John Sandall
John Sandall is the CEO and Principal Data Scientist at Coefficient.
His experience in data science and software engineering spans multiple industries and applications, and his passion for the power of data extends far beyond his work for Coefficient’s clients. In April 2017 he created SixFifty in order to predict the UK General Election using open data and advanced modelling techniques. Previous experience includes Lead Data Scientist at YPlan, business analytics at Apple, genomics research at Imperial College London, building an ed-tech startup at Knodium, developing strategy & technological infrastructure for international non-profit startup STIR Education, and losing sleep to many hackathons along the way.
John is also a co-organiser of PyData London, co-founded Humble Data in 2019 to promote diversity in data science through a programme of free bootcamps, and in 2020 was a Committee Chair for the PyData Global Conference. He is currently a Fellow of Newspeak House with interests in open data, AI ethics and promoting diversity in tech.
Sessions
🎵 What happens when you feed an embedding model with folk tunes? 🤖
Our journey starts with a powerful transformer-powered NLP use-case: automated topic modelling using embedding models, clustering algorithms and LLMs. But what if, instead of vibe-based summarisation of free-text survey responses, we wanted vibe-based summarisation of 46,000 folk tunes?
Join us to discover the hidden melodic structures found within music, with 🎻 live demonstrations 🎻 to highlight the differences between a "bluesy reel" and an "Amixolydian jig" discovered through a variety of unsupervised machine learning techniques. Our adventure continues by exploring how to develop a semantic "vibe search" engine for music, a regression model for tune popularity, combined into a folk tune recommender system.
Expect a unique blend of LLM theory, practical advice for applying transformers to text data, code samples, and live violin demos of AI-discovered folk tunes. This talk would be appropriate for anyone curious about LLMs, those looking for ideas on using embeddings for NLP, or anyone who likes foot tapping.