W4GL has been researching universal coordination protocols in a distributed environment for some time. One of our particular focuses has been on providing useful information in a crisis situtation.
During crises (such as fires, earthquakes, floods etc) there is often a lot of very useful information on social media and in other places. Unfortunately the social media streams can become unusable because too much rumor and misinformation is mixed in with the useful and actionable intelligence.
It is our hope that by furthering research in the area of this project we can identify and promote methods, partners and a consensus method that will allow a distributed network of agents and people to more reliably (and more rapidly) identify useful information during a crisis.
Who we are
Some of the key individuals involved with this project include:
Linc is a successful entrepreneur and award winning photographer, TEDx speaker and community builder. He initiated the Open Source Open Society conference and was co-founding CEO of hologram company 8i.
Edward Abraham, PhD
Edward is the founder and CEO of Dragonfly Data Science. He enjoys bringing evidence to the table, using information to make clear, principled decisions.
Edward has a PhD from the University of Cambridge, where he studied theoretical physics in the cosmology group led by Professor Stephen Hawking. He worked as an oceanographer for 10 years, researching interactions between the physical and biological environment. Since starting Dragonfly, he has focussed on statistical analysis, with a focus on applications around decision making.
Miles Thompson is a technical architect with more than twenty years experience as developer and technical lead, especially in finance and machine learning. He was CTO and then Senior Technical Architect of the leading independent financial research provider in NYC for many years. He holds a Bsc (Hons I) in Math and BA in Sociology.
Yvan Richard, PhD
Yvan is an ecologist who specialises in using statistical computing software to solve complex problems.
The techniques and methods he learned from work in ecology (including Bayesian analysis, machine learning, spatial analysis, and capture-recapture models) translate perfectly into many other contexts, such as in this project. Yvan’s expertise in R is particularly sought after when people wish to display complex information simply and beautifully.