My research interests are in the area of computer graphics, particularly in data-driven materials, virtual humans, and computational photography and display.

Smart Visualization: One picture is worth a thousand words. For thousands of years, people have been using information graphics—visual representation of data—to comprehend and analyze information. However, creating high-quality visualization is a daunting task especially for ordinary people who are neither graphic artists nor computer scientists. To democratize the use of visualization, I am interested in automated visualization design and tailoring the visual responses to highly dynamic user interaction situations and unanticipated information. More recently, I have been working in the area of interactive visual text analytics, which combines state-of-the-art text analytics with novel interactive visualization to empower average business users to analyze massive amounts of textual data and facilitate their decision making (e.g., making a purchase decision based on the visual text analysis of extensive consumer reviews).

Mixed-initiative Human-Computer Interaction (HCI): I believe that the future of HCI is to facilitate the development of a man-computer symbiosis where both humans and machines can leverage their strengths and avoid their weaknesses. I am especially interested in the development of mixed-initiative intelligent information systems, where the humans and computers work together collaboratively to facilitate information seeking an analysis. To enable computers to take sensible initiatives and push this class of systems to main stream applications, I am particularly interested in developing novel and practical computational approaches to the problem. Moreover, I am interested in exploring new interaction paradigms where users can interact with complex system responses (e.g., system-derived text summarization results) and the use of interactive machine learning in support of an adaptive, mixed-initiative human-computer interaction, where both humans and computers can learn from each other.

Opportunistic Social Computing: The use of social software (e.g., social networking, micro-blogging, and online forums) has penetrated the masses. I am very much interested in finding out how such phenomena will change our daily lives as well as its long-term impact on our world. In particular, I am especially interested in how social computing will bring us opportunistic information and collaboration partners whenever we need them but without subjecting ourselves to “constant availability and instant intimacy” as we do today. To achieve this goal, I believe there are fundamental research issues to be addressed.They include but not limited to:

•Understanding, modeling, and automatically deriving social profile of a person, a community, or an organization based on their digital footprints (i.e., online behavior);
•Use of the derived social profiles to objectively reveal key characters of individuals and organizations, assess community/organization dynamics, value, and risks, predict the development or growth of individuals, communities and organizations, and help establish opportunistic collaborations among individuals and organizations;
•Monitoring social channels (e.g., facebook and twitter) and detecting most valuable channel(s) for extracting social intelligence (e.g., knowledge about car repair or the consumer complains/needs); Mining of social messages to distill insight (information or people) for opportunistic information sharing (e.g., sharing the extracted consumer complains), knowledge acquisition (e.g., asking target audience to voice their problems and suggest their solutions), and crowd-sourced problem solving (e.g., social Q&A). See my webpage for more info.

The popularity of web-based mapping services such as Google Earth/Maps
and Microsoft Virtual Earth (Bing), as well as the increasing use of
smartphones, has led to an increasing awareness of the importance of
location data and its incorporation into both web-based search
applications and the databases that support them.

Location data is a subset of the broader area of spatial and
multidimensional data, and my interests lie in developing efficient
representations and algorithms for its use in applications in the
computer graphics, databases, computer vision, geographic information
systems (GIS), image processing, high dimensional data, and search
domains. My work is based on the observation that these
representations invariably reduce to finding ways to sort the data
with the caveat that sorting is a linear process, having a reference
point, which yields an explicit order. The drawback here is that in 2
and higher dimensions, the data must be resorted when the reference
point is changed. This is not the case with methods that use implicit
sorts such as those that sort objects with respect to the space that
they occupy (e.g., quadtrees, octrees, R-trees, etc.). I characterize
the problem as one of “sorting in space”. I am interested in
developing algorithms for problems in the above domains that are based
on the notion of sorting. Some examples include finding nearest
neighbors and shortest paths in spatial networks where the distance is
along the edges of the network rather than as “the crow flies”,
as well as finding similar trajectories.

Although location data is usually specified geometrically, many of
today’s web-based applications increasingly find it being specified
textually as it corresponds to place names. I am interested in
techniques for identifying such data as well as resolving it to the
right geometric location as these specifications are often ambiguous.
I have a number of projects that try to make use of such data for
searching databases such as those in the deep web as well as
spreadsheets, news, tweets, to name a few, and in mobile applications.

The research in the 3D Interaction Group spans 3D user interfaces, interaction techniques, and applications, especially in the area of Virtual Reality (VR) and Augmented Reality (AR). Interaction in three dimensions is not well-understood, but is crucial to highly interactive VR/AR applications like immersive education, scientific visualization, and immersive design. The mission of our lab is to perform both basic and applied research in 3D interaction and VR/AR technology, and to develop applications of VR/AR in a wide variety of domains.

Current research topics include understanding the effects of display fidelity and interaction fidelity, 3D interface design for gaming, models of 3D pointing performance, high-precision pointing techniques for large displays, free space gesture interfaces, VR applications for sports, and the effects of spatial information presentations on learning.

HP Labs Immersive 3D

HP Labs is conducting research using large display walls, 2D and 3D. HP has multiple product and services offerings in the so-called “Big Walls” area ranging from digital signage to large telepresence facilities (such as HP’s Halo). Our research extends this by following two main, intertwined branches. The first examines a new style of 3D entertainment, and the other targets commercial and industrial uses for 3D enabled operations centers, brainstorm or war rooms, and control centers. We are conducting research in:
• Computer vision, image processing, and recognition,
• Robust multi-imager and multi-camera modeling and calibration,
• 3D data visualization,
• High performance computation, transport, and imaging on hardware accelerated platforms (CPUs and/or GPUs),
• Novel human-big wall interaction modalities across heterogenous systems, and
• Local and remote collaboration technologies.

We have publicly shown our Immersive 3D Entertainment efforts with events at the 2010 Sundance Film Festival and the Earth Wind and Fire Concert at 2011 CES in Las Vegas (links listed below). HP’s goal is to experiment with non-standard aspect ratios (e.g. 3:1 for concert stage, 5.5:1 for basketball court) to allow the audience to experience the event as though you are seated at the best seat in the house. This is a break from the traditional movie view from the director’s eyes and allows the audience to “look around” and soak up the scene which works so well in 3D. To do this, we are experimenting with large multi-projector 3D displays (Pluribus), multi-imager camera capture (Herodion), and advanced digital image pipelines (Pericles). We conduct in-situ laboratory studies at entertainment events such as concerts, sports events, fashion shows, and other gaming.

We have built an experimental operations center with multiple 2D and 3D walls and other services. Together with many HP customers, we are exploring novel ways to use Big Walls to address issues in areas such as disaster recovery, emergency response, hospital wards, city monitoring and management, mergers and acquisition planning, product planning, and supply chain management. We are creating a next generation capability by using Big Walls together with mobile devices, touch surfaces, 3D data visualization techniques, and techniques for displaying, manipulating and visualizing large amounts of unstructured data. In addition to our research, we participate in the real world by providing support for HP’s worldwide operations centers and the HP supported gallery at the Newseum due to open in 2012.

Links to entertainment reviews
HP 3D Live: CES Earth Wind and Fire concert

http://blogs.forbes.com/oliverchiang/2011/01/08/ces-hp-believes-in-3-d-too-but-on-a-larger-scale-much-larger/

http://www.monstercable.com/events/ces2011/3dexperience.asp

http://h20435.www2.hp.com/t5/The-Next-Bench-Blog/Earth-Wind-and-Fire-Going-Large-Live-and-in-3D-at-CES/ba-p/60745

http://www.hardwaregeeks.com/index.php/site/comments/hp_streams_concert_live_in_3d/

http://venturebeat.com/2011/01/14/hp-streams-earth-wind-fire-in-live-3d-on-a-huge-screen/

2010 Sundance Film Festival

http://philmckinney.com/archives/2010/01/sundance-and-3d.html

http://h20435.www2.hp.com/t5/The-Next-Bench-Blog/Stories-in-3D/ba-p/52743

HP Newseum announcements

http://www.hp.com/hpinfo/newsroom/press/2010/100930a.html

http://www.newseum.org/news/2010/09/hp-announcement.html

I am interested in shape / data analysis using geometric and topological methods, with applications ranging from graphics, visualization, bio-informatics, and manifold learning. In particular, my research aims to integrate geometric and topological ideas to develop both theoretical foundations and practical algorithms for information estimation, feature identification and abstraction, shape characterization, matching, and shape deformation. I am also interested in exploring how structures and results developed in computational geometry and topology can be used and extended to complement / augment statistical data analysis methods.

I work mostly in two areas Computational Geometry and Computational Biology.

