Pinpointing where poverty is most severe and tracking its changes over time is crucial for helping communities effectively. However, traditional benchmarks like household surveys and national censuses often fall short—they’re expensive, slow, and infrequent. In countries like Sri Lanka, this means we’re often relying on outdated information, hindering our ability to respond to sudden economic shocks or disasters. On top of that, poverty cannot be determined by income data alone, rather its multidimensional, where factors such as infrastructure, access to services, and economic activity also play a role in determining a community’s well-being. To capture these complexities, our DAP team (Data, Algorithms, and Policy) explored something different: how to rethink the way we measure poverty in Sri Lanka using AI with non-traditional data sources? 
Pinpointing where poverty is most severe and tracking its changes over time is crucial for helping communities effectively. However, traditional benchmarks like household surveys and national censuses often fall short—they’re expensive, slow, and infrequent. In countries like Sri Lanka, this means we’re often relying on outdated information, hindering our ability to respond to sudden economic shocks or disasters. On top of that, poverty cannot be determined by income data alone, rather its multidimensional, where factors such as infrastructure, access to services, and economic activity also play a role in determining a community’s well-being. To capture these complexities, our DAP team (Data, Algorithms, and Policy) explored something different: how to rethink the way we measure poverty in Sri Lanka using AI with non-traditional data sources? 
In a recent episode of “Samata IT” (IT for All) on Jathika Rupavahini, LIRNEasia’s Senior Research Manager and Data, Algorithm, and Policy Team Lead Merl Chandana shed light on the dynamic landscape of data science and how it offers ample opportunities for individuals from diverse backgrounds. “Data science is an evolving field which is still being developed. Therefore, there are not many seasoned experts in this filed, which means there is a significant scope for newcomers to make their mark,” Merl said. Contrary to conventional perceptions, he stressed that one doesn’t necessarily need prior experience in data science to pursue a career in the field. “What matters most is not the years of experience, but the willingness to learn. 
On 2nd October 2023, Research Manager and Team Lead (Data, Algorithms, and Policy) Merl Chandana, alongside Junior Researcher Chanuka Algama, held a session titled ‘Applied data science research for social good’ at the University of Kelaniya’s Department of Statistics and Computer Science. The session delved into LIRNEasia’s journey of forming a data science team and using large datasets to yield critical insights for public policy. They contrasted LIRNEasia’s applied data science approach with traditional academic research and private sector practices. Additionally, they highlighted the emerging ‘AI for Social Good’ movement and its potential as a career avenue. The slides used can be accessed below.
Keynote presentation for South Eastern University, 10th Annual Science Research Sessions 2021, 30 November 2021 - by Rohan Samarajiva, LIRNEasia
In our big data for development work, we collaborate with data-savvy economists as well as economists who can code. Within Sri Lanka, we have not found them, but we keep looking. But looks like this is the future of economics. But what the tech economists are doing is different: Instead of thinking about national or global trends, they are studying the data trails of consumer behavior to help digital companies make smart decisions that strengthen their online marketplaces in areas like advertising, movies, music, travel and lodging. Tech outfits including giants like Amazon, Facebook, Google and Microsoft and up-and-comers like Airbnb and Uber hope that sort of improved efficiency means more profit.