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Li jing lei1/4/2024 ![]() ![]() In addition to her technical contributions, she is a national leading voice for advocating diversity in STEM and AI. ![]() Li is the inventor of ImageNet and the ImageNet Challenge, a critical large-scale dataset and benchmarking effort that has contributed to the latest developments in deep learning and AI. Li has published more than 300 scientific articles in top-tier journals and conferences in science, engineering and computer science. In the past she has also worked on cognitive and computational neuroscience. Fei-Fei Li’s current research interests include cognitively inspired AI, machine learning, deep learning, computer vision, robotic learning, and AI+healthcare especially ambient intelligent systems for healthcare delivery. She also holds a Doctorate Degree (Honorary) from Harvey Mudd College. ![]() degree in physics from Princeton in 1999 with High Honors, and her PhD degree in electrical engineering from California Institute of Technology (Caltech) in 2005. Since then she has served as a Board member or advisor in various public or private companies. Li was Vice President at Google and served as Chief Scientist of AI/ML at Google Cloud. And during her sabbatical from Stanford from January 2017 to September 2018, Dr. She served as the Director of Stanford’s AI Lab from 2013 to 2018. Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford’s Human-Centered AI Institute. Vice Provost for Undergraduate Educationĭr.Office of Vice President for Business Affairs and Chief Financial Officer.Office of VP for University Human Resources.Stanford Woods Institute for the Environment.Stanford Institute for Economic Policy Research (SIEPR).Institute for Stem Cell Biology and Regenerative Medicine.Institute for Human-Centered Artificial Intelligence (HAI).Institute for Computational and Mathematical Engineering (ICME).Freeman Spogli Institute for International Studies.Stanford Doerr School of Sustainability.Combined with a smartphone capable of color analysis, POCT of VSCs can be achieved, providing an approach for the monitoring of halitosis and screening of periodontitis. By integrating the hydrogels into a sensor array, the oral health conditions of patients with halitosis can be evaluated and distinguished, offering risk assessment of periodontitis. Furthermore, visual and in situ monitoring of Porphyromonas gingivalis responsible for periodontitis can be realized. A linear detection range of 0–1 ppm with a detection limit of 61 ppb can be achieved, covering the typical VSC concentration in the breath of patients with periodontitis. VSCs can reduce disulfide bonds within the network, leading to expansion of the hydrogel and thus change of the structural color. Here, a structural color hydrogel for naked-eye detection of exhaled VSCs is presented. However, current detection methods often require bulky and costly instruments, as well as professional training, making them impractical for widespread detection. ![]() High-sensitivity detection of exhaled VSCs is urgently desired for promoting the point-of-care testing (POCT) of halitosis and screening of periodontitis. Oral pathogens can produce volatile sulfur compounds (VSCs), which is the main reason for halitosis and indicates the risk of periodontitis. ![]()
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