stanford's Machine Learning course. He previously served as a research economist at Australia's Productivity Commission, providing independent policy advice on economic, social, and environmental issues. His core strengths include statistics, leadership, research, and building collaborative relationships across disciplines, organizations, and countries.

https://www.linkedin.com/in/jasmine-brazilek-70b0a4149/ 

Jasmine has over 6 years of experience in the technology industry, focusing on cybersecurity and data science. At Anthropic, she designed security systems for unique use cases, bridging gaps in existing tools. 

Highly productive and detail-oriented, Jasmine has also been involved in effective altruism for 7 years, primarily focusing on earning to give. She completed the ‘Practical Deep Learning for Coders’ course from FastAI and has gained significant skills in ML, primarily during in her time at CAML. She has now trained dozens of models, generated dozens of datasets of thousands of rows and learnt iteratively through experience and reading papers.

Jasmine served as primary reviewer for 'Securing External Deeper-than-black-box GPAI Evaluations' (Tlaie & Farrell, 2025), contributing technical feedback on frameworks for secure external evaluation of general-purpose AI models.