meta_hackathon_winners

LabLens AI — Evidence-Grounded Scientific Experiment Analysis Assistant The Problem Experimental research often involves repetitive data cleaning, statistical calculations, graph generation, and interpretation. These tasks can be time-consuming, particularly for students and researchers who may not have extensive programming or statistical experience. General-purpose AI assistants can explain scientific concepts, but they may also produce unsupported numerical claims, invent experimental conditions, or describe results that are not present in the dataset. In scientific work, an explanation must be traceable to actual measurements and calculations. Our Solution LabLens AI provides a structured workflow for scientific experiment analysis. Users provide experimental data in CSV format. The application validates the data, identifies invalid observations, performs statistical calculations, fits mathematical models, generates plots, and prepares a structured analysis report. Meta's Muse Glimmer model is used to explain the computed results in accessible scientific language. The model receives calculated results rather than being asked to invent or independently calculate experimental measurements. Core Features - CSV-based experimental data input. - Numeric data validation and cleaning. - Identification and reporting of invalid observations. - Linear regression and statistical analysis. - Calculation of slope, intercept, R², RMSE, and correlation statistics where applicable. - Diagnostic plots, including fitted data and residual visualizations. - AI-generated interpretation based on computed results. - Evidence-conscious reporting that distinguishes numerical outputs from model explanations. - Outlier screening designed to flag observations for review rather than automatically deleting them. Technology Stack - Python - Streamlit - NumPy and Pandas - SciPy / statistical analysis utilities where applicable - Matplotlib - Meta Muse Glimmer through NVIDIA NIM - OpenAI-compatible model API interface Responsible AI Design LabLens AI is designed around a simple principle: **the calculations should be reproducible, and the AI explanation should remain grounded in those calculations.** The Python analysis pipeline performs the numerical work. The AI model explains the results and is instructed not to invent experimental conditions, instruments, uncertainties, or references. LabLens is intended as a research-support tool, not a replacement for experimental validation or scientific judgment.