Google DeepMind

Mentors: Student: Aryan Saboo

EchoGem introduces a novel batching engine designed to answer multiple questions about the same source parallelly to reduce response times heavily. It does so by building a modular smart batching engine for Gemini that teaches the model to think in context-aware batches. What sets EchoGem apart is its focus on modularity and testing—each component is designed to be independently swappable and improvable allowing for open testing and matching different strategies for different parts of the engine. Instead of a naive sliding window or top-k search approach, EchoGem uses semantic clustering of both chunks and questions to form intelligent batch groups. It is also committed to measurable, reproducible gains. Every design decision—whether it's a new chunking strategy or a different context ranking model—can be rigorously backtested using a structured evaluation suite.

Mentors: Student: Chen-Hao Wu

Gemma is a lightweight, open-source large language model by Google DeepMind. This project aims to build an intuitive web interface for fine-tuning Gemma models. The interface will allow users to upload datasets, configure hyperparameters, monitor training progress, and export trained models — all without writing a single line of code. By lowering the entry barrier, the UI will empower a broader range of users to experiment with and adapt large language models to their specific tasks.

Mentors: Student: Adel Muursepp

The project will close the documentation and evaluation gap for Google’s Gemini models by contributing structured onboarding guides and benchmarking templates to Promptfoo and Weights & Biases Weave, with specifically introducing models like Gemini 2.5 Pro, Gemini 2.0 Flash and Gemini 2.0 Flash-Lite. Despite Gemini’s powerful features—like multimodality and advanced safety settings—its presence in open-source evaluation tools lags behind GPT or Llama models. By improving usability, comparability, and safety transparency, this work will help developers, researchers, and product teams integrate and assess Gemini effectively, making it a fully accessible option in the LLM ecosystem.

Mentors: Student: Jevon Mao

This project proposes the creation of a comprehensive Postman Workspace tailored for Google’s Gemini API suite. It will offer developers a robust, user-friendly hub to explore, integrate, and test Gemini’s capabilities, significantly lowering the barrier to entry for new Gemini developers.

Mentors: Student: Rodrigo Sagastegui

This project explores how lightweight Gemma models can be applied in life-critical situations through an open-source Crisis Response Toolkit. The main deliverable, One Minute Agent, is an offline AI assistant designed to provide first-aid guidance during emergencies, bridging the gap before responders arrive and helping improve survival rates. The work also investigates offline deployment, function-calling abilities, and agent workflows to demonstrate how Gemma can support real-time decision-making in crisis scenarios.