Over the last three decades Prof. Crutchfield has worked in the areas of nonlinear dynamics, solid-state physics, astrophysics, fluid mechanics, critical phenomena and phase transitions, chaos, and pattern formation. His current research interests center on computational mechanics, the physics of complexity, statistical inference for nonlinear processes, genetic algorithms, evolutionary theory, machine learning, quantum dynamics, and distributed intelligence. He has published over 130 papers in these areas, including the following recent, related publications. Most are available from his website: http://csc.ucdavis.edu/~chaos/.

My research group creates new software and algorithms which enable robots to effectively assist physicians and automatically accomplish healthcare tasks. We focus on developing motion planning algorithms and physically-based simulations with applications to robot-assisted surgery, treatment planning, medical image registration, personal assistance, and physician training. Our research spans the following areas:

(*) Motion Planning for Healthcare Robotics: Our objective is to compute actions that enable a robot to automatically accomplish clinical or assistive tasks. Using sampling-based and geometric approaches, we develop algorithms to automatically maneuver medical devices such as robotic surgical assistants or steerable needles around anatomical obstacles to difficult to reach sites, enabling new surgical and interventional procedures. Our research identifies and exploits parallels between medical problems and traditional robot motion planning problems while explicitly considering the complexity and uncertainty inherent in healthcare applications.

(*) Physically-based Medical Simulation: Human soft tissues are heterogeneous and have nonlinear properties, resulting in complex deformations during clinical procedures. Using finite element methods and mesh maintenance algorithms, we are developing simulations of soft tissues and their interaction with medical devices. These simulations can assist physicians in registering diagnostic and treatment images obtained at different times, and can also be used for interactive physician training and procedure planning.

My work i is broadly at the intersection of HCI and Information Retrieval/Data Mining. I generally work with extremely large datasets of text (Web, large collections of scientific texts, historical collections of books), behavior (click and search logs), and networks (social systems, Web, P2P, etc.). My work tends to have both an empirical descriptive/modeling component as well as a system-building component (leveraging what know to build better systems). Current areas of focus include:

- Temporal-informatics – providing front ends to users who have data-extraction needs for time-varying data (e.g., http://www.cond.org/zoetrope.html)

- Meme mutation – how information mutates as it moves through networks

- Scientific data evolution – mining large collections of scientific literature to detect evolution and emergence

- Collaborative analytics/visualization – building systems to support users without a great deal of statistical and visual literacy in understanding data

- Privacy – how perceptions shape behavior

My group works in computer vision with an emphasis on the interpretation of video and the understanding of human behavior, in particular the social behavior of children. I am leading a large multi-institution effort to develop the science and technology of behavior imaging— methods for sensing and modeling behavior to support the diagnosis, treatment, and understanding of developmental disorders such as autism. A related area of interest is video interpretation and dynamic scene understanding, where we have developed novel video segmentation methods based on temporal causal analysis. A third area is perception for robotics, with specific projects in autonomous high-speed driving (the autonomous rally car project) and manipulation in clutter using the PR-2 robot from Willow Garage. Projects are available in all of these areas.

The Interface Ecology Lab imagines and develops integral and sensitive human-centered computing to support nuanced and exciting aspects of life, including how we form and express ideas and intentions, how we learn and innovate, how we gesture and communicate, how we coordinate and cooperate, how we participate and share, how we function under stress, and how we respond to crises. We connect diverse methodologies, engaging an interface ecosystems approach to engage computing in holistic avenues of human experience.

We have just developed an innovative multi-finger sensor, ZeroTouch. We are now poised to integrate ZeroTouch into interactive environments, developing new embodied bi-manual and free air interaction techniques and experiences.

We have released the open source meta-metadata language and architecture, for authoring platform-independent wrappers for heterogeneous information sources. Meta-metadata supports information extraction and knowledge integration. It facilitates writing programs that enable new human experiences of information visualization, physically-based modeling, and embodied interaction. Research here involves new knowledge semantics and modeling, on the one hand, and new interactive visual applications, on the other. Social media and digital libraries are connected.

We are developing special relationships with disaster response organizations. Texas Task Force 1 engages in major disasters, such as 9/11, and hurricanes Katrina, Rita, and Ike. TEEX Disaster Preparedness and Response runs an internationally-renowned academy for first responders. We are engaged both in developing zero fidelity simulation games for education, and in creating innovative multi-surface information systems for crisis response.

The typical search interface is great for finding a single element of information, but weak for information-based ideation tasks, in which the human goal is to to develop ideas with support and stimulus from information. The combinFormation platform reconceptualizes information-based ideation support as mixed-initiative information composition, integrating browse-search-collect-visualize-and-organize, while representing each collection as a connected whole. To validate composition, information-based ideation metrics and grounded theory are extended and synthesized, developing formative and summative evaluation methodologies. Application contexts include thesis writing in computer science, and design in architecture and mechanical engineering.

Computational Imaging, Computational Photography and Display, Computer Vision, Medical Imaging, 3D imaging, Image,Video and Light-Field Processing

My research explores how computing can support human creativity and mediate interpersonal communication. My group develops techniques for creating three-dimensional content and for simulating human motion. I am also interested in related issues in computer vision and machine learning.

The research goal of the Music Computation and Cognition group at USC is to systematically study, computationally model, and scientifically explain human abilities in music perception and cognition, and in music making, such as music performance, improvisation, and composition.

Research projects at the laboratory address the three main areas of music analysis, performance, and composition/improvisation. They include:

• decoding and generation of decisions and gestures in music performance;
• analysis of musical ensemble in networked music performances;
• multi-modal interaction in human-machine musical improvisation; and,
• automatic analysis and visualization of musical structure.

The MuCoaCo website — http://www-bcf.usc.edu/~mucoaco — and the MuCoaCo blog — http://mucoaco.blogspot.com — contain detailed project descriptions, video demonstrations, and lists of publications and awards. The broad areas spanned by the research projects are mirrored in the seminar course on Topics in Engineering Approaches to Music Cognition — http://www-scf.usc.edu/~ise575 .

The successful postdoctoral researcher is expected to engage in music computing research, and to have completed graduate-level training in at least one of the following quantitative analysis areas: mathematics, computer science, operations research, signal processing, statistics, or neuroscience. It is highly advantageous for the candidate to possess graduate-equivalent practical or theoretical knowledge in music analysis, performance, composition, or music perception and cognition. Preference is given to individuals with prior experience in music computing research.

MuCoaCo affiliates have had backgrounds in Computer Science, Electrical Engineering, Industrial and Systems Engineering, Music Composition, Music Performance, Neuroscience, and Psychology.

Noshir Contractor (http://nosh.northwestern.edu) is the Jane S. & William J. White Professor of Behavioral Sciences in the McCormick School of Engineering & Applied Science, the School of Communication and the Kellogg School of Management at Northwestern University, USA. He is the Director of the Science of Networks in Communities (SONIC) Research Group at Northwestern University.

Professor Contractor is investigating factors that lead to the formation, maintenance, and dissolution of dynamically linked social and knowledge networks in a wide variety of contexts including communities of practice in business, translational science and engineering communities, public health networks and virtual worlds.

Research in the SONIC (Science of Networks in Communities) http://sonic.northwestern.edu which he directs seeks to understand and enable multidimensional social networks. Multidimensional networks include different types of nodes (such as people, documents, data sets, and tags) and the different types of relations that connect them.

In order to understand the emergence and dynamics of multidimensional social networks, research in the SONIC lab combines (social science) theory driven and data driven (statistically modeling, data mining and machine learning) approaches.

In particular the lab is interested in the statistical modeling of networks using p* /Exponential Random Graph Modeling (ERGM) techniques and stochastic actor orient modeling techniques (implemented in SIENA). In addition, there is a strong emphasis on conducting these analysis on large scale networks. The lab has access to many large scale server-side sources of data such MMOs (Massively Multiplayer Online games) and the Web of Science. The insights gained from understanding the emergence and dynamics of multidimensional networks is used to enable networks by building sophisticated recommender systems that provide individualized suggestions on who to collaborate with or what team to assemble.

His research program has been funded continuously for over 15 years by major grants from the U.S. National Science Foundation with additional current funding from the U.S. National Institutes of Health (NIH), Air Force Research Lab, Army Research Institute, Army Research Laboratory and the MacArthur Foundation.

Professor Contractor has published or presented over 250 research papers dealing with communicating and organizing. His book titled Theories of Communication Networks (co-authored with Professor Peter Monge and published by Oxford University Press, and translated into simplified Chinese in 2009) received the 2003 Book of the Year award from the Organizational Communication Division of the National Communication Association. He is the lead developer of C-IKNOW (Cyberinfrastructure for Inquiring Knowledge Networks On the Web), a socio-technical environment to understand and enable networks among communities, as well as Blanche, a software environment to simulate the dynamics of social networks.

