Optimising the placement of sensors/transmitters/ receivers in a network to reduce costs and guarantee robustness.
Facilitating targeted maintenance strategies through analysis of sensors on everyday passenger trains and predicting problems.
Providing smarter maintenance regimes for infrastructure through data driven sensor analysis.
Offering unique insights into modern machine learning, using principled Bayesian inference to build explainable systems
Capturing protein flexibility, calculating binding affinities, and predicting novel binding sites.
PolyChord Algorithm Parsing Himmelblau Function
PolyChord Ltd brings together true academic excellence in data science with commercial expertise to help you optimise your use of sensors. Unlike other data-science companies, we have genuinely unique tools to enhance the placement of sensors in a network and extract useful information from noisy data sets.
In Total Funding from Grants and Investments
Years of Hands-On Experience in Data Science
Of our Data Science Team in the Past Year
Of our Data Scientists are Postdoctoral Research Physicists
Data collection is often the most resource-intensive part of the process of engaging with machine learning. PolyChord can handle “dirty” or even incomplete data sets without any lengthy preparation periods, allowing you to leverage the value of your existing datasets.
PolyChord gives you concrete percentages stating “how good” the solution it has created is. These calculations are done simultaneously with the solutions, and are vital in many applications where safety regulations are paramount. In Machine Learning or Neural Networks, you never have any certainty how good those answers are. This is a key strength for PolyChord.
Unlike other sampling tools, PolyChord does not require tuning. PolyChord effectively maps the entire “data landscape” in a single run, as opposed to doing many short, random, and incomplete explorations of the landscape (like other tools). This makes it more a comprehensive, concrete sampling of a data landscape.