🎓 Graduate Student | Data Science, Analytics, and Engineering @ Arizona State University
🤖 Aspiring Data Scientist | ML Engineer | AI Explorer
I am a driven and analytical data enthusiast passionate about transforming data into impactful solutions. With hands-on experience in Data Science, Machine Learning, and AI, I specialize in extracting insights, optimizing models, and delivering actionable outcomes.
- 🔍 Exploring advanced topics in Statistical Machine Learning, NLP, Deep Learning, and Cloud AI
- 🤖 Currently diving into Agentic AI, Retrieval-Augmented Generation (RAG), and Generative AI, building innovative projects while upskilling through specialized courses
- 🤝 Open to collaborations on data-driven projects, AI research, or cutting-edge ML applications
- 🛠️ Skilled in Python, SQL, Tableau, Power BI, Google Cloud Vertex AI, and more
- 📝 Published researcher in Machine Learning Applications in Cloud Forensics
- 🎯 Actively seeking opportunities in Data Science, ML Engineering, and AI Research
📂 Portfolio & Projects: GitHub - nehavaleti
📨 Contact: nvaleti1@asu.edu, valetineha9@gmail.com | LinkedIn
HackSoda’24 – 🥉 3rd Place (Online Safety Track)
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- Engineered a Chrome Extension leveraging GPT-4 Mini API to summarize legal documents, reducing reading time by 70%.
- Built a Flask backend for instant processing and real-time delivery of simplified terms & conditions.
- Enhanced accessibility of legal content for non-expert users through concise, high-accuracy NLP pipelines.
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- Achieved 90% prediction accuracy using Random Forest & Linear Regression to forecast taxi demand across NYC boroughs.
- Conducted EDA on millions of ride records to identify temporal & spatial demand patterns.
- Designed an interactive Tableau dashboard to optimize taxi allocation and reduce wait times.
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- Developed an ensemble model (SVM + Random Forest) on MobileNetV2 features to achieve 95%+ accuracy.
- Preprocessed large image datasets for model training and evaluation.
- Enabled early disease detection for improved crop yield and agricultural efficiency.
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- Built a real-time streaming pipeline ingesting 500K+ urban transport edges from NYC taxi trip data.
- Applied PageRank and BFS algorithms in Neo4j to detect high-impact traffic nodes.
- Maintained 95% system uptime using containerized microservices orchestrated with Kubernetes.
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- Designed a RAG system integrating GPT-4 for personalized book recommendations.
- Indexed 1,000+ books using FAISS & ChromaDB, leveraging emotion-driven embeddings for contextual relevance.
- Increased recommendation relevance by 65% via fine-tuned vector search and semantic filtering.
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- Built an interactive BI dashboard to analyze churn trends in the telecom sector using DAX-calculated KPIs.
- Highlighted at-risk customer segments by contract type, payment method, and tenure.
- Provided data-driven retention insights, improving stakeholder decision-making.
➡️ More projects on my GitHub
- Applications of Machine Learning in Cloud Forensics
IEEE Xplore | Read Here
- 🤖 Agentic AI – Architecting autonomous AI agents with planning, reasoning, and adaptive decision-making capabilities for real-world tasks.
- 📚 Retrieval-Augmented Generation (RAG) – Advancing context-aware LLM systems by integrating real-time knowledge retrieval and semantic search.
- 🖼 Computer Vision & Bioinformatics – Applying deep learning in Kaggle competitions to tackle medical imaging and bioinformatics challenges.
- ✨ Generative AI – Experimenting with LLM fine-tuning, prompt engineering, and multimodal AI to deliver domain-specific solutions.
- ⚙️ Scalable Data Pipelines – Building Kubernetes + Kafka + Neo4j pipelines for real-time graph analytics and complex query execution (CSE 511 Project).
- 🥉 3rd Place - HackSoda24 (Online Safety Track) for developing T&C Digest
- 🎤 Volunteer at ASU’s International Students and Scholars Center, supporting cultural & career events