I have broad research interests covering the impact of technology on work, social interactions and economics. As a user experience researcher, my specific interests include the design of knowledge management systems and online communities, secure mobile applications, workflow orchestration, and business process modeling by non-programmers.

My research interests are very broad, covering all aspects of enterprise security. I am currently particularly interested in insider threat research, the application of anomaly detection to improving security algorithms, usable security (particularly in large data centers), and improvements in identity and access management. Other related areas include visualization of data for security purposes, security as it pertains to cloud services, and data leak prevention. A postdoc will be expected to help define an appropriate research direction based on a combination of personal interest and ongoing consultation and collaboration with CA researchers and the applicable business units.

Researchers at the Lab work closely with the security business unit to ensure that the research is going in a direction that is likely to produce results that can be used to improve products and thus impact the security postures of our clients. (CA products are currently used by 98% of the Fortune 1000.)

This position provides the opportunity for a postdoc to perform research that has the possibility of improving security for a large number of government and private organizations internationally, as well as providing the opportunity to publish in journals, present at conferences, and patent innovations.

Anita Komlodi is Associate Professor and Graduate Program Director for Human-Centered Computing. Most of her current research focuses on user diversity in technology use and attitudes: age, gender, and cultural differences in technology interactions. She is also interested in the study of Human Information Behavior and the design and evaluation of user interaction methods in information retrieval and information visualization.

It is now widely recognized that current commercial many-core systems are simply not good enough: most programmers can’t handle them. Therefore, alternatives must be developed. Anticipating this problem over a decade ago, the Explicit Multi-Threading (XMT) framework has been under development at the University of Maryland. XMT is a general-purpose many-core computing platform with the vision of a 1000-core chip that is easy to program but does not compromise on performance.

XMT is built to support the PRAM theory of parallel algorithm, which is second in its wealth only to the serial algorithms. Since four decades of parallel computing research provided no real alternative to the PRAM, the XMT project sought to draft specifications for the general-purpose many-core desktop of the future, by first inventing hardware and software support for the abstractions developed by PRAM algorithmics — a task deemed impossible by architecture researchers prior to the accomplishments of the XMT project.

A 2010 status report of XMT appears in U. Vishkin, Using simple abstraction for reinventing computing for parallelism, CACM, January 2011. Order of magnitude speedups, dramatic advantages on teachability from middle school to graduate courses have been demonstrated. And favorable student ranking for achieving speedups relative to standard platforms have been demonstrated.

So far, XMT has spanned applications, parallel algorithms, compilers, HW/SW and education of parallelism. Research opportunities building on this promising foundation include now also CS education, bioinformatics, machine learning and other applications, security, OS, and SW architectures.

There is so much more to the potential of many-core parallel computing than the horizons of commercial hardware offer!

I work on projects related to the design and evaluation of large-scale software infrastructure in support of data collection and analytics of social media information during times of crisis and/or mass emergency. My work makes use of techniques and technologies found in software engineering, software architecture, Web application frameworks and REST-based Web services.

I work at the Scientific Computing and Imaging institue on uncertainty visualization which aims to capture indications of uncertainty such as error, accuracy and variance and reflect this information into visualizations. This work draws from both scientific and information visualization, as well as computer graphics, statistics, and others. Current projects look at simulation data from materials science, atmospheric and climate modeling, and genomics.

Current interests include:
– 3D shape and motion capture and reconstruction
– joint analysis of large corpora of images. shapes, or motions
– large-scale virtual content creation
– qualitative analysis of stochastic dynamical systems
– lightweight fusion of distributed signals
– mobility patterns and their influence in mobile network communication
– inference on rankings and other discrete structures

Overview: I lead a multidisciplinary group that examines a range of human-centered computing issues. A main area of current research is in “crisis informatics.” This line of research combines human-centered computing and socio-technical empirical study with software engineering; natural language processing and information extraction; information visualization; and network security, privacy and scalability. My group conducts empirical research of on-the-ground emergency activities as well as CMC-based interaction, which has had national and international impact. We continue to drive our results toward design and implementation of new forms of Computer-Mediated Communication for use in crisis situations by both citizens and responders. Our goal is to break new ground on delivering technology innovation to those who are most in need of it.
Background: In an increasingly global society and on a planet experiencing effects of climatic change, large-scale emergencies both instigated by humans and arising from nature can devastate human life and a tightly-woven social fabric. A prevailing hope is that information and communication technology (ICT) aimed at official responders can help reduce impacts of large-scale disruptions, including political crises, natural disasters, pandemics, and terrorist threats. Our program builds on the hopes of ICT, but takes a different approach, focusing instead on an understudied but critical aspect of large-scale emergency response—the needs and roles of members of the public. By viewing the citizenry as a powerful, self-organizing, and collectively intelligent force, ICT can play a transformational role in crisis. Our research aims to leverage the knowledge of members of the public through reuse of publicly available computer mediated communications. The research that our multid isciplinary group will conduct includes the study and integration of heterogeneous information and–with techniques of information extraction through natural language processing as well as trust and reputation modeling–add meta- information to help users assess context, validity, source, credibility, and timeliness to make the best decisions for their highly localized, changing conditions.

Intellectual contributions include the expansion of broad thinking about societal-scale interaction in the area of crisis informatics, and bridging information, cognitive and computer science. This work also includes consideration of implications to emergency management and telecom policy. Innovation will be based on empirical study of CMC and citizen information needs in a range of international crisis events, with the development of methods to analyze such behavior in light of privacy, security, ethical and policy issues. Our project integrates technology and emergency management partners in its mission.
Skills. A CI Fellow working with me is first and foremost encouraged to establish a line of independent research that follows their PhD research trajectory. However, I invite applicants to extend their current research to bridge with our large research venture that includes 7 faculty and numerous graduate students for mutual benefit.

To that end, a number of skills would fit well into our larger research effort: ethnographic research on sociotechnical systems; policy and institutional considerations; systems building of collaborative environments; social network analysis; information visualization; data mining and natural language processing. Previous knowledge and experience with emergency and disaster response is welcomed but not required.

Video processing, image processing, filter banks and wavelets, image analysis and understanding, 3D – multiview signal processing, machine learning, signal processing for medical devices, 3D video processing and communications.

The Lydia news/blog analysis project seeks to build a relational model of people, places, and things through natural language processing of news sources and the statistical analysis of entity frequencies and co-locations. Our analysis is quite different from Google News. We track the temporal and spatial distribution of the entities in the news: who is being talked about, by whom, when, and where? Please visit our website (http://www.textmap.org) to see our analysis of news obtained from over 500 daily online news sources.

Now is a particularly exciting time to join the Lydia project ! We have begun active collaborations with political scientists, sociologists, and finance professionals to apply our news analysis to their fields. This opens up Computer Science research opportunities in data mining, machine learning, network analysis, and visualization, We easily keep up with all world’s news feeds plus one terabyte corpus of historical news, thanks to our 28-node cluster and Hadoop-based distributed processing. Our current focus is on NLP, particularly improving our entity recognition and sentiment analysis, in English and other languages.

I also have an exciting bioinformatics project in synthetic biology. Working with virologists, we design virus genome sequences to serve as vaccines, then synthesize our designs to see how they grow! Our work has been in Science and Nature Biotechnology, and is now being applied to several human and animal pathogens.

Grasping and manipulation, dexterous robots, robot hands and robot hand design, human subjects studies of dexterous manipulation, dexterity in animated characters, physically based character animation, puppeteering and interfaces for animating characters and game play.

We are interested in investigating how cognitive design principles can be used to improve the effectiveness of visual displays. The goals of this work are to discover the design principles and then instantiate them in both interactive and automated design tools. Recent projects based on this methodology include automated map design algorithms, systems for designing cutaway and exploded view illustrations of complex 3D objects, tools for crowdsourcing data analysis, perceptual studies of charts and diagrams, and interactive tools for manipulating digital photographs and video.

Electrical-engineering (ee) degrees. Learning in fields such as art/architecture.
Widely published in archival journals. Three edited books. Authored encyclopedia articles.
Doctorate: dissertation in stochastic control (ee); mathematics and statistics minors; reading knowledge of Russian and French. Now use Google-translate to write Russian.
Rand Corporation employment and UCLA projects on applied problems ranging from operations research to biomedical computing.

Emeritus Professor since 1994.

My research interests lie in human-computer interaction in scientific collaborations, collaborative creativity, cyberinfrastructure, eScience, information visualization, usability in daily life, and how social media and computer-mediated communication are changing scientific practice.

I am especially keen to work with someone with a strong background in visualization who would like to make use of our facilities to do eye tracking on a very high resolution stereo display. We are currently investigating human visual strategies used in working with stereo displays, and also in using 3D gaze point to optimize volume visualizations for improved perception.

