An important theme in my work is the development of data management technology to address new problems introduced by emerging applications, including the Web and scientific applications. My recent research has focused on two main topics: scientific data management and Web mining. I work both on the development of core data management techniques and on applications that use these techniques. This gives me the opportunity to work with scientists in cool areas such as Environmental Sciences, Biology, Physics, and Graphics/Visualization. Some specific problems I am interested in include: provenance management (storage, querying, visualization), graph indexing and mining; infrastructure to support reproducible papers; large-scale (and Web) information integration; focused Web crawling; hidden Web crawling; data mining.

My my main interests involve social systems informatics. The this interdisciplinary program in Social Systems Informatics is directed towards advancing and disseminating computational approaches involving the measurement, modeling, and testing of how social interactions affect a broad range of human outcomes. These social interactions range from face-to-face, media communications, or other social actions and are often massive in size. Key outcomes involve major characteristics of our lives related to education, health, work, and play; attitudes, behaviors, performances in these areas are not simply products of individual factors but are fundamentally shaped by the social contexts and interactions in which people live. Computationally, social systems informatics relies on methods in system science, including social network analysis, agent based modeling, data mining, intelligent data analysis, and biostatistical methods, to study how individual and social system interactions affect health and other outcomes.

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.

Arun Iyengar does research and development into Web performance, distributed and parallel computing, as well as fault-tolerant computing and high availability at IBM’s T.J. Watson Research Center. His work in caching and load balancing is widely deployed on the Web. His technical work has been incorporated into products by several companies (by IBM and other companies) as well as into commonly used open source software.

Arun is an IEEE Fellow, has received several best paper awards, has received the IFIP Silver Core Award, and has earned the distinction of IBM Master Inventor. He is Co-Editor-in-Chief of the ACM Transactions on the Web, Chair of IFIP Working Group 6.4 on Internet Applications Engineering, a Founding Executive Committee Member and former Chair of the IEEE Computer Society’s Technical Committee on the Internet, and the IEEE Computer Society’s Representative to IFIP Technical Committee 6 on Communication Systems.

Arun has PhD and master’s degrees in Computer Science from the Massachusetts Institute of Technology where he was a National Science Foundation Graduate Fellow and a bachelor’s degree Summa Cum Laude with distinction in Chemistry (with a minor in Computer Science) from the University of Pennsylvania.

The Computer Supported Collaboration (CSC) Laboratory conducts research to inform the design of information systems for collaboration.

We investigate the development and use of information infrastructures in science and engineering, computer supported cooperative work (CSCW), and computer supported cooperative leisure. Recent projects have explored these themes in domains like environmental microbiology, functional brain imaging, museum exhibit design, and online calendar sharing. Previous exposure to CSCW research and some interest in biological and physical science is desirable for this position.

We use primarily qualitative social science research methods, often conducting interviews or observational fieldwork in places of work and leisure to holistically investigate information systems as evolving social and technical entities. Candidates should have familiarity with literature on to the topics of cyberinfrastructure or boundary objects and independent research experience using qualitative social science methods.

The focus of my research is information seeking and behavior that goes beyond individuals. Specifically, I am interested in information seeking activities that take place in groups/collaboration, or in online communities using various social media or networking services. My primary objective for studying these topics is to develop a theoretical framework for investigating information seeking and behavior involving multiple actors/participants and their interactions that is lacking for collaborative and social situations. Another related objective is to build a model for evaluating and explaining various aspects of information propagation through interactions among people whether they are working in close collaboration or connected through open online communities. This direction leads to investigations related to “information as a thing” and construction of online communities around information seeking and sharing. A by-product of such investigations would be their applications to analyzing various socio-political issues through social media usage and user participation.

At Rutgers, I run a research group on information seeking (http://www.infoseeking.org), with subgroups on collaborative information seeking (http://collab.infoseeking.org) and social information seeking (http://social.infoseeking.org). I’m also associated with the social media group (http://sm.rutgers.edu) and the NLP group (http://salts.rutgers.edu).

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/.

Anything to do with information. Have been involved in > 110 research grants, giving hundreds of papers and presