Stanford Translational AI Lab (STAI)
Detecting neuropsychiatric symptoms (NPS) such as anxiety, agitation, or depression is essential because it enables the early identification and more tailored treatment of mental health conditions. Currently, NPS is detected using patient- or caregiv…
Our research project focuses on leveraging ambient intelligence and computer vision within intensive care unit (ICU) environments to support clinical documentation and patient monitoring.
Our senior care project focuses on developingadvanced AI models to improve the quality of life and safety for older adults. By analyzing data from ambient sensors, our AI models can track seniors’ daily behaviors and detect any unusual changes or imm…
Our project on human rendering from occluded videos addresses the challenge of accurately reconstructing 3D human bodies from single-view, in-the-wild video data, where unexpected occlusions often occur.
We are working on a series of works around Video Understanding & Motion Analysis, focusing on developing models to understand human motion, activities, and behaviors in real-world environments from videos. This work includes building systems for vide…
We explore video language models for understanding human activities, focusing on detailed and fine-grained actions, including the activities of daily living (ADLs) in home environments. We developed InteractADL, a multi-view video dataset with scene …
Despite the remarkable advancements in neuroscience and related fields, we still cannot fully explain the complexities of the human brain. Foundation models hold promise, but so far, these applications have focused only on single modalities of data, …
Neural decoding is the process of understanding how brain activity corresponds to different stimuli. It is often considered one of the primary objectives in cognitive sciences.
We aim to employ recent advances in computer vision and artificial intelligence to assist in differentiating dementia phenotypes with a solid understanding of the neural underpinnings of gait and mobility disruptions related to dementia. Our analysis…
Motor symptoms, such as changes in gait and functional mobility, are among the earliest detectable signs of various neurodegenerative diseases, including Parkinson's and Alzheimer's. However, our understanding of the underlying causes—specifically, w…
The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning for medical imaging studies. Confounders influence the relationship between input data (e.g., brain MRIs) and output variables (e.g., diagno…
Nearly 60 million people in the United States struggle with mental illness each year. Yet, healthcare providers identify depression in only 47% and detect clinical deterioration in only 21% of cases.
This project focuses on developing and creating rigorous evaluation frameworks for multimodal foundation and large language models to advance mental health research and clinical applications. Leveraging advanced AI techniques, we integrate diverse da…
Computerized cognitive training (CCT) is a scalable, well-tolerated intervention that has promise for slowing cognitive decline. The effectiveness of CCT is often affected by a lack of effective engagement. Factors like Mental fatigue and reaction ti…