In addition, we have active projects in developing provably better visual ways of communicating information about evolving hurricanes to officials responsible for making public health and safety decisions, and in using game technology as a framework for teaching and learning computer programming.

We are working actively in medical informatics on Electronic Health Records and data analysis of temporal event sequences such as found in patient histories. Our other major effort is on network visualization, related to the NodeXL (www.codeplex.com/nodexl) and other tools, especially as applied to Technology-Mediated Social Participation.

I specialize in interactive visualization and visual analysis of large and complex data. In addition, I maintain an active research agenda in 3D urban modeling and spatial awareness.

Currently, I’m investigating human + computing in visual analytics where I look to identify the roles of human interactions and automated analysis techniques. This area of research has led me to think more about the nature of interaction (in analytical tasks), and how to interpret the interaction in a machine-readable way. On the other hand, I am also looking at incorporating data mining and machine learning methods into the analysis loop, while investigating how to combine the two (human computing and computer computing).

More generally, I am interested in pushing the boundary of using automated methods in creating visual interfaces and quantifying the semantics behind the visual representations that the user would interact with. Much of this work relies on developing metrics in order to evaluate the automated methods, which is an open challenge in the visualization world, and is a topic that I’d like to investigate further.

The Scientific Computing and Imaging (SCI) Institute at the University of Utah invites applications for one post-doctoral researcher for interdisciplinary work in scientific visualization. The successful candidate will perform research in scientific visualization and work closely with SCI Institute researchers and external collaborators to integrate research into SCI Institute software applications and apply this software to compelling problems in science and engineering.

The SCI Institute is seeking a highly talented and committed individual with a demonstrated ability to work well with minimal supervision in a multi-disciplinary research environment. The successful candidate will contribute to the Institute’s world-class research and software development in scientific visualization and have the opportunity to develop their research, publication, and presentation skills under mentorship from established faculty investigators.

Human activity analysis, gesture and sign language recognition, internet vision, object recognition, tracking and analysis of groups (including animals, humans, cells), comp. vision for virtual reality and human-computer interaction, indexing and retrieval methods for distributed video databases, multicamera tracking.

GUI for molecular modeling software and highly parallel computation with GPUs for materials modeling

My primary interest is in programmable graphics hardware and the things you can do with it. Much of my recent work is at the intersection between rasterization and ray tracing or volumetric effects, including monte-carlo tracing for soft shadows, multiple refractions, translucent objects, and higher-order GPU traced primitives for isosurface rendering.

Meanwhile, I also maintain interests in GPU programming for non-rendering purposes. Recent works in that vein include image segmentation, longest common subsequence, and multilevel multidimensional scaling.

My research interests are in modeling, representations and algorithms for realistic image synthesis. My current projects include advanced methods for the multi-spectral capture of material appearance, multi-resolution design of material appearance, designing textures, and creating systems for initial conceptual design of both shape and appearance. My work includes adapting methods from the human perception literature to inform techniques for graphics modeling. I work on projects in cultural heritage and evolutionary biology to provide context for the methods I am developing.

My primary research interests are in computer graphics and animation, especially using physical simulation for computer animation. I am also interested in scientific computing, numerical methods, computational physics and computational geometry.

Anything that involves transforming images into descriptions (or vice versa)

1. Usability and information visualization applied to biomedical informatics, including bioinformatics and clinical informatics
2. Collaborative data portals in support of bioinformatics, including semantic applications.
3. Translational research tools, including social networking & collaboration finding, coordination tools and resource ontology systems.

Computer graphics, vision, and multimedia
Computational photograph and cinematography;

Our research thesis is to develop large-scale data-intensive analytics platform that combines massively parallel data management with massively parallel data analytics, to extract insights from a combination of structured, unstructured, static and streaming data. We also research on novel applications in industrial contexts, such as retail intelligence, consumer social media, environmental sensing and energy production and operations management, that depend on such an analytics platform for scalabiility and performance.
We are particularly interested in candidates who have experience and knowledge in one or more of the following areas:
• Data mining for time series analysis, real time analytics, incremental algorithms, and event stream processing.
• Analytics over mobile social media and information extraction from text; Web 2.0 applications and consumer analytics.
• Implications of multi-core, parallel co-processors, and flash devices on dat a management and analytics
• Large scale data warehousing (data integration, query optimization, workload management), design of robust query processing algorithms and adaptive indexing

I am interested in extraction of information from scientific documents to help improve recommendation systems, search engines, etc. We have ongoing projects in table, figure, and algorithm metadata extraction from the CiteSeerX digital library. We are also working on a citation recommendation system for CiteSeerX. As part of a second project, ChemXSeer, which aims to build cyberinfrastructure for chemistry, I am investigating indexes for graph databases. I am also interested in text mining and exploring techniques to extract entities and their relationships in vertical search engines.

My main research areas are visualization, geometry processing, and high-performance computing.

I’m interested in machine learning applied to computer vision, graphics, and photography. I study the statistical properties of images, and how we can exploit those for image understanding, enhancement, and designing novel cameras. I’m also interested in designing better low-level features for object recognition.

My research interests are in the areas of computer vision and digital imaging and video. More specifically I have been recently working on example based image editing and analysis, object and action recognition and summarization of visual data.

My research spans the following broad topics in the field of computer systems:

•Computer Architecture and Compilers – I am interested in a wide range of aspects involving the intersection of architecture and compilers. My specific focus is on performance, power consumption, and reliability issues for embedded and multi-core processors.
•Reconfigurable Computing – I look into uses of reconfigurable computing as a general enabling technology. Specifically, I focus on the acceleration of various diverse application domains such as cryptography, image and video processing, and data mining, as well as the use of multi-FPGA platforms for fast system prototyping.
•Security – I study the use of automation to address various aspects of security and trust. Past projects in this area include the design and analysis of compiler and architectural approaches to improve software security, as well as an investigation into design methodologies that reduce the effectiveness of side-channel attacks on hardware/software systems.

My research team is engaged in projects involving design, meta-design, virtual worlds, virtual teams, and collaborative work. We often work on this research in the context of software engineering and particularly, collaborative software engineering. However, we also work in a number of other contexts, such as the knowledge work carried out in large organizations and communities of practice, or the play and work carried out in online games and virtual worlds. Depending on the research context, we perform field (ethnographic-like) studies, laboratory studies, and usability inspections to gather data about people’s behavior. Often our work involves developing prototype software tools or new user interfaces and evaluating them. Sometimes our work is only about observation and analysis.

Recently, we constructed visual tools to help software developers and managers maintain an awareness of distributed software development activities. We are currently investigating ways these same tools can support trust in global software development teams. We also are carrying out a small scale ethnographic study to better understand design in virtual worlds and extend theories of meta design based on our findings.

I have always been keenly interested in what technology and structure imply about human behavior and have a desire to craft work environments that augment human capabilities. An example was my dissertation work about a software tool that improved the performance of otherwise poorer performers on programming tasks without hindering better performers. Thereafter, I was always interested in projects that informed on how individual differences could be reduced. In addition to augmenting human capabilities, I am interested in user interfaces that improve the human experience for their end users. I sometimes think of this in terms of creating more humane interfaces or interfaces that support our humanity. Increasingly, I am drawn to research in the humanities for insight.

I am greatly influenced by the writings of Carl Jung, Herbert Simon, Douglas Engelbart, Donald Schoen, Donald Norman, Marvin Minsky, Mitch Walker, my own graduate advisor, Gerhard Fischer, and his colleagues at the University of Colorado, Boulder, and a number of researchers in Artificial Intelligence, Cognitive Science, Queer Studies, Activity Theory, Participatory Design, Human-Computer Interaction, Computer-Supported Cooperative Work, and Science and Technology Studies.

I work broadly in the area of human-computer interaction with a specific focus on information visualization and visual analytics. In particular, my research helps people and organizations who have large datasets that they seek to analyze and understand. We build interactive visualization systems that allow people to explore the data and examine it under different perspectives.

More recently, my work has focused on visual analytics of datasets that are composed of unstructured, semi-structured, or structured textual documents. Large document collections can be found in areas such as intelligence and law enforcement, academic disciplines, consumer product reviews, investigative reporting, etc. We are inventing visualization techniques and building systems to help people who work in these areas to make sense of large sets of documents.

I would, however, be interested in exploring almost any type of data visualization that helps people with analysis. I bring an HCI perspective to these types of problems and my research typically involves much user and task analysis as well as evaluation.

For more examples of my work and the types of projects I do, please see my research group’s webpage at http://www.gvu.gatech.edu/ii.

Dr. Mohler’s research interests are focused on the implications if spatial ability and visual thinking on the development and production of computer graphics. He also delves into interactive media use for communication and education.

