I'm an Electrical and Computer Engineering Honors and Mathematics student at The University of Texas at Austin, with a Finance minor. I'm interested in building AI, machine learning, and software systems that turn complex data into useful, real-world decisions.
Languages: Python, Java, C++, C, SQL, JavaScript, TypeScript
AI/ML: PyTorch, TensorFlow, scikit-learn, Transformers, Hugging Face, NLP, RAG, Computer Vision, OpenCV
Data & Cloud: Databricks, Apache Spark, Delta Lake, AWS
Software & Tools: Git, GitHub, Docker, Linux, Bash, Jupyter, Streamlit, Flask, RESTful APIs
Hardware & Systems: Microcontrollers, FPGA, Verilog, LTspice, ADCs/DACs, UART, GPIO
AI-powered campus assistant for UT Austin that combines a modern Next.js frontend, FastAPI backend, and LLM integration to help students navigate courses, campus resources, organizations, events, and student life.
Tech: Next.js, TypeScript, Tailwind CSS, FastAPI, Python, Gemini API, ChromaDB, PostgreSQL, Docker
Impact: Demonstrates full-stack AI application development, modern frontend engineering, backend API design, cloud deployment, and a scalable foundation for Retrieval-Augmented Generation (RAG).
AI security analyst copilot that helps investigate simulated phishing and security events using retrieval-augmented generation, threat context, and structured incident reporting.
Tech: Python, RAG, LLMs, Vector Search, MITRE ATT&CK, Streamlit
Impact: Demonstrates how AI can support faster security triage, citation-grounded analysis, and clearer remediation recommendations.
Generative AI proof-of-concept that analyzes clinical documents, extracts key medical information, identifies documentation issues, and presents results through an interactive dashboard.
Tech: Python, Streamlit, Google Gemini API, PyPDF
Impact: Demonstrates how AI can reduce manual review time and improve visibility into clinical documentation quality.
Transformer-based NLP system for identifying financial entities such as organizations, people, and locations from financial text.
Tech: Python, PyTorch, Hugging Face, Transformers, FiNER-ORD
Impact: Built an applied NLP pipeline for financial text analysis with model evaluation and error analysis.
AI-powered smart cooking assistant that uses computer vision and retrieval-based recommendations to suggest recipes from available ingredients.
Tech: Python, Computer Vision, Embeddings, RAG
Impact: Shows practical use of multimodal AI for personalized consumer applications.
- Built AI and software projects across healthcare, finance, education, cybersecurity, embedded systems, and consumer applications.
- Developed experience with machine learning, NLP, RAG, computer vision, full-stack web development, data pipelines, and dashboarding.
- Applied technical work to real-world problems involving clinical documentation, financial text, campus navigation, agriculture, and automation.
- Strengthened full project lifecycle skills, including research, development, testing, documentation, presentation, deployment, and full-stack engineering.
- Applied AI and machine learning
- LLM-powered software systems
- Retrieval-Augmented Generation
- AI agents and automation
- Data engineering and analytics
- Software engineering for business decision-making
- LinkedIn: www.linkedin.com/in/shrihan-anikapati
- GitHub: https://github.com/shri30a
- Email: [email protected]