Arshika Lalan
I am a Pre-Doctoral Researcher at Google Research India, working in the MASSI lab mentored by Prof. Milind Tambe. My work focuses on developing robust bandit algorithms for efficiently delivering health awareness information in underserved communities in India. My CV is available here. My personal website can be found here!.
Research Areas
Authored Publications
Google Publications
Other Publications
Sort By
Adherence Bandits
Jackson A. Killian*
Aditya Mate*
Manish Jain
The Workshop on Artificial Intelligence for Social Good at AAAI 2023 (2023)
Preview abstract
We define a new subclass of the restless multi-armed bandit framework, that we name Adherence Bandits, designed to capture the dynamics prevalent in many public health intervention problems. We discuss key properties of Adherence Bandits, their real-world motivations, how structures lead to both technical and computational advantages, and natural extensions that have been or can be made to the subclass. We summarise key research works that have contributed to the growing sub-area and finish by highlighting future directions of research
View details
Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation
Shresth Verma
Kumar Madhu Sudan
Amrita Mahale
Aparna Hegde
The Workshop in Data Science for Social Good, KDD 2023 (2023)
Preview abstract
Mobile health programs are becoming an increasingly popular medium
for dissemination of health information among beneficiaries in less
privileged communities. Kilkari is one of the world’s largest mobile
health programs which delivers time sensitive audio-messages to
pregnant women and new mothers. We have been collaborating with
ARMMAN, a non-profit in India which operates the Kilkari program,
to identify bottlenecks to improve the efficiency of the program. In
particular, we provide an initial analysis of the trajectories of benefi-
ciaries’ interaction with the mHealth program and examine elements
of the program that can be potentially enhanced to boost its success.
We cluster the cohort into different buckets based on listenership
so as to analyze listenership patterns for each group that could help
boost program success . We also demonstrate preliminary results on
using historical data in a time-series prediction to identify benefi-
ciary dropouts and enable NGOs in devising timely interventions to
strengthen beneficiary retention.
View details
No Results Found