My research interests currently span the areas of Information Visualization and HCI. I am largely interested in supporting end-users (e.g. knowledge workers, scientists, or children) in both creating visualizations and analyzing data.

We have most recently developed and evaluated a novel method for visualizing the diversity and depth of a pool of objects. This work may apply not only in areas such as university admissions and team building/management but possibly to other areas of ‘design’ such as drug design, or investment portfolio design.

In forward looking research, I am generally interested in understanding how the everyday user is affected by the large amount of data available to them and how they can and do use this data to solve problems and inform decisions and behavior change in their everyday lives. I am particularly interested in the use of visual interaction methods to facilitate this process.

For example, it is becoming clear that sensor technology is going to play an important role in aging at home. Smart homes are being built to monitor elderly individuals, help them in their everyday tasks (activities of everyday life), and to provide feedback to their community of caregivers (care managers, physicians, and family) so that this community can make informed decisions regarding care and to simply put their minds at ease in the specific case of immediate family. Unfortunately, very little work has investigated the use of this vast amount of data to help elderly individuals take care of themselves. With the Center for Healthy Aging Research at OSU, I am currently investigating techniques to better support self awareness through technology. This work applies not only to aging, but to fitness and consumption behaviors as well.

While these are particular projects of interest to me, I am interested in exploring data visualizations in general with a focus on the everyday end-user. For more information, please visit my web page at http://eecs.oregonstate.edu/~metoyer.

geometric and topological reconstruction from samples, detection of symmetries and repeated patterns in data, sense making from distributed sensor signals, structure-preseving summaries, information discovery and brokerage in sensor networks, mobility analysis and prediction, routing to mobile users

My research interests are in the areas of computer vision, visual and multi-media technology, and robotics. Common themes that my students and I emphasize in performing research are the formulation of sound theories which use the physical, geometrical, and semantic properties involved in perceptual and control processes in order to create intelligent machines, and the demonstration of the working systems based on these theories.

My current projects include basic research and system development in computer vision (motion, stereo and object recognition), recognition of facial expressions, virtual(ized) reality, tracking of a large number of stem cells in live microscopic images, content-based video and image retrieval, autonomous navigation of ground and air vehicles, and quality of life technology.

My research is in the general area of Human Centered Computing. My research interests include virtual reality, virtual worlds, 3D user interface design, multi-modal interaction, virtual human/human interaction, and HCI. Current projects include creating and testing virtual patients for training nursing students in interview skills (with the School of Nursing), developing procedures and evaluating technology to improve vehicle inspection on the assembly line (with Department of Mechanical Engineering and BMW), an experimental investigation of social inhibition and facilitation in avatar interactions in virtual worlds, and the design and construction of multi-touch display hardware and applications (joint with Juan Gilbert’s research group).

I have supervised 10 Post Doctoral Fellows and Visiting Researchers in addition to a number of Research Scientists. Our current research group includes three Ph.D. students (Lauren, Toni, and Jerome), two undergraduates (Austen and Adam), one Post Doctoral Fellow (Dr. Amy Ulinski), Assistant Professor Sab Babu, and myself. During the summers we are joined by 5-8 more undergraduates supported by a NSF REU Site Grant and the DREU program. Our group philosophy stresses team projects, mutual support and mentoring, interdisciplinary research, and frequent meals together.

In addition to my research and teaching, I am also Director of the School of Computing at Clemson University. The School consists of three Divisions: Computer Science, Visual Computing, and Human Centered Computing. Further information of the School is available at: www.clemson.edu/computing .

My current research interests include the visualization of data with associated uncertainty, multivariate visualization, volume rendering, information visualization, perceptual and illustration issues in visualization, artistic rendering, dynamic and interactive representations and interfaces, and the experimental validation of visualization techniques.

I am interested in a wide variety of application domains, including medical, atmospheric, environmental, educational, policy-making, and machine learning. I believe visualization research is most productive in collaboration with domain experts.

To achieve truly life-like telepresence, we must better understand key multimedia acquisition, processing, and display technologies. These include video segmentation and matting for background replacement and for gesture-based human-computer interaction, depth sensing, augmented reality, 3D rendering, and view synthesis. We must also understand how to embed these technologies in real-time systems to create seamless and highly intuitive interaction experiences.

My research revolves around the question: “How can we use our
understanding of human perception and artistic traditions to improve
our tools for communicating and data understanding?” I am exploring
this question in four areas: visualization (creating tools to help
people make sense of complex data sets); creating better tools for the
creation of images and video; creating better character animation
technologies for films and games; and computational structural
biology (understanding the shapes of molecules).

I’m a statistician working at the intersection of statistical graphics and infovis, developing tools to support better data analysis. I’m particularly interested in visualisation, but my interests span the entire data analysis process from first accessing and reshaping the data into appropriate form for analysis, to the iterative cycle of modelling, visualising and transformation that leads to understanding, and finally to communicating that new knowledge to others.

I’m particularly interested in how we can use visualisation to better understand the complex statistical and machine learning models that are needed to accurately model large quantities of data. The state-of-the-art tools can give incredibly accurate predictions, but how can we open up the black box and understand what these models tell us about the underlying science.

Rice University has a beautiful campus, located in a tree-lined residential neighbour of Houston. From Houston it’s easy to travel anywhere in the country from the Southwest and Continental hub airports. Rice has a particularly strong program of post-doctoral mentoring with many opportunities for you to improve your teaching, research and grant writing skills through workshops, lunches and tutorials. You’ll gain the skills to become a successful academic and prosper on the job market. Finally, Rice has one of the highest rates of free lunches in the country!

My interests are in two very different fields:
* Foundations of visualization: how does it work, how do we read visualization, how do we understand data that is presented visually, etc.
* Visualization for visual communication: how can visualization be used to more effectively communicate complex data to broad audiences?

Visualization is a very applied field, and that is a good thing. But we also need to work on understanding its foundations better: how do we attach meaning to visual shapes? How do we read information from visualizations? What roles does the structure of the visualization play (in addition to the basic value encodings)? How can we build better models that will allow us to make testable predictions, like a real science?

At the same time, visualization is a great way to get people interested in data. And there is more data available today than there ever has been. But how do we provide better visual means to communicate that data? How can we make visualizations fun and engaging without losing their ability to clearly and correctly get data across? How can we employ ideas from storytelling to get more information across?

The Visualization Center at UNC Charlotte is one of the largest concentrations of faculty and graduate students working in visualization in the U.S. We have state-of-the-art hardware, great topics to work on, and interesting people to work with.

My research interests span computer vision and computer graphics. In particular, my research focuses on using “Internet-scale” data to improve scene understanding and allow smarter image synthesis and manipulation. I am also interested in the connections between human and machine vision.

I lead the Mobile Computing research group at IBM’s Almaden Research Center. We focus on three different themes:
- Understanding how users employ the latest generation of smartphones in order to understand how to build effective mobile user experiences and to identify opportunities to improve existing or create new mobile applications and services.
- Creating new mobile collaboration applications and services. For example, based on our studies of how users employ smartphones we identified an opportunity to improve mobile email clients to better support how users actually use email while mobile: identifying what’s new, identifying what’s important enough to handle immediately, deleting messages that aren’t needed, and deferring the handling of all other messages until users reach a desktop or laptop. See http://www.almaden.ibm.com/cs/projects/mobileemail/ for further details on that project.
- Exploring how mobile phones can act as sensors to capture information as well as windows to access it. We are particularly interested in how many users can contribute information to a central service that can aggregate and analyze that information to provide increased value to a broader community.

In addition to these core areas, my team also explores the creation of new interaction techniques (such as ShapeWriter – http://www.almaden.ibm.com/u/zhai/shapewriter_research.htm) and of underlying theories and models for how mobile users interact.

My research tends to focus on the following three topics:

1) Acquisition and representation of realistic surface appearance and 3D shape: We are investigating new optical scanners that allow measuring the 3D geometry and material properties (BRDFs, SVBRDFs, BSSRDFS, etc.) of physical objects. A related goal is developing representations of measured surface appearance that are compact and enable intuitive editing.

2) Data cashing and reuse in interactive rendering systems: We are developing new real-time rendering algorithms that can share data at the pixel level across consecutive frames. Open problems in this area include developing automated methods for identifying profitable values to cache and adaptive allocation algorithms, new hardware architectures for supporting this type of data reuse, and ways of using this general framework to improve common rendering effects (e.g., spatial supersampling and motion blur).

3) Data-driven image processing: We recently started a project that will investigate new algorithms for improving the quality of an input image using millions of example images harvested from the Internet. We already have access to a unique computing infrastructure that will allow performing these types of data-intensive computations.

