Professor Batzoglou’s group is interested in algorithms and computational systems for genomics. They have been involved in several recent projects.
MLAGAN, the first large-scale multiple alignment system. Using MLAGAN, they can align and compare the entire DNA sequences of Human, Mouse, Rat, and other organisms, and discover elements that are evolutionarily constrained such as genes and gene-regulatory sites.
ProbCons, a multiple aligner of proteins based on a probabilistic model, and on a new technique that they call “probabilistic consistency” in alignment. Using ProbCons they can align hundreds of protein sequences with significant higher accuracy than was previously possible.
ICA-based clustering of genes using microarray data. By projecting gene expression vectors into independent components, they cluster genes into statistically interesting components that may represent independent biological processes in a cell. This group is currently involved in the ENCODE project, an NIH initiative to analyze 1% of the human genome with computational and experimental techniques, a pilot study that can be scaled to the complete genome at a later time.
The Bejerano lab focuses on harnessing Comparative Genomics of Human and related species to the fascinating challenge of understanding Human Embryonic Development.
Recent research has highlighted many thousands of regions in the human genome that have never been studied before. These regions appear to enact the exquisite resource allocation control required during embryonic development. Among them are Prof. Bejerano’s discovery of “ultraconserved elements”, arguably the most mysterious regions in the human genome.
The Bejerano Lab focuses on deciphering the syntax and grammar of this unique regulatory language; tracing its origins, evolution and affect on the human lineage; and understanding its contribution to human diseases, aiming to discover new approaches to diagnose, and possibly even cure and prevent them.
Our computational approaches rely heavily on machine learning, probabilistic and statistical reasoning, and projects range from the design of discovery-facilitating computational tools, to their extensive application in pursuit of novel biological insights.
Michael Genesereth is director of the Stanford Logic Group. He is most known for his work on Computational Logic and applications of that work in enterprise computing and electronic commerce.
Computational Logic is that branch of Computer Science concerned with the representation and processing of information in the form of logical statements. “If A is true and B is true, then either C is true or D is true” – things of that sort. Research topics carried out under Prof Genesereth’s supervision include formal languages, automated reasoning, and “deliberate systems” (computer systems capable of controlling their activity based on declarative specifications, changeable at runtime).
Professor Guibas heads the Geometric Computation group in the Computer Science Department of Stanford University. He is a member of the Computer Graphics and Robotics Laboratories. He works on algorithms for sensing, modeling, reasoning, rendering, and acting on the physical world. Professor Guibas’ interests span computational geometry, geometric modeling, computer graphics, computer vision, robotics, and discrete algorithms — all areas in which he has published and lectured extensively. Current activities focus on animation, collision detection, efficient rendering, motion planning, image data-bases, and physical simulations.
Specific projects include data structures for mobile data (kinetic data structures), ad-hoc sensor and communication networks, randomized geometric algorithms, rounding and approximating geometric structures, visibility-based motion planning, monte-Carlo algorithms for global illumination and motion planning, organizing and searching libraries of 3D shapes and images, physical simulations with deformable objects (molecules, fabric).
Leonidas Guibas obtained his Ph.D. from Stanford in 1976, under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, MIT, and DEC/SRC. He has been at Stanford since 1984 as Professor of Computer Science. He has produced several Ph.D. students who are well-known in computational geometry, such as John Hershberger, Jack Snoeyink, and Jorge Stolfi, or in computer graphics, such as David Salesin and Eric Veach. At Stanford he has developed new courses in algorithms and data structures, the mathematical foundations of computer graphics, and geometric algorithms. Professor Guibas was recently elected an ACM Fellow.
Professor Khatib pursues research on robotic control, haptic interface, mobile manipulation, and simulation. A new field of robotics is emerging. Robots are today moving towards applications beyond the structured environment of a manufacturing plant. They are making their way into the everyday world that people inhabit. The successful introduction of robotics into human environments will rely on the development of competent and practical systems that are dependable, safe, and easy to use. The discussion focuses on strategies and algorithms associated with the autonomous behaviors needed for robots to work, assist, and cooperate with humans.
