David is a senior research scientist with twenty years’ experience developing advanced statistical models. David’s career began in theoretical physics (PhD) in the field of quantum photonics researching light-matter interactions in photonic crystals, a potential platform for quantum computing.
He then spent ten years working for a quantitative hedge fund developing high frequency trading systems and quantitative investment strategies. During that time, he researched and took to production eight major systems, including orderbook trading, trend-following, and mean-reversion.
David’s particular expertise is in representation learning, using linear algebra and neural networks to transform unstructured data, notably text, into simpler mathematical forms better suited to information retrieval and business intelligence.
David is a driven data scientist, determined to stay at the bleeding-edge of research, and keen to mentor bright, young, emerging data scientists.
He then spent ten years working for a quantitative hedge fund developing high frequency trading systems and quantitative investment strategies. During that time, he researched and took to production eight major systems, including orderbook trading, trend-following, and mean-reversion.
David’s particular expertise is in representation learning, using linear algebra and neural networks to transform unstructured data, notably text, into simpler mathematical forms better suited to information retrieval and business intelligence.
David is a driven data scientist, determined to stay at the bleeding-edge of research, and keen to mentor bright, young, emerging data scientists.