Research Tools: code, datasets, & models

ReMe is a web-based framework that helps researchers create AI chatbots for personalized training and interventions aimed at strengthening memory and cognitive functions. Early evaluations show its potential to contribute to digital health innovation and…

EvoDiff is a general-purpose diffusion framework that combines evolutionary-scale data with the distinct conditioning capabilities of diffusion models for controllable protein generation in sequence space. EvoDiff generates high-fidelity, diverse, and structurally-plausible proteins that cover natural…

PEACE enhances multimodal large language models (MLLMs) with geologic expertise, enabling accurate interpretation of complex, high-resolution maps. By integrating structured extraction, domain knowledge, and reasoning, it supports critical tasks in disaster risk, resource discovery, and…

The MCP Server for Azure AI Foundry Labs is designed to supercharge team velocity in adopting and evaluating breakthrough AI research. By equipping GitHub Copilot with custom tools for intelligent model discovery, tailored implementation guidance,…

With Project Amelie, we are unveiling our first Foundry autonomous agent that can perform machine learning engineering tasks. ML teams can use the agent to initiate complex machine learning tasks using prompts —such as, “Help…

Precio is a Rust library that implements the Precio protocol for computing private layered histograms and sums.

An evaluation of AI-driven microscopy image analysis suggests reliability issues in single cell analysis. This repository contains code used in our paper, “Representation Learning Methods for Single-Cell Microscopy are Confounded by Background Cells,” to evaluate…

In this work, we investigate automatic design composition from multimodal graphic elements. Although recent studies have developed various generative models for graphic design, they usually face the following limitations: they only focus on certain subtasks…

µProtein is an open-source framework for protein sequence optimization, combining a protein fitness prediction model with reinforcement learning to efficiently explore the mutational landscape. It demonstrates strong generalization across diverse proteins and has been experimentally…

Protein language models (PLMs) pretrained via a masked language modeling objective have proven effective across a range of structure-related tasks, including high-resolution structure prediction. However, it remains unclear to what extent these models factorize protein…