TECH TALKS

Old meets new – Combining operational research and machine learning to make optimal supply chain decisions.

Faculty Data Scientist Tobias Schwedes outlines robustness in AI, a framework for credibility and how it is applied in practice.

Faculty Data Scientists Mark Worrall and Christian Donnerer discuss using and creating private synthetic data.

Faculty Data Scientist Giles Shaw gives an overview of machine learning’s role in identifying tampered images

Faculty Lead Data Scientist Scott Stevenson discusses training ML models to identify if two pieces of audio contain the same voice.

Faculty Data Scientist Omar Sosa gives an introductory talk about Bayesian inference.

Faculty’s Research Scientist Christopher Frye discusses the techniques that are most effective for detecting and explaining anomalies in data. 

Faculty Research Scientist Christopher Frye discusses AI safety and shapley values for AI explainability.

Faculty Lead Data Scientist for Consumer Business, Gary Willis outlines use cases and applications of ML for marketing including approaches like propensity and uplift modelling. 

Faculty Lead Data Scientist Scott Stevenson and ML Engineer Victor Zabalza will share insights on building tools and workflows to monitor ML systems.

In this talk, Faculty Data Scientist Omar Sosa will provide an introduction to hierarchical modelling, how it can be used, how it’s implemented and the limitations of using this approach.

In this talk, Faculty’s Director of AI, Ilya Feige, and R&D Lead Tom Begley will demonstrate how we build demand forecasting tools that organisations can use and trust.