Hi there

My name is Sai Manvith Chatrathi. I'm a Computer Science student at UMass Boston, focused on artificial intelligence, machine learning, and software engineering. I'm particularly interested in reinforcement learning, language models, and intelligent systems that learn from interaction and feedback.

My interests center on how learning-based systems can reason, adapt, and make better decisions. Alongside AI/ML, my work spans software engineering, full-stack development, and embedded systems, allowing me to approach technical problems from both research and engineering perspectives.

My current interests include reinforcement learning and modern foundation models as I prepare for graduate research in artificial intelligence and machine learning. Below are selected projects that reflect my research interests and engineering experience.

Research & Engineering Projects

Selected work across intelligent systems, software, and computing.

My projects span language-model agents, multimodal AI, machine learning, distributed systems, backend engineering, operating systems, and real-time embedded computing. Across these areas, I am especially interested in building intelligent systems that can reason over information, learn from interaction and feedback, make decisions under uncertainty, and remain reliable when deployed in complex real-world environments.

These projects represent a selected portion of my work. I have developed additional research, university, personal, and engineering projects beyond those highlighted here.

Experience

Research and engineering experience across AI, software systems, intelligent interfaces, and real-world computing.

Machine Learning
Deep Learning
PyTorch
TensorFlow
EEG / EMG
Signal Processing
React.js
Flask

Research Focus

I work on machine-learning systems for analyzing EEG and EMG signals in neurorehabilitation research, with a focus on translating physiological sensor data into computational representations that can support motor-intent prediction. My work spans the complete research pipeline, from data preprocessing and feature extraction to model development, evaluation, inference, and visualization.

Model Development

I developed deep-learning pipelines using PyTorch and TensorFlow for real-time analysis across more than 10,000 experimental data samples and built and evaluated models for motor-intent classification, achieving approximately 85% classification accuracy. I also optimized preprocessing, feature-extraction, and inference workflows, reducing processing time by approximately 30%.

Research Systems

Beyond model development, I built React.js and Flask-based visualization and monitoring tools that allow research teams to inspect model outputs and rehabilitation metrics. I have worked across machine learning, backend services, data pipelines, and user-facing research software, giving me experience connecting experimental models with usable end-to-end systems.

Research Direction

This research has shaped my interest in intelligent systems that must learn from noisy real-world observations, generalize beyond individual examples, and produce decisions that can be evaluated in meaningful application settings.

Python
React.js
Three.js
Flask
Raspberry Pi
BLE / RFID
Docker
NGINX

Engineering Focus

I contribute to the development of a real-time 3D indoor and outdoor navigation platform that combines interactive software, routing algorithms, wireless localization, and distributed embedded devices. The system connects React.js and Three.js visualization with Python-based services and physical sensing infrastructure.

Navigation & Localization

I developed core navigation features and routing services for real-time path computation and integrated BLE, RFID, and Raspberry Pi mesh nodes for localization. This integration improved location accuracy by approximately 25% while requiring coordinated development across frontend software, backend APIs, networking, and embedded hardware.

Infrastructure

I also built and maintained backend services and REST APIs and worked with Dockerized microservices, NGINX, and Firebase-based deployment workflows. These improvements reduced deployment overhead by approximately 35% while supporting greater system availability and reliability.

Systems Perspective

Working on PACE has given me experience with intelligent systems that operate beyond a purely digital environment: software must interpret observations from physical infrastructure, compute useful actions in real time, and remain robust as sensing and environmental conditions change.

Large Language Models
PyTorch
TensorFlow
Hugging Face
LoRA
RLHF
Model Evaluation
Responsible AI

Research Focus

Through Stanford University coursework using Stanford datasets, I worked with large-scale machine-learning and language-model pipelines using PyTorch, TensorFlow, and Hugging Face. My work focused on model experimentation, optimization, evaluation, and understanding how training choices affect model behavior across datasets.

Model Optimization

I fine-tuned language models and implemented parameter-efficient and feedback-based optimization techniques including LoRA and RLHF, reducing model training time by approximately 40%. I also developed automated evaluation, benchmarking, and monitoring workflows for comparing model performance across experiments.

Evaluation

I built tools for model evaluation and bias analysis and examined robustness, reliability, and generalization across multiple datasets. This work gave me practical exposure to the challenges involved in adapting foundation models efficiently while evaluating more than aggregate model performance alone.

Research Direction

The experience contributed directly to my current research interests in reinforcement learning, learning from feedback, foundation-model behavior, and the design of AI systems that can adapt their behavior through interaction and evaluation.

Across my research and engineering experience, I have worked at multiple layers of intelligent systems: from physiological data and machine-learning models to language-model optimization, real-time navigation, distributed software, APIs, and embedded devices. These experiences have shaped my interest in building AI systems that can learn from data and feedback, reason under uncertainty, interact with complex environments, and remain reliable when deployed in real-world settings.

Contact

I'm always interested in discussing research, graduate opportunities, AI/ML, software engineering, and challenging technical problems. I'm particularly interested in opportunities to contribute to meaningful research and build intelligent systems with real-world impact. Feel free to reach out.

Reach out on Email, LinkedIn, or Phone.

Hi there, I’m

Sai Manvith

Hi there

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