Anisha Shende

Anisha Shende

she/her

Undergrad at VIIT

Hello!

I’m Anisha Shende. 👋

I am a passionate developer with a keen interest in creating beautiful and functional applications. I love to explore new technologies and implement creative solutions to solve real-world problems.

Career Highlights

  • Published Research Papers in reputed journals, including a publication on “Optimal Joint Angles for Prosthetic Knee Design Using Gait Analysis” in an IGI Global publication.
  • Finalist in the Dark Patterns Busters Hackathon 2023, organized by the Ministry of Consumer Affairs and IIT-BHU.
  • Designed and deployed a cross-platform AI assistant using Flutter, integrated with state-of-the-art LLMs like Gemma2, Llama3, and Mixtral8 for enhanced capabilities.
  • Filed a patent for a hybrid wearable device for real-time gait analysis and prosthetic knee optimization.
  • Organized workshops as the Flutter Lead for Google Developers Group and actively contributed to open-source projects, ranking among the top contributors in Winter of Code 3.0

Research Interests

My research primarily revolves around Artificial Intelligence (AI), with a particular emphasis on Generative AI and Large Language Models (LLMs).

  • AI-Powered Research Engine for Commercial Courts
    • Designed for the Smart India Hackathon 2024 to expedite dispute resolution in commercial courts.
    • Integrated predictive analytics and multilingual support for customized legal research.
  • Prediction of Optimal Joint Angles for Prosthetic Knee Design
    • Conducted research under the guidance of Dr. Shehal Rathi.
    • Evaluated machine learning models like RNN, Bidirectional LSTM, and Random Forest, achieving 95% accuracy in predicting joint angles.
    • Research featured in the IGI Global publication, “Modern Digital Approaches to Care Technologies for Individuals with Disabilities.”
  • Cross-Platform AI Assistant
    • Developed a Flutter-based assistant compatible with desktop, Android, and web platforms.
    • Integrated Gemma2, Llama3, and Mixtral8 LLMs with Groq API for faster inference.
    • Utilized ChromaDB for embeddings and implemented a FastAPI backend for efficient RAG-based data retrieval.

Connect with Me

Looking for more? Visit my Portfolio Website! 🌟