Rose Sathyanathan

[Address]

Education

2025—Present

M. Tech (Research) Computational and Data Sciences

2021—2025

B. Tech Computer Science

2009—2019

ICSE

2023—Present

Technical Lead

Engineered a semantic search system for a large document corpus, able to effectively retrieve relevant extracts.
Re-engineered existing server-based application backend with a serverless architecture, enabling automatic global scaling and reducing costs and operational overhead.
Optimized dynamic routing and streaming from multiple LLM endpoints, cutting end-user latency, improving throughput and mitigating downtime under peak loads.
Fine-tuned BART with a classification head to serve as a zero-shot text classifier, and deployed the model as a containerized microservice for inference in production.

2022—2023

Full Stack Engineer

Designed and developed several applications for the web with NextJS.
Architected and built a microservices-based backend with Express on NodeJS.
Deployed and maintained applications and services (MongoDB, Redis) on AWS, while optimizing for cost.
Set up continuous integration pipelines for automated testing and deployment, with Github Actions and Jenkins.

2022—2024

CS Subsystem Lead

Led development of the autonomy stack that powered the team’s top-40 finish at SAUVC 2023 and a top-10 placement in 2024, including simulation, perception, and control components.
Set up a ROS-compatible underwater simulator/testbench that mirrored real-world dynamics, enabling end-to-end testing of navigation and control systems.
Implemented rudimentary underwater SLAM using stereo vision and a depth sensor under severe visibility constraints, along with classical CV techniques (HOG, edge and line detection) for object recognition.
Architected and implemented low-level motor control with compensation for non-linear thrust characteristics across several heterogeneous motors, and along with a high-level DoF based control system with an integrated PID controller for stable underwater movement.

Final Year Project

Improved on the LMDX paper by proposing a better prompting strategy, paired with a constrained decoding approach to reduce hallucinations and inaccuracies.
Achieved a slight improvement in performance over the current best achieved by the original paper.

Hobby project

Designed an incomplete parser that can generate an AST from valid Wolfram Language code.
Built an interpreter that provides a REPL environment, and can run also scripts.
Implemented several built-in operators and functions, and provide basic computer algebra capabilities with NumPy/SymPy.

Rose Sathyanathan, Kinshuk Vasisht, Danish Pruthi

Findings of the Association for Computational Linguistics (ACL 2026)

https://doi.org/10.48550/arXiv.2605.03050

Experienced with ROS and Robotics Systems
Proficient in JavaScript, Python, Go and C/C++
Familiar with PyTorch, TensorFlow, Python DS stack (NumPy, Pandas, Matplotlib), CUDA