Imaging methods that can visualize biological samples with high and temporal resolution are critical for modern biomedical research and clinical practice.
Stanford researchers have developed a personalized arrhythmia risk prediction tool for dilated cardiomyopathy (DCM) patients using patient-derived induced pluripotent stem cells (iPSCs) to replicate heart biology and accurately predict arrhythmia risk, enabling timely interven
Stanford researchers have developed an innovative underwater sensing system inspired by the whiskers of aquatic mammals, enabling robots to detect and track contact with high precision in low-visibility conditions.
Stanford researchers have developed a hierarchical event readout method that solves a major latency problem in event-based image sensors. Event-based sensors output data only when pixels change, enabling fast and efficient sensing.
Researchers at Stanford have developed a general software framework that reconstructs high-resolution spatial fields from sparse, irregular, or noisy measurements.
Non-small-cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases, making it the leading cause of cancer-related deaths globally. Post-surgical recurrence and treatment resistance are the main causes of cancer-related mortality.
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 software platform featuring an integrated digital twin framework to enable 24/7, carbon-free operations of electric vehicle (EV) fleets.
Stanford researchers have developed an approach to enhance Phlego cement production by leveraging the Streckeisen (QAPF) diagram, a powerful tool for classifying igneous rocks based on their mineralogical composition.
Stanford researchers in the WE3 and S3 Labs developed software for biogas modeling suitable for real-time, co-digestion forecasting control for waste streams with widely varying biodegradability rates.
Stanford researchers have developed a networked audio system that enhances the experience of teleconferencing, and online performances, gaming, and gatherings.
Stanford researchers have patented a real-time auralization-reverberation system (CAVIAR - Chamber for Augmented Virtual and Interactive Audio Realities) for providing immersive and interactive audio environments.
Code In Place is an innovative program from Stanford University that provides free, high-quality introductory courses in Python programming, utilizing volunteer tutors to reach a global audience.
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.