I seek a post doc to work with me on projects dealing with coordination in distributed software development. More specifically, projects in my group include data collection and analysis of large-scale software systems, and system design and development to implement cutting edge coordination tools (e.g., Palantír – an eclipse plugin that tracks changes and predicts conflicts, Tesseract – a browser based application that enables interactive exploration of different project entities). My work is placed within the broader context of the Software Engineering lab at UNL (Esquared lab). We have very strong connections with industry, fueling the projects we work on and providing us with interesting opportunities for testbeds.
Within the broad interests, there is room for you to pursue your own projects, though I would also expect you to join in working on grant proposals, papers, advising, etc.

We focus on creating tools to better capture and share visual information. The goal is to create an entirely new class of imaging platforms that have an understanding of the world that far exceeds human ability and produce meaningful abstractions that are well within human comprehensibility.

The group conducts multi-disciplinary research in modern optics, sensors, illumination, actuators, probes and software processing. This work ranges from creating novel feature-revealing computational cameras and new lightweight medical imaging mechanisms, to facilitating positive social impact via the next billion personalized cameras.

With more than a billion people now using networked, mobile cameras, we are seeing a rapid evolution in activities based on visual exchange. The capture and analysis of visual information plays an important role in photography, art, medical imaging, tele-presence, worker safety, scene understanding and robotics. But current computational approaches analyze images from cameras that have only limited abilities. Our goal is to go beyond post-capture software methods and exploit unusual optics, modern sensors, programmable illumination, and bio-inspired processing to decompose sensed values into perceptually critical elements. A significant enhancement in the next billion cameras to support scene analysis, and mechanisms for superior metadata tagging for effective sharing will bring about a revolution in visual communication.

Areas of expertise are human computer interaction, user interface design, usability evaluation and educational gaming technologies. Some of our user studies focus on novice programmers utilizing visual programming techniques (example applications are SecondLife, educational game, 3Dgames). We want to improve user interface design and interaction of applications to improve their practical usage (applications iPhone, WebPages, virtual communities, etc.) In our educational research, we want to capitalize on the dimensions that games can add to computer science undergraduate and graduate curriculum.

Developing and evaluating educational games
Promoting creativity in computer science education
providing intelligent support for learning
Developing and evaluating data-driven methods for intelligent learning support
Developing and evaluating games for health and exercise promotion
involving undergraduates in research
broadening participation in computing
evaluating BPC initiatives
developing games and software for kids to learn computing

I am interested in the interface between discrete and continuous computations. The theory of real computation is a current challenge, and its complexity theory is in infancy.

I am also interested in the algorithmic challenges in applications from computational geometry, graphics, algebraic geometry and continuous mathematics. In the last 20 years,
we have understood a great deal of how to solve the nonrobustness problems in basic geometric algorithms, but much tougher challenges lie ahead in nonlinear geometry, and in transcendental computations.

The focus of my work is on the design and evaluation of augmented reality experiences, especially games and new media artifacts. Our work ranges from the design of underlying technologies and toolkits to support the design, implementation and evaluation of such experiences, through the design, creation, deployment and evaluation of the artifacts themselves.

By Augmented Reality, I explicitly mean systems and experiences that directly augmented a user or player’s perception of the world with spatially registered 3D media. In other words, overlaying graphics and sound directly and precisely on the world, rather than simply creating location-aware or context-sensitive media.

We currently use a wide range of technology in our work, ranging from high end computers connected to precise trackers, to handheld devices (including high-end smart phones like the iPhone, and development kits for next-gen devices). The experiences we design range from tabletop AR games through outdoor wide-area social experiences.

I am particularly interested in students who have a passion for using advanced technology to design, create and evaluate new kinds of AR experiences. My research group includes computer science, human-centered computing, HCI and digital media students. To support our research goals, we collaborate with researchers and students in computer vision, computer graphics, digital media, art and design (both at Georgia Tech and the Savannah College of Art and Design).

My current research focuses on stimulating the human senses in making virtual experiences more realistic. I am also interested in the topic of effective interaction techniques in immersive virtual environments. I am director of the Human Interaction in Virtual Environments (HIVE) Lab, where we are focusing on the use of vibrotactile feedback for various applications in virtual environments, simulation, and real environments.

Irfan Essa works in the areas of Computer Vision, Computer Graphics, Computational Perception, Robotics and Computer Animation, with potential impact on Video Analysis and Production (e.g., Computational Photography, Image-based Modeling and Rendering, etc.) Human Computer Interaction, and Artificial Intelligence research. Specifically, he is interested in the analysis, interpretation, authoring, and synthesis (of video), with the goals of building aware environments, recognizing, modeling human activities, and behaviors, and developing dynamic and generative representations of time-varying streams.

He is motivated to build smart intelligent machines that can interact with people. He is also interested in machines that can handle large amounts of media and wants leverage social networks to build systems for large groups of people to interact and create smart media.

Computer Graphics and Visualization, Biomedical Imaging and Computer Vision, Geometric and Physics-based Modeling, Virtual Reality and Human-Computer Interaction, Computer Animation and Simulation.

Our lab includes advaced research in the areas of computer graphics and a broad array of visual computing topics from input sensors to amatuer content creation (3D model extraction from photos etc), to procedural representations of avatars and animations of same, there are some elements of Video and Image processing using complex statistical algorithms and explorations of challenges / new application scenarios made possible by the evolution of Mobile Internet Devices and the sensors that are expected to be attached, also Mobile Augmented Reality whereby such MIDs can add value to the experience by leveraging the sensor inputs along with direct connection to the internet.

My research is focused on navigating and positioning objects in 2d and 3d virtual environments. The research has developed general gesturing techniques, a new mouse driver for desktop environments, and modeled the effectiveness of users to locate themselves in 3d environments and position objects in 2d. The laboratory has a stereo visor for augmented and immersive environments, magnetic and optical trackers, and grasping gloves.

Future research is extending gesturing techniques for navigating and specifying objects or regions in multidimensional virtual environments using multimedia virtual environments. The primary application area is studying and interrogating multidimensional scientific data. We intend to learn the limitations of computer users to navigate abstract environment and improve the navigation and positioning techniques by human performance modeling.

The primary goal of my research is to help people make sense of overwhelming amounts of information by combining the flexibility of higher-order cognition, the strengths of social collaboration, and the power of machine learning and visualization. My current research uses technology to help large groups of people collaborate to produce knowledge and solve problems that exceed an individual’s cognitive capacity. To this end I have pursued two complementary lines of research: first, investigating how to optimally harness the “wisdom of crowds” in social collaborative systems such as Wikipedia; and second, building machine learning and visualization-based techniques to increase the effectiveness of these systems.

My research interests are in the area of asynchronous (i.e. clockless) and mixed-timed circuits and systems.

Specifically, I am interested in design and test of asynchronous systems, including: (i) high-level specification and translation; (ii) performance analysis; (iii) system-level optimization, including bottleneck detection and alleviation; (iv) mapping to asynchronous pipelined circuits; and (v) test and testability issues.

In addition to pure asynchronous systems, I am interested in mixed-timed systems as well, including globally-asynchronous locally-synchronous systems (GALS), latency-insensitive design, elastic systems (both synchronous and asynchronous), and on-chip-networks (NOCs).

My research group actively pursues work at various levels of abstraction: from circuit level (asynchronous pipeline circuits), to microarchitecture level, to system level. The work also targets several application areas in high-speed and low-power computing.

A particular area of research interest is mobile low-power graphics hardware. In collaboration with Prof. Anselmo Lastra, we are developing architectural techniques for ultra-low-power graphics, suitable for handheld gaming devices, cell phones, PDAs, etc. An active line of investigation is to dynamically trade off solution quality for energy savings when energy conservation is critical. This project also targets several levels of design abstraction: circuit, microarchitecture, system, memory, and software.

Finally, I am interested in exploring the application of asynchronous design techniques for the next-generation silicon and non-silicon emerging technologies, such as nano, quantum and biologically-inspired computing.

My area of research is to develop computational methods that will impact the way scientists analyze data from biological and medical genomics experiments. These methods will allow the incorporation of Gene Ontology information into modeling of gene expression data, and facilitate the simultaneous identification of biological processes that are affected by a treatment or another intervention. I propose to incorporate the gene correlation structure into our gene expression model as well as the indicator of the gene group (obtained from the Gene Ontology). The proposed models should allow more realistic fits to real data. We propose to study Bayesian and non-Bayesian alternatives in order to estimate the model parameters. In addition we propose a methodology to help scientists to develop a gene signature (or pattern) that enables the identification of groups of patients related to a condition or disease. This signature is based on a novel gene weighting scheme applied to the standard Random Forest algorithm. I also propose to extend this weighting scheme to other existing classification and prediction methods such as linear discriminant analysis, support vector machines, LASO and others. An additional interesting and important proposed application of these methods is to the analysis of DNA barcodes.

