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Riffbot.ai is a web-based platform that leverages AI-powered, customizable chatbots to generate dynamic, personalized self-reflection experiences for learners while providing real-time insights to enhance engagement and improve learning outcomes at scale.

Stanford researchers have developed a novel representation learning model that improves data-driven learning by incorporating more relevant data relationships. This approach significantly enhances both model performance and inference accuracy.

Stanford researchers have developed an innovative AI-driven solution that leverages the BERT-based AI model to automatically classify patient-provider messages into 12 distinct categories, reducing clinician workload and enhancing workflow efficiency in healthcare settings.

The Satellite Hardware-In-the-loop Rendezvous Trajectory (SHIRT) dataset consists of images and pose labels associated with two rendezvous trajectory scenarios (ROE1 and ROE2) in Low Earth Orbit (LEO) created from two different sources.

SPEED+ is an advanced dataset for vision-based spacecraft pose estimation with specific emphasis on evaluating the robustness of Machine Learning (ML) models across the domain gap.

Researchers in the Murmann Mixed Signal Group have developed a pipelined chip architecture with inverted residual and linear bottlenecks-based networks for energy efficient Machine Learning inference on edge devices.

Researchers at Stanford have developed a machine learning method for identifying anomalies in complex data streams.

Researchers at Stanford have developed an imitation learning method, IQ-Learn, shown to surpass existing methods in some applications.

Near-infrared (NIR) imaging is a valuable research tool that produces quality images with high spatial and temporal resolution through millimeter tissue depths.

Researchers in Roger Kornberg's lab have developed a deep convolutional neural network algorithm that predicts the location and strength of transcription factor activation domains (ADs) in eukaryotes.

This invention is a machine learning algorithm for determining coronary artery calcification (CAC) scoring from routine CT scans.

Stanford researchers have an efficient method to measure gradients simultaneously and in parallel, as related to an optical network.

Scientists at Stanford have developed a machine learning program with broad potential for diagnostic applications which analyzes mass spectrometry data profiling metabolites in a patient sample ("metabolomics" data) and predicts infection status.

Using a novel convolutional neural network architecture, PlexusNet can be used for histologic image analysis with smaller parameter and training sets than current state-of-the-art models.

Stanford researchers at the Liao and Xing Labs have developed and tested a machine learning algorithm for augmented detection of bladder cancer. Machine learning has the potential to enhance medical decision making in cancer detection and image analysis.