Wikimedia MLOps

Chris spent over a decade applying statistical learning, artificial intelligence, and software engineering to political, social, and humanitarian efforts. He is the Director of Machine Learning at the Wikimedia Foundation. Previously, Chris was the Director of Data Science at Devoted Health, Director of Data Science at the Kenyan startup BRCK, cofounded the AI startup Yonder, created the data science podcast Partially Derivative, was the Director of Data Science at the humanitarian non-profit Ushahidi and was the director of the low-resource technology governance project at FrontlineSMS. Chris also wrote Machine Learning For Python Cookbook (O’Reilly 2018) and created Machine Learning Flashcards.

Chris earned a Ph.D. in Political Science from the University of California, Davis researching the quantitative impact of civil wars on health care systems. He earned a B.A. from the University of Miami, where he triple majored in political science, international studies, and religious studies.

Neal is currently a Staff Machine Learning Engineer at Monzo Bank in London, where he focus on building machine learning systems to help make money work for everyone (reviewed in 2020, 2021, 2022) and Monzo’s machine learning platform. :airplane: Before joining Monzo, Neal was a Senior Data Scientist at Skyscanner, where he built recommender and ranking systems to improve travel information in the app.

:school: / :calling: Before Skyscanner, Neal was a Senior Research Associate in the Computer Lab at the University of Cambridge, working on healthcare mobile apps that use smartphone sensors. He spun out this research into a startup that was part of Accelerate Cambridge in the Judge Business School.

:mortar_board: Neal did an MSci in Computer Science, PhD on recommmender systems, and first postdoctoral research position on urban data science in the Department of Computer Science at University College London, where he's still an Honorary Research Associate. While at UCL, Neal also spent time as a visiting researcher at Telefonica Research, Barcelona and worked as a Data Science consultant.

:bulb: Neal's work has always focused on systems that use machine learning - this has taken me from recommender systems to urban computing and travel information systems, digital health monitoring, smartphone sensors, banking, and open source machine learning tools. You can read more about Neal's work and research in the Press & Speaking

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.