Stanford University
Showing 5,261-5,280 of 5,802 Results
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Karen D. Wang
Affiliate, Physics
BioMy research is situated at the intersection of machine learning and human cognition. In my work, I apply learning analytics and data mining techniques to students’ interaction data in technology-based learning environments. The goal is to translate fine-grained behavioral data into meaningful evidence about students’ cognitive and metacognitive processes. These enhanced understandings of students’ mental processes and competencies are then used to guide the design of and evaluate instructional materials embedded in educational technology.
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Ge Wang
Associate Professor of Music and Senior Fellow at the Stanford Institute for Human-Centered AI
BioGe Wang is an Associate Professor at Stanford University in the Center for Computer Research in Music and Acoustics (CCRMA). He specializes in the art of design and computer music — researching programming languages and interactive software design for music, interaction design, mobile music, laptop orchestras, expressive design of virtual reality, aesthetics of music technology design, and education at the intersection of computer science and music. Ge is the author of the ChucK music programming language, the founding director of the Stanford Laptop Orchestra (SLOrk). Ge is also the Co-founder of Smule (reaching over 200 million users), and the designer of the iPhone's Ocarina and Magic Piano. Ge is a 2016 Guggenheim Fellow, and the author of ARTFUL DESIGN: TECHNOLOGY IN SEARCH OF THE SUBLIME—a book on design and technology, art and life‚ published by Stanford University Press in 2018 (see https://artful.design/)
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Li Wang
Assistant Professor of Biology
Current Research and Scholarly InterestsWe study how the extraordinary diversity of cells and synapses in the brain is generated, organized, and maintained, and how these processes are disrupted in diseases such as neurodevelopmental disorders and brain cancer. By combining single-cell and spatial genomics, lineage tracing, perturbation screens, synaptic proteomics, and machine learning models, we aim to uncover the molecular rules that define neural identity and connectivity.
Our research spans two interrelated themes, each grounded in human biology and driven by cutting-edge technologies. By comparing these processes across species, we aim to uncover both conserved mechanisms and human-specific innovations that define the unique features of the human brain.