I’m happy to mentor bright and enthusiastic researchers in just about any
area of computer vision, robotics, or perceptual AI. Recent work includes some nascent collaborations with the UR Medical Center, the George Eastman House photographic museum, Kodak research labs, and the Eastman School of Music.

The Center for Policy Informatics (CPI) leverages cutting-edge technology and cross-disciplinary theory to help individuals and communities make and evaluate policy choices. Established in January 2008, the center’s modeling, simulation and theoretical work supports and critiques activities at ASU’s Decision Theater, a world-class interactive 3d immersive visualization environment created to facilitate group decision-making (www.decisiontheater.org ). CPI’s goal is to develop policy-relevant models that balance the complexity of real-world phenomenon with transparent, actionable evidence. Both externally funded and community focused projects provide the center a forum for the exchange of ideas and resources for modeling and simulations.

For example, researchers at the Decision Theater recently developed an interactive table-top event that used both a system’s dynamics model and decisions support system to test coordination strategies between ASU and external agencies during a pandemic outbreak. The participants were leaders in Arizona’s health and education communities. The value of this exercise was immediately relevant when the swine flu broke out and participants referred to their experience with the decision theater as being instrumental in their response.

Future work will be in a number of context including challenges in public health, sustainability, urban growth, crisis management, and related policy decisions. For example, the challenge of discouraging adolescents from smoking must address a multitude of factors that influence an individual’s decision to smoke: peer influence, social networks, physical addictions, environmental factors, family behaviors, genetics, marketing, and individual decision?making. This project seeks to understand the decisions, environments, and pathways in which the major forces of addictive behavior interact. It will achieve this understanding through creation of a systems?based, social?network model for smoking behavior that will spur new scenarios for possible interventions and allow policymakers and pubic?health professionals to visualize the cascading effects of a multitude of individual decisions.

Please refer to the Decision Theater and Center for Policy Informatics websites for a sample of the range and types of research that is possible ( http://cpi.asu.edu/home and www.decisiontheater.org ).

Potential CI fellows will be expected to assist with both current research and to develop an independent research agenda consistent with vision of the Center for Policy Informatics and the Decision Theater at ASU. Fellows with a multidisciplinary background are particularly encouraged to apply.

Current interests focus on analysis and visualization of large collections of photos (e.g., from the Internet). I’m also interested in optical flow, face modeling, and other topics.

I’m a member of the Media Interaction and Understanding Lab. My focus is mobile systems. How people will use media on these systems and how desktop computing, printing, and other media consumption devices can be used in conjunction with mobile devices. My academic background is in networking, systems, performance and QoS, but for teh past several years my focus is on the use of mobile devices for applications, especially those that involve imaging.

Recently I created and led a project that created a novel mobile consumer application — a mobile media imaging project for a cosmetics consumer application. We solved mobile usability problems, computer vision and color correction, all with any cell phone. Here are some URLs

http://www.sfgate.com/cgi-bin/article.cgi?f=/c/a/2007/07/11/BUG34QU3961.DTL&feed=rss.business

http://www.infoworld.com/archives/videoTemplate.jsp?Id=1330&type=News&tag=Mobile%20Tech

http://www.hpl.hp.com/news/2007/jul-sep/color_match.html

http://www.oprah.com/slideshow/omagazine/2009_omag_beauty_favorites

This application has attracted attention from main stream media and has been licensed to a major cosmetics company. We also built a mobile translation service (text extracted from images) and a home decor color selection application.

Much of my work now is focused on mobile printing, viewing content on the mobile for capture and later use (by printers and other devices). I would love to host someone interested in mobile applications from a usability perspective, applied imaging, mobile web experiences etc. We build demonstrators for the iPhone, Nokia phone, Windows Mobile devices, RIM devices etc. We hope to accept any client device and place much of the computation in “cloud services” designed for specific applications. The ideal candidate should be comfortable prototyping and building systems as well as communicating effectively in written and oral form.

Please feel free to reach out with questions.

Nina

My research interests are in visualization and graphics, in
particular how to deal with large datasets in visualization and
graphics effectively and efficiently. We have been conducting
cutting-edge research in data analysis, visualization, and
geometry processing and computing, including out-of-core
techniques for datasets too large to fit in main memory, graphics
compression, multi-resolution/level-of-detail (LOD) techniques,
time-varying data visualization, computational geometry,
isosurface extraction, surface and volume simplification and LOD
rendering, computational topology and topology-driven
visualization, and high-performance algorithms and data
structures.

My current research is focused broadly on designing and implementing novel algorithms for data mining, analytics and discovery in the context of applications including but not limited to business process management, web services, service oriented architecture, and social networking (blogosphere, twitter, facebook, second life etc). I am also interested in scalable and versatile solutions for data provenance, and data management. I am investigating how data analytics and discovery algorithms can leverage provenance to provide more effective results. I also consider whether the data sources are distributed and owned by different entities which leads to the problem of data integration. Another aspect of my research explores visualization of mined data and addresses adaptive and versatile solutions for simplification detail in the mined results. These projects allow the opportunity to apply existing techniques and develop new techniques in fields such as artificial intelligence, machine learning, matrix factorization, graph theory, computer graphics and other disciplines.

My group is working toward building systems that can visually understand and interact with their environment. Motivated by this goal, we think about roughly three different things: new visual sensors and methods to acquire meaningful information from their data; efficient representations of this information; and applications of these representations to computer vision and graphics tasks.

Current focus areas include shape and appearance capture, color image processing, human perception of colors and shapes, reflectance and illumination modeling, physics-based approaches to scene understanding, identity recognition, and socially-aware vision systems.

G. Michael Youngblood, Ph.D. is an Assistant Professor in the Department of Computer Science at The University of North Carolina at Charlotte, Co-director of the Games + Learning Lab, and Director of the Game Intelligence Group. His work studies how artificial agents and real people interact in virtual environments including computer games and high-fidelity simulations in order to understand the elements and patterns of learning and interaction for the development of better artificial agents. He has worked with real-time computer games, intelligent environments, and robotics since 1997.

Key Research Areas of Interest:
* Using visual and graph-based data mining techniques to analyze and discover interesting patterns in interaction data in online and offline games and virtual worlds
* Ways to make the game/simulation AI-driven character creation process easier and more accessible
* Discovering new techniques and knowledge representations that allow interactive characters in games and virtual worlds to become more realistic (with an eye towards human-level behavior)
* Learning from the game character (agent) creation process at the meta-level
* Better knowledge representations for supporting intelligent decisions in interactive worlds
* Efficient 2D and 3D navigation mesh decompositions for agent navigation as well as information compartmentalization and network partitioning
* Exploring the development and integration of knowledge tools into the interactive, immersive world creation process to support better decisions and more realistic AI-driven participants
* Inferring world geometry from only the spatial movements of the actors in the environment
* Detecting and visualizing behavioral activities across large virtual worlds
* Exploring multi-agent or team-based character creation and behaviors with an eye towards reducing the creation burden, better supporting data structures, and developing a deeper understanding of group dynamics in games/simulation
* Understanding the interaction differences between the real and virtual worlds and how activities map between them
* Using AI as a core game mechanic
* Using AI and games in Computer Science Education
* In general, AI/Machine Learning in games/simulation environments in first-person, third-person, role-playing, real-time strategy, and massively-multiplayer (MMO) genres.

Some of Dr. Youngblood’s current projects include BehaviorShop and BEHAVEngine, which are aimed at making character creation and scenario setup for first and third-person mission rehearsal simulations possible in the field by minimally trained warfighters. The goal is to improve their operational readiness through relevant and always available training—leveraging a practice makes perfect mentality coupled with the idea that the individuals in the field know better than anyone what challenges they encounter daily, so training tools need to fit and support their realities. An extension of this project called CULTURE (Culture Understanding and Learning Tool Using Real-world Examples) focuses on creating cultural simulations to help promote cross-cultural understanding through a collection of simulation opportunities to walk in the shoes of people around the world. These projects leverage current and prior work in information representation and knowledge creation from Dr. Youngblood’s Common Games Understanding and Learning (CGUL) Toolkit.

digital media, virtual environments, interactivity, art, humanities, gaming, language.

My research interests revolve around the development of robust methods for visualizing and analyzing large-scale time-varying multi-dimensional data. Thus, I have interests in three-dimensional flow visualization, determination of boundary surfaces and methods to track these surfaces, query-driven visualization, visualization of tensor fields, visualization of function fields, and analytic methods to determine error bounds for all of these methods. We have been very successful recently creating new methods for visualizing these structures.

