Professional profile · Updated 2026

Experience with evidence.

Research, industry delivery, peer-reviewed work, and a production-minded AI/ML toolkit—all in one detailed, printable profile.

3.7 / 4.0M.S. GPA
IEEEPublished
3Research + roles
9.68B.Tech CGPA

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Raaj Patel

Raaj Atulkumar Patel

AI/ML Engineer | AI Research | Machine Learning Systems

rajp98250@gmail.com|pate2682@pnw.edu|github.com/Raajp10|raajpatel.online

Hammond, IN | AI/ML Engineering Internship (US)

Summary

M.S. Computer Science student and AI/ML Research Assistant building end-to-end machine learning systems in Python and PyTorch, spanning deep learning, LLMs and RAG, time-series anomaly detection, and computer vision. IEEE-published author. Seeking Big Tech AI internships and AI research lab roles.

Projects

DERGuardian — Cyber-Physical Anomaly Detection for DER Systems

Python | OpenDSS | PyTorch | Time-Series ML
  • ●Built: Research pipeline for DER anomaly detection with simulation-grounded telemetry, synthetic cyberattack scenarios, detector benchmarking, and held-out evaluation.
  • ●Evaluated: Threshold, Isolation Forest, autoencoder, GRU, LSTM, and Transformer detectors across 5s/10s/60s/300s windows, keeping benchmark and held-out synthetic results separated.
  • ●Result: Canonical benchmark: Transformer @ 60s window, F1 = 0.8182.
  • ●Extended: Explored a lightweight LLM as an experimental extension for anomaly-pattern explanation, kept separate from the core detector benchmark.

AI Expense Tracker with RAG-Enhanced Financial Reasoning

FastAPI | SQLite | Next.js | LLMs | RAG
  • ●Problem: Financial queries need trustworthy answers grounded in real data, not model-generated numbers.
  • ●Built: Full-stack system converting natural language -> SQL -> retrieved context -> grounded explanations.
  • ●Built: RAG pipeline where numerical values are fetched directly from SQLite; the LLM is used for explanation only.
  • ●Built: Single LLM integration point supporting 5 backends: Ollama (local), OpenAI, Groq, HuggingFace, and vLLM-style inference.
  • ●Built: CRUD APIs, analytics SQL, budget vs actual analysis, anomaly detection, plus schema/index tuning.

Forest Fire Severity Detection (IEEE Xplore)

PyTorch | OpenCV | Computer Vision
  • ●Built: End-to-end computer vision pipeline using PyTorch and OpenCV.
  • ●Built: Applied preprocessing, augmentation, training, and evaluation.

Selected Publication

P. K. Barik, J. Suthar, and R. Patel, “Forest Fire Severity Detection using AI,” 2025 International Conference on Sustainable Energy Technologies and Computational Intelligence (SETCOM), IEEE, 2025. DOI: 10.1109/SETCOM64758.2025.10932627.

Experience

Graduate Research Assistant — AI and Cyber-Physical Systems

Jul 2025 - Present

Purdue University Northwest | Advisor: Prof. Shafkat Islam

  • ●Design time-series ML detection pipelines for cyberattacks in Distributed Energy Resource (DER) systems using high-frequency electrical telemetry.
  • ●Build OpenDSS simulations of IEEE 13-bus and 123-bus networks with integrated PV and battery storage (BESS) assets for controlled anomaly studies.
  • ●Benchmark 6 anomaly detectors across 5s, 10s, 60s, and 300s windows; a Transformer at 60s achieved benchmark F1 = 0.8182 on the canonical evaluation.
  • ●Generate and validate attack scenarios covering false data injection, spoofing, and command manipulation, with reproducible preprocessing and evaluation pipelines.

Navarang Engineering Works

Jul 2024 - Jul 2025

Software Developer (Contract)

  • ●Built and deployed the company website end to end and introduced digital tooling that reduced manual record-keeping.

Einfochips (Arrow Electronics)

Jan 2024 - May 2024

Engineering Intern

  • ●Supported AI and embedded workflows through Linux automation scripting, debugging, and structured test execution.

Education

Purdue University Northwest

Expected May 2027

M.S. in Computer Science | GPA: 3.7 / 4.0

Hammond, IN

Pandit Deendayal Energy University (PDEU)

May 2024

B.Tech in Information and Communication Technology | GPA: 9.68 / 10 CGPA

Gandhinagar, India

Technical Skills

Languages: Python, SQL, Java, C, Go, JavaScript

Machine Learning: Deep Learning, Neural Networks, Computer Vision, Time-Series Modeling, Anomaly Detection, Intrusion Detection, Feature Engineering, Model Training and Evaluation, Hyperparameter Tuning

LLMs and Generative AI: Retrieval-Augmented Generation (RAG), Prompt Engineering, Text-to-SQL, LLM Evaluation, OpenAI API, Hugging Face, Ollama, Groq, vLLM

Frameworks and Libraries: PyTorch, OpenCV, scikit-learn, NumPy, Pandas, Matplotlib, FastAPI, SQLAlchemy, Next.js, React

Data and Systems: SQLite, Relational Design, Indexing, Analytics SQL, REST APIs, Data Pipelines, Git, Linux, Jupyter, OpenDSS, AWS (fundamentals)