In addition to the new capabilities they bring to the physical robot, these models and algorithms and more generally the body of developments in robotics is having a significant impact on the virtual world. Haptic interaction with an accurate dynamic simulation provides unique insights into the real-world behaviors of physical systems. The potential applications of this emerging technology include virtual prototyping, animation, surgery, robotics, cooperative design, and education among many others.
Haptics is one area where the computational requirement associated with the resolution in real-time of the dynamics and contact forces of the virtual environment is particularly challenging. The presentation describes various methodologies and algorithms that address the computational challenges associated with interactive simulations involving multiple contacts with complex human-like robotic structures.
Professor Koller has been a pioneer in the area of probabilistic inference and relational models. Her framework “Probabilistic Relational Models” is in widespread use around the works in applications as diverse as intelligent data analysis, robotic mapping, image understanding, and computational biology.
Professor Koller has been working on understanding genetic processes from a variety of genomic data sets, using techniques from machine learning and probabilistic models. In one recent project, they considered the problem of gene regulation. All of the cells in our body contain exact the same DNA, but the behavior of different cells can vary radically. The reason is that some genes are activated in some cells and dormant in others. Understanding the regulatory processes that cause genes to activate has important implications on comprehending how cells function. It also affects how diseases that involve breakdown in regulatory processes, such as cancer, can develop.
In their recent work, published in the highly prestigious journal Nature Genetics, Daphne and Eran Segal, together with several other collaborators (including Stanford alum Nir Friedman, now at Hebrew University), provided a high-throughput computational method for extracting regulatory circuits from large collections of gene expression measurements. The method identified modules of genes that are co-regulated and determined the regulatory genes that tell each module of genes to turn on or off – in other words, to start or stop making proteins. The proteins from each module, in turn, are responsible for a different cell process. The results of the analysis were shown to reproduce many regulatory relationships that were previously discovered. More interesting, in collaboration with Prof. David Botstein’s group (Stanford, Genetics Department), they also tested some of the method’s novel predictions in real wet-lab experiments. They “knocked out” a regulator under the conditions where it is predicted to be active. In the tested knock outs, three out of three turned out to regulate predicted genes. This showed that the method works, and allowed the characterization of three previously uncharacterized genes.
The goal of Professor Latombe’s research is to create autonomous agents that sense, plan, and act in real and/or virtual worlds. His work focuses on designing architectures and algorithms to represent, sense, plan, control, and render motions of physical objects.
The key underlying issue is to efficiently capture the connectivity of configuration or state spaces that are both high-dimensional and geometrically complex. Specific topics include: collision-free path planning among obstacles, optimal motion planning using dynamics equations, motion planning to achieve visual tasks, dealing with sensing and control uncertainty, assembly planning, construction of 3-D models of complex environments, visual tracking of articulated objects, relating shapes to functions, and reasoning in multiple-agent worlds. Applications include: robot-assisted medical surgery, integration of design and manufacturing, graphic animation of digital actors, study of molecular motions (folding, binding).
His current projects include the study of motion pathways of bio-molecules, the acquisition and exploitation of geometric models of 3D deformable objects, and the creation of multi-limbed rock-climbing robots.
Research in Professor Li’s lab focuses on two intimately connected branches of vision research: computer vision and human vision. In both fields, we are intrigued by visual functionalities that give rise to semantically meaningful interpretations of the visual world.
In computer vision, we aspire to build intelligent visual algorithms that perform important visual perception tasks such as object recognition, scene categorization, integrative scene understanding, human motion recognition, material recognition, etc.
In human vision, our curiosity leads us to study the underlying neural mechanisms that enable the human visual system to perform high level visual tasks with amazing speed and efficiency.
Questions: How do words evoke meaning? Can we model this process statistically? What algorithms can learn such models from large amounts of incomplete data? How can we use these methods to build better natural language interfaces?