The primary focus of our group is to apply computer vision techniques to telepresence, videoconferencing and collaboration situations. We are also interested in 3D acquisition techniques, such as IR structured light and time of flight systems. I do work in image based relighting, often applied to archaeological and museum artifacts. New possibilites enabled by practical techniques based on computer vision methods such as photometric stereo, optical flow, etc are of great interest. Please refer to my research page, http://www.hpl.hp.com/personal/Tom_Malzbender/ for more detail. Several opportunities exist for applying image capture under varying lighting direction, for instance applications in dermatology.

The FASTlab (Fundamental Algorithmic and Statistical Tools Laboratory), consisting of 19 people including 11 PhD students, works on the problem of how to perform machine learning/data mining/statistics on massive datasets, and related problems in scientific computing and applied mathematics. We employ a multi-disciplinary array of techniques from machine learning, nonparametric statistics, convex optimization, linear algebra, discrete algorithms and data structures, computational geometry, computational physics, Monte Carlo methods, distributed/cloud computing, data visualization, programming language theory, and automated theorem proving. We have developed the current fastest algorithms for several of the most fundamental machine learning methods. We also develop new machine learning methods for difficult aspects of real-world data. Our work has enabled high-profile scientific results which have been featured in Science and Nature. We strive for first-of-a-kind analyses of massive datasets from domains such as cosmology, cancer diagnosis, spam blacklisting, and retail transactions.

Spectral Graph Theory, Computational Geometry, Image Processing,
Numerical Linear Algebra, Scientific Simulations, Parallel Computing,
Graph Partitioning, Manifold Learning.

Digital photography and video, HCI, automatic design & presentation of information, non-photorealistic rendering, visualization, image-based rendering, digital typography, and color.

Cyrus Shahabi has two books and more than hundred articles, book chapters, and conference papers in the areas of databases, GIS and multimedia. Dr. Shahabi is the director of Infolab that currently has 2 post-doctorate research staff, 1 programmer staff, 9 PhD students, 2 MS students, and 3 undergraduate students with an annual research funding of $1M. Dr. Shahabi has been the single principle investigator of five NSF grants and has continuously received funding from several other agencies such as NASA, NIH, DARPA, AFRL, and DHS as well as several industries such as Google, Microsoft, NCR and Chevron.

Several real-world systems have been developed at Dr. Shahabi’s lab, InfoLab, in the past 13 years. One of his successful projects is called GeoDec, for geospatial decision making. GeoDec is an end-to-end system that allows users to navigate through a 3D model of a location (e.g., a city), and to issue customized spatiotemporal queries (such as shortest path queries, Nearest Neighbor (NN) queries, and event queries) and retrieve information while navigating about the area. In this system, large-scale sources of static and dynamic data with various modalities, including satellite imagery, road vector data, maps, point data for a specific geographic location and moving objects, 3D models, textures, and video streams are seamlessly integrated, visualized in real-time, and efficiently queried. For detailed information about the large database of spatiotemporal data maintained at InfoLab, including the entire road network of the U.S. provided by NAVTEQ, in addition to more than 15 layers of point data for the entire U.S. (e.g. Transportation Hubs, Hospitals, etc.) GeoDec and its underlying research activities have been receiving funding from NSF, Google, Microsoft, and METRANS.

Another real-world system developed at Infolab is ProDA (for Progressive Data Analysis). This system has been used by Chevron and NASA to store and access large multidimensional data streams. These datasets are similar in nature to the datasets acquired in this proposal. ProDA and its underlying research activities have been receiving funding from NSF, Microsoft, Chevron and NASA/JPL.A press release by Microsoft Research praising the ProDA system can be found at: http://infolab.usc.edu/News/PDF_FINAL_PRODA.pdf

Other areas of research include traffic data analysis, spatial databases, geospatial information integration, location privacy, spatial data outsourcing, and sensor networks.

Dr. Shahabi is the recipient of the NSF CAREER award as well as the U.S. Presidential Early Career Awards for Scientists and Engineers (PECASE).

I am an Assistant Professor in the computer graphics group at Carnegie Mellon University. I was one of the creators of Foldit, the computer game where users contribute to science by folding proteins. I also pursue research in the simulation and animation of very high-dimensional nonlinear phenomena like animal morphology, human motion, and large fluid systems. One thread of my research addresses the complexity of such systems by developing model reduction tools that generate compact representations. A complementary thread seeks to control such systems, which means learning to set inputs to produce desired effects. While I seek theoretical advances, I am also deeply interested in the implications for science and engineering of these techniques, from fluid dynamics to laying down a joint cognitive and biomechanical basis for animal motion.

This year I am pleased to announce that HP Labs in Palo Alto, California is an option for CIFellows. To support this participation, I have posted this offer to mentor in the area of computational categorization in the area of color imaging.

Specifically, I am interested in the application of machine learning, statistical pattern recognition or natural computation techniques to a sizeable, diverse and growing collection of web-derived ground truthing databases for color imaging. This research thread is of fundamental significance given that “categorization of sensory inputs is the nexus between perception and cognition.”(1) The research thread also has basic applied implications, such as machine color naming or online tools derived from web-based visual experiments.(2)

Potential CIFellows should be multi-disciplinary researchers with an interest in computational categorization and topics such as:

* adversarial machine learning,
* mutual boostrapping,
* advanced color image processing,
* applied corpus linguistics,
* web-based software tools,

By way of additional background, I am a principal scientist in the Print Production Automation Lab and have been with HP for almost 15 years. I have had productive collaborations with students and researchers from around the world. Finally HP Labs in Palo Alto is a world-class industrial research laboratory in the center of silicon valley within easy driving distance to San Francisco, Napa Valley, Yosemite national and many other sites of interst.

(1) D.J. Freedman, M. Riesenhuber, T. Poggio, E.K. Miller, “Categorical Representation of Visual Stimuli in the Primate Prefrontal Cortex”, Science, v. 291, p. 315 (2001).

(2) N. Moroney, “Thousands of Online Observers is Just the Beginning”, HP Labs Technical Report HPL-2009-59, (2009).

See research web page

http://faculty-staff.ou.edu/P/Dimitrios.V.Papavassiliou-1/

My areas of research are computer graphics, computer vision, and computational imaging. Specifically, I research computational techniques that can help us author more expressive imagery using digital cameras. I publish primarily at SIGGRAPH.

My primary research area is in the multimedia database management system. Research interests include graph-based multimedia data modeling, indexing structure, query processing, content/semantic based multimedia retrieval and summary. My research works utilize various techniques for multimedia data mining, video processing, multimedia ontology, medical imaging, and kinematic analysis.

Our research group was established in 1999 by Prof. Han-Wei Shen. Our main research focus is on Visualization and Computer Graphics. Specifically we are conducting cutting edge research in data analysis and visualization for scientific, medical, and information technology applications. To date, the Gravity research group has graduated seven Ph.D.s and two Masters. Our graduates have been placed in various academic institutions, industry, and national laboratories. Our research has been funded by NSF, DOE, NIH, and NASA.

My research interests are broadly in artificial intelligence, decision-making, and machine learning. My current research involves the study of algorithms for discovering new representations from data and prior knowledge.

Many successful intelligent systems over the past 50 years are dependent on a carefully handcoded representation provided by a human expert. A major new challenge for AI research is automating representation discovery by developing algorthms for automatically constructing features or basis functions that reflect the nonlinear geometry of a data or state space.

My current research into representation discovery builds on harmonic analysis, a subfield of mathematics where spatial and temporal data is transformed into a frequency oriented coordinate system. I am exploring both global Fourier techniques based on diagonalization principles, such as eigenvector representations (e.g. Laplacian eigenfunctions), as well as multiscale representations, such as diffusion wavelet analysis. I am also exploring group representation theory for building compact basis functions on large “symmetric” spaces.

My students and I are exploring a wide spectrum of topics in representation discovery, from the design of new algorithms for learning and decision-making, as well as applications in 3D computer graphics, information retrieval, Markov decision processes and reinforcement learning, natural language processing, robot learning, and transfer learning.

The research goals of USC’s Computational Emotion Group and ICT’s Social Simulation Lab are to study and model human behavior. Our work on human behavior modeling includes computational models of human emotion, decision-making, theory of mind reasoning and nonverbal behavior.

The Computational Emotion Group also studies the application of these models to the design of virtual humans, life-like facsimiles of humans that look and act like humans and can interact with humans using spoken dialog much as humans interact with each other. Current efforts of the Emotion Group include validating the EMA model of emotion against human data, machine learning approaches to deriving models of nonverbal behavior, the role of physiological processes in emotion and the animation of expressive behavior within the SmartBody virtual human animation system.

The Social Simulation Lab works on modeling and simulating human social interaction, at a more aggregate level (groups, cities, etc.). Ongoing research includes tractable approaches to modeling of theory of mind reasoning and decision-theoretic, descriptive models of human-like decision-making. We also study automated and data-driven approaches to validate and facilitate authoring of large scale social simulations.