Hello, I am Atharva! I am currently an MSCS student at Northeastern University.
I am interested in reinforcement learning research and its applications in the industry. My focus is mainly on hierarchical reinforcement learning and its applications in multi-agent and partially observable environments.
I have been actively doing ML and AI projects/internships since my undergrad. Notably, I did a research internship at AllGo Embedded systems for driver activity detection.
Previously have full-time work experience at redBus India where I worked as a Software Engineer in the data engineering team. My work included creating Big Data Pipelines for a Customer Life Cycle Management System. I worked on various big data technologies like Spark, AWS Athena, AWS Glue, Mongo, DB, etc. through which I got experience in Database handling and management.
In my free time, I enjoy playing the guitar, listening to music, and gaming.
This section shows some of my recent projects.
Comparison of Option-Critic and Actor-Critic Architectures in Multi-Agent Reinforcement Learning
Achieved near state-of-the-art performance by training agents using option-critic and actor-critic algorithms for centralized multi-agent RL on four rooms and petting zoo MPE environments.
Distributed Real-Time Collaborative Editor
Developed a responsive app for a distributed text editor (like google docs) that can support multiple users updating documents in real-time and is fault tolerant by leveraging an architecture with data replication management and distributed transactions.
Distributed Hierarchical Agglomerative Clustering for Music Recommendation
Evaluated distributed k-means clustering with average linkage clustering on Apache Spark using AWS EMR to group similar songs together from million song dataset for music recommendations.