Professor Liang’s interests include machine learning, natural language processing, natural language semantics, weakly-supervised learning, statistical learning theory, and program induction.
Chris Manning works on systems and formalisms that can intelligently process and produce human languages. His research concentrates on probabilistic models of language and statistical natural language processing, information extraction, text understanding and text mining, constraint-based theories of grammar (HPSG and LFG) and probabilistic extensions of them, syntactic typology, computational lexicography (involving work in XML, XSL, and information visualization), and other topics in computational linguistics and machine learning.
Professor Ng’s research focuses on machine learning for data mining, pattern recognition and control. His work addresses the fundamental mathematical properties of learning as well as their practical application.
Using machine learning, he hopes to build the best, open-source spam filter in the world. He also applies machine learning to problems in control such as autonomous helicopter (and fixed-wing aircraft) flight, and legged robot walking. These are problems that were either intractable to human engineering efforts, or that took thousands of person-hours to find solutions. His learning methods are typically able to design better-than-human controllers in minutes.
Using machine learning, his autonomous helicopter also recently became the first to be capable of sustained inverted (upside-down) flight.
Professor Salisbury’s research is in the area of robotics and haptics with particular emphasis on enabling enhanced human-machine interaction. His appointment in the departments of computer science and surgery reflects his interest in medical applications.
His NIH-sponsored research team is working to create a collaborative, simulation-based surgical training environment, utilizing networked multi-hand haptic and visual simulation to support surgical skill and team training. He is also developing of mechanical and control systems for human-friendly robots – devices that will work in cooperation (and contact) with humans. This work addresses teleoperative and autonomous tasks as well as affective aspects of human-machine interactions. His work on human interface technologies focuses on the development of new haptic interface devices to enable multi-hand, multi-finger interaction. This is part of his visio-haptic workstation project.
Some of Professor Salisbury’s previous activities, which have resulted in significant technology transfer, include his involvement in creating advanced technologies exemplified by SensAble Technology’s PHANTOM haptic interface, Intuitive Surgical’s daVinci Surgical System, and Barrett Technology’s WAM Arm.
Professor Savarese directs the Computational Vision and Geometry Lab (CVGL) at Stanford. Our research addresses the theoretical foundations and practical applications of computational vision. Our interest lies in discovering and proposing the fundamental principles, algorithms and implementations for solving high level visual recognition and reconstruction problems such as object and scene understanding as well as human behavior recognition in the complex 3D world. My group’s research is sponsored by US government agencies such as NSF and Navy as well as industrial partners such as Ford, Toyota, Google, TRW and KLA-Tencor.
Professor Shoham’s artificial intelligence work includes formalizing common-sense (including notions such as time, causation, and mental state), and multi-agent systems (including agent-oriented programming and coordination mechanisms).
His current interests concern problems at the interface of computer science and game theory, including foundational theories of rationality, online auctions, and electronic commerce.
Professor Thrun seeks to understand information processing and decision making in robotics and decentralized systems. Thrun is best known for his contributions to probabilistic robotics, which applies methods from statistics and decision theory to robotics problems. Many of Thrun’s algorithms define the state of the art in robotics perception and control.
Thrun has built a number of pioneering robot systems. In 1997, the world’s first robotic museum tour guide for the German Museum in Bonn, a year later, a similar robot for the Smithsonian Museum. In 1999, he developed an autonomous robot for picking up balls from a tennis court. In 2000, he developed a series of robotic assistants for the elderly, which provide a range of services, such reminding people to take their medication, escorting them to the doctor, or being a telepresence interface to deliver off-site health care services. In 2002, a robot for mapping abandoned coal mines.
In 2003, Thrun developed one of the first ground mapping helicopters, showing how flying robots can assist ground vehicles when exploring urban terrain. All these innovations are based on the new paradigm of probabilistic robotics, and the basic science of statistical estimation in robotics.