May 29, 2023
Can AI facilitate the digital financial inclusion of migrant workers ?
As COVID-19 started raising its fang in early 2020, a 21-day lockdown was imposed in the nation to restrict the spread of infection in the country. With the businesses shut and markets closed, millions of migrant workers lost their jobs. With limited access to cash and digital banking services, they struggled to send money back home, which in usual days was done mostly through informal personal networks. Due to the staggered movement of people from cities to villages, the households of migrant workers suffered as they did not have money even to buy the essentials.

4 min read

Aug 12, 2021
AI Powered Automatic Medical Coding of Electronic Health Records
--Sudarsun Santhiappan Jeshuren Chelladurai-- Medical coding (codifying the medical records) has essentially remained a human powered job since its inception. BUDDI.AI aims to change that by partially automating the process using AI technology. To know more about BUDDI.AI, a talk on “AI Powered Automatic Medical Coding of Electronic Health Records'' was organized as a part of second RBCDSAI AImpact Seminar on 12th August 2021. The talk was delivered by Sudarsun Santhiappan (Co-founder of BUDDI.

2 min read

Jan 21, 2021
Fireside chat: with RBCDSAI Distinguished Fellow Prof. Srinivasan Parthasarathy
Ethics in AI is one of the most discussed topics in recent times. Read about the fireside chat with Prof. Srinivasan Parthasarathy where he spoke about AI, Data Science, and Ethics. Get to know the various challenges and solutions embraced by different service providers to keep their clients safe and happy.

3 min read

Sep 5, 2020
Finding Influencers in Social Networks: Reinforcement Learning Shows the Way
Social Network Analysis has given us many tools to effectively manage information dissemination in a social group, study growth and dynamics in such groups, etc. But one of the key challenges when studying social groups of underprivileged or socially marginalized groups is the recovery of the underlying social network itself. This study proposes a machine learning approach for learning to effectively allocate a limited budget to discover the network.

5 min read