Designing In-situ Interaction with Ubiquitous Robots

Lawrence Kim is a PhD candidate in Mechanical Engineering at Stanford University where he is advised by Sean Follmer. His research lies at the intersection of human-computer interaction, robotics, and haptics with a focus on studying the interaction with multi-robot systems. He has received best paper and best paper honorable mention awards at CHI and UIST, and a Fast Company's honorable mention award in Innovation by Design. He is also a recipient of a Samsung Scholarship.

Research at the Service of Free Knowledge

Leila Zia is a Principal Research Scientist and the Head of the Research team at the Wikimedia Foundation, the foundation that operates Wikipedia and its sister projects. Her research interests include quantifying and addressing the gaps of knowledge in Wikipedia and Wikidata, understanding Wikipedia's readers, and studying the contributor diversity on Wikimedia projects. She received her PhD from Stanford University in Management Science and Engineering.

Intelligent Agents, the Knowledge Graph and Open Data for Learning

Mark Musen is Professor of Biomedical Informatics at Stanford University, where he is Director of the Stanford Center for Biomedical Informatics Research. Dr. Musen conducts research related to intelligent systems, reusable ontologies, metadata for publication of scientific data sets, and biomedical decision support. His group developed Protégé, the world’s most widely used technology for building […]

Algorithms for Assessments of Problem-Solving Tasks

Chris Piech is an Assistant Professor of Computer Science Education at Stanford University. His research is in machine learning looking to understand human learning. He also believes that in 2019 there is a unique opportunity to build better learning experiences that serve more students. Chris is teaching CS398 which is a research-level course that explores […]

Sensor-Based Assessments

Nick Haber is an Assistant Professor at The Stanford Graduate School of Education. Nick is interested in machine learning, computer vision, and human-computer interaction. His work thus far has primarily involved face detection and tracking, using Constrained Local Models. On top of this, he developed engagement scoring, gaze tracking, and emotion detection for the purpose […]

Measuring What Matters

Daniel Schwartz is dean of Stanford Graduate School of Education and an expert in human learning and educational technology. Schwartz oversees a laboratory whose computer-focused developments in science and math instruction permit original research into fundamental questions of learning. He has taught math in rural Kenya, English in south-central Los Angeles, and multiple subjects in […]