Tim Pattenden

Tim Pattenden

After university, a short stay in a City accountancy firm convinced me that what I really should be doing was solving more challenging analytical problems and that is what I have been doing in Tessella for about 13 years. The application of mathematics to solve business problems is a pretty wide field, so I have enjoyed learning about many different areas of human activity, including designing radar systems for the Royal Navy, designing water networks for a utility company, and helping to make assessments of probabilities in international treaty verification. In all these varied and fascinating areas of work similar principles of sound mathematical analysis apply. I am now working to support Tessella's management in both broadening and deepening the analytical skillset we have in the company, helping to develop those skills that can turn a mass of data into valuable insight.

Small and agile or large and powerful: size matters

Businesses must ask themselves, where does data science fit? There has been much hype around data science, and in particular recently about artificial intelligence (AI), with Google’s DeepMind announcing that its programme AlphaGo is able to “create knowledge itself”, and Accenture Research and Frontier Economics suggesting that AI can enable 38 per cent profit gains […]

Is finance learning fast enough from other sectors?

One place you could judge the answer to this might be the MATLAB EXPO, a gathering to discuss the use of MATLAB software across a wide range of industries. At a recent EXPO, I listened to the head of strategic analytics for a leading global bank considering this issue. His view was that banks are […]

Knowledge in a world of uncertainty

Our world is full of uncertain information on which we must base decisions: what is the condition of equipment I cannot inspect directly, and should I replace it? What are the intentions of a neighbouring country, given their declarations and the results of some partial observations I have been able to make? Will this drug […]

Explainability: machine learning and human guidance

Lurking in the shadows of the big data revolution, roams a contentious claim that stubbornly refuses to lie down. Put simply, it says that feeding machine learning algorithms with the right data means that scientific models (or any other type for that matter) are no longer needed to explain why things happen as they do. […]

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