Hi, I'm Arav!

MS Computer Science Student | AI/ML Engineer | Healthcare NLP Researcher

Graduate student at UMass Amherst (May 2026) specializing in AI/ML and Natural Language Processing. Building innovative solutions at the intersection of healthcare and artificial intelligence to make medical care more accessible across languages and cultures.

Work Experience Research Projects

Building Model Context Protocol servers for GenAI platform at UMass Center for Data Science•

Developing perceptual hash-based image similarity detection for open-source RescueBox forensics platform•

Researching LLM-powered medical translation at UMass BioNLP Lab•

Building Model Context Protocol servers for GenAI platform at UMass Center for Data Science•

Developing perceptual hash-based image similarity detection for open-source RescueBox forensics platform•

Researching LLM-powered medical translation at UMass BioNLP Lab•

Building Model Context Protocol servers for GenAI platform at UMass Center for Data Science•

Developing perceptual hash-based image similarity detection for open-source RescueBox forensics platform•

Researching LLM-powered medical translation at UMass BioNLP Lab•

About Me

Get to know me!

Hi, my name is Arav and I am a Bay State Scholar, AI/ML Engineer, and Healthcare NLP Researcher based in Amherst, MA.

I am currently pursuing my Master's in Computer Science at the University of Massachusetts Amherst with a 4.0 GPA, specializing in Data Science and Natural Language Processing. I recently completed my Bachelor's in Computer Science from UMass Amherst with a minor in Economics (GPA: 3.93), where I focused on data-driven solutions and machine learning applications.

My research interests lie at the intersection of Natural Language Processing and healthcare, where I work on developing innovative solutions for clinical text analysis and multilingual medical data processing. I'm particularly passionate about creating AI systems that can make healthcare more accessible and efficient across different languages and cultures.

I believe in leveraging AI/ML and NLP to solve real-world challenges and creating scalable, production-ready solutions that drive meaningful impact. My expertise spans machine learning, natural language processing, and healthcare AI applications.

My Skills

Python

TypeScript

JavaScript

Java

PyTorch

React

Node.js

Spring Boot

Flask

Express

LangGraph

AWS

Git

Docker

Kubernetes

Apache Spark

MySQL

PostgreSQL

Jira

EducationView Transcript

Master of Science in Computer Science

University of Massachusetts Amherst

Expected Graduation: May 2026

GPA: 4.0

Concentration:

Data Science

Achievements:

Graduate Bay State Scholarship
Data Science for the Common Good Fellow (Summer 2025)

Relevant Coursework:

Machine LearningReinforcement LearningTrustworthy & Responsible AIAdvanced Natural Language ProcessingAlgorithms for Data ScienceAdvanced Information RetrievalStatistics for Data Science

Bachelor of Science in Computer Science

University of Massachusetts Amherst

Graduated May 2024

GPA: 3.93

Minor:

Economics

Achievements:

Chancellor's Scholarship
Dean's List (All Semesters)

Relevant Coursework:

Software EngineeringData Structures & AlgorithmsAdvanced AlgorithmsOperating SystemsDatabase Management

Work Experience

Sep '25 - Present

Boston, MA

Software Engineering Intern

UMass Center for Data Science and AI

Designed and deployed Model Context Protocol (MCP) servers to enable document generation (PDF, DOCX) directly through LLM chat interfaces, improving content workflow efficiency by ~40%.
Implemented an MCP connector for Amazon Athena, enabling natural language querying of AWS-hosted databases through LLMs, cutting query formulation and debugging time by ~75%, empowering non-technical users to perform structured data seamlessly.

May - Aug '25

Boston, MA

Data Science Fellow

UMass Center for Data Science and AI

Led development of Media Cloud classifier pipeline, a fully automated, containerized BERT-based classifier processing 100K+ news articles from a 2B+ corpus with 96% accuracy, automating ingestion, labeling, and model training.
Implemented Optuna-based hyperparameter optimization and dashboard-driven evaluation, boosting reproducibility and deployment-ready ML workflows.

May - Sep '24

Los Angeles, CA

Machine Learning Intern

Prime Focus Technologies

Developed a RAG-powered support chatbot using LangChain and FAISS vector database to handle 500+ daily queries, achieving customer satisfaction improved by 30% and achieving 88% user satisfaction.
Created an automated query classification system that reduced manual triage and saved $15K annually in support costs.
Deployed end-to-end production-grade conversational AI systems with JavaScript frontend, Spring Boot microservices, Flask APIs on AWS Lambda, and Kubernetes, achieving <200ms response time and 99.5% uptime.

Sep '23 - May '24

Amherst, MA

Undergraduate Course Assistant

UMass Amherst

Conducted 5+ weekly office hours, assisting 50+ students with code troubleshooting and learning support.
Graded 200+ assignments with precision, offering constructive feedback to support student growth.

Research Experience

Jan '25 - Present

Amherst, MA

LLM Researcher

UMass BioNLP Lab

Developed MedCOD framework integrating UMLS and LLM-KB knowledge sources to enhance English-to-Spanish medical translation — improving translation quality by 80% (BLEU ↑ from 24.47 → 44.23) through structured prompting and LoRA fine-tuning.
Published research in EMNLP 2025 Findings, contributing a novel approach to domain-specific translation addressing healthcare communication barriers for limited English proficiency populations.

Aug - Dec '24

Amherst, MA

ML Engineer

UMass RescueBox

Contributed to RescueBox, an open-source digital forensics platform developed by UMass Rescue Lab for processing large-scale digital evidence. The system enables forensic professionals and rescue operations to rapidly analyze thousands of images and audio files using ML-powered automation — critical for time-sensitive investigations.
Engineered and deployed modular forensic analysis plugins, including deepfake detection and perceptual hash-based image similarity systems. Built RESTful APIs, PostgreSQL + pgvector-backed vector search, and auto-generated UIs, reducing manual forensic analysis time by ~70% and improving cross-platform accessibility.
Optimized inference and data pipelines by converting PyTorch models to ONNX and integrating ONNX Runtime for real-time performance, achieving 3× faster inference speeds and enabling large-scale duplicate image detection (10K+ images) through high-throughput perceptual hashing algorithms (pHash, dHash, PDQ, etc.).

Projects

YOLO Knowledge Distillation

An optimized knowledge distillation framework for YOLOv8 using PyTorch. Trained and evaluated across CIFAR-10, Tiny-ImageNet, and Oxford Pets datasets. Research paper attached below.

PyTorchPythonDeep Learning

UMass Outing Club Gear Locker

Scalable REST API using Express.js/TypeScript with Firebase Real-time Database, handling 100+ daily transactions. Led a team of 3 developers implementing Agile methodologies and CI/CD pipeline with GitHub Actions.

TypeScriptExpress.jsFirebase

Run!

Developed an educational game in C# and Unity, inspired by Dead by Daylight. Players solve mental math puzzles to evade ghost pursuers, activate beacons, and survive the night. Created procedural terrain generation for diverse environments and implemented A* pathfinding with Unity's NavMesh for adaptive ghost AI and dynamic chases. Demo linked below.

C#UnityGame Development