Robert Munro is a computational linguist in the area of communication technologies and works largely on less resourced languages. As a graduate fellow at Stanford University, much of his research involves topics such as crowdsourcing and machine learning. Mr. Munro originally came into the field through his previous research experience. After graduating from University of Sydney in 2004 with majors in Linguistics, Computer Science, Information Systems, and English and Film Studies, he proceeded to work on the Hans Rausing Endangered Languages Project at the University of London. Mr. Munro worked as the project’s software developer – designing digital archives, working with multimedia development, and researching into computational linguistics.
19 April 2012
ICT4D Professional Profile: Robert Munro
After his original experience with HRELP, Mr. Munro proceeded to work on many ICT4D projects worldwide. For example, Mr. Munro was involved with the Mission 4636 service during the January 12th, 2010 earthquake in Haiti. With this service, Haitian’s were able to text their medical needs and receive aid. Mr. Munro helped to coordinate the translation and categorization of text messages that were received. With the help of Crowdflower, their crowdsourcing platform, Mr. Munro and his colleagues were able to translate the messages within ten minutes. Overall, the initiative was successful and they were able to process more than 80, 000 messages – “the first time that crowdsourcing had been used for real-time humanitarian relief and the largest deployment of humanitarian crowdsourcing to date.”Along with crowdsourcing efforts, one of Mr. Munro’s major areas of interest includes machine loading. In 2011, Mr. Munro worked as Chief Technology Officer at the Global Viral Forecasting, an initiative dedicated to predicting and preventing the emergence of new disease outbreaks. In particular, he worked with a system called EpidemicIQ. With the help of thirty labs worldwide, the team, currently, is able to gather information about epidemics and load them into the system to filter out what is relevant. The machine-loading technique gathers various types of information that it can then use to predict a certain epidemic arising in an area. For example, Google Flu trends determined that flu outbreaks could be predicted by simply tracking the symptoms that are usually searched.Beyond these experiences, Mr. Munro has worked in Sierra Leone as Chief Information Officer for Energy for Opportunity (EFO), an organization devoted to finding a safe and environmentally friendly way of providing electricity to communities throughout West Africa. He currently, “heads the IT services at EFO and does everything from developing software systems to training and acceptance testing” (EFO).When he is not involved in attending conferences or performing research, Mr. Munro enjoys blogging at Jungle Light Speed and traveling around the world.