Research Areas : Artificial Intelligence

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.

Faculty

Research Focus

Russ Altman	application of computing technologies to basic molecular biological problems, now referred to as bioinformatics
Serafim Batzoglou	computational genomics
Gill Bejerano	computational genomics
Paulo Blikstein	cognitive computational modeling, agent-based modeling, complexity sciences; constructionist, project-based, learning environments
Kwabena Boahen	building a simulation platform that will enable the cortex's inner workings to be modeled in detail
Ron Dror	computational biology
Stefano Ermon	probabilistic reasoning and inference, machine learning, computational sustainability, sequential decision making, and control theory
Ed Feigenbaum	knowledge-based systems
Richard Fikes	knowledge representation
Mike Genesereth	computational logic, semantic web, computational law, enterprise management, general game playing
Noah Goodman	computational models of cognition, integrating logic and probability; probabilistic programming languages; natural language semantics and pragmatics
Leonidas Guibas	computational geometry, image processing, graphics, computer vision, sensor networks, robotics, discrete algorithms
Daniel Jurafsky	computational linguistics, speech recognition, natural language
Oussama Khatib	robotics, haptics, motion planning
Mykel J. Kochenderfer	N/A
Anshul Kundaje	development of statistical and machine learning methods for integrative analysis of diverse functional genomic and genetic data to learn models of gene regulation
Jean-Claude Latombe	robot-assisted surgery, integration of design and manufacturing, digital actors, molecular motions
Jure Leskovec	mining and modeling large social and information networks
Fei-Fei Li	computer vision
Percy Liang	modeling both natural and programming languages, and exploring the semantic and pragmatic connections between the two; analyzing learning algorithms, both theoretically and empirically
Christopher Manning	natural language processing, information retrieval,
John McCarthy	formal reasoning
Clifford I. Nass (1958-2013)	language, speech
Andrew Ng	machine learning, reinforcement learning/control, broad-competence AI
Nils Nilsson	robotics
Roy Pea	how innovations in computing and communications technologies and affiliated socio-cultural practices can influence learning, thinking, and educational systems
Kenneth Salisbury	robotics, haptics, computer-aided surgery
Silvio Savarese	theoretical foundations and practical applications of computational vision
Yoav Shoham	logic, multi-agent systems, game theory, electronic commerce
Sebastian Thrun	robotics, machine learning, probabilistic methods
Benjamin Van Roy	reinforcement learning