In the past decade, an abundance of data has become available, such as online data on the Web, scientific data such as the transcript of the human genome, sensor data acquired by robots or by the buildings we inhabit. Turning data into information pertaining to problems that people care about, is the central mission of AI research at Stanford.
Members of the Stanford AI Lab have contributed to fields as diverse as bio-informatics, cognition, computational geometry, computer vision, decision theory, distributed systems, game theory, image processing, information retrieval, knowledge systems, logic, machine learning, multi-agent systems, natural language, neural networks, planning, probabilistic inference, sensor networks, and robotics.
Research Focus:
computational geometry, image processing, graphics, computer vision, sensor networks, robotics, discrete algorithms
Title:
Associate Professor of Computer Science
Research Focus:
computational logic, semantic web, computational law, enterprise management, general game playing
Title:
Kumagai Professor of Computer Science, Emeritus
Title:
Assistant Professor of Developmental Biology & Computer Science
Title:
Associate Professor of Computer Science
Title:
Professor of Bioengineering, Genetics, and Medicine (and by courtesy, Computer Science)
Title:
Associate Professor of Computer Science
Research Focus:
machine learning, reinforcement learning/control, broad-competence AI
An artist might spend weeks fretting over questions of depth, scale and perspective in a landscape painting, but once it is done, what's left is a two-dimensional image with a fixed point of view. But the Make3d algorithm, developed by Stanford computer scientists, can take any two-dimensional image and create a three-dimensional "fly around" model of its content, giving viewers access to the scene's depth and a range of points of view.
Given an image, we propose a hierarchical generative model that classifies the overall scene, recognizes and segments each object component, as well as annotates the image with a list of tags. To our knowledge, this is the first model that performs all three tasks in one coherent framework. For instance, a scene of a ‘polo game’ consists of several visual objects such as ‘human’, ‘horse’, ‘grass’, etc. In addition, it can be further annotated with a list of more abstract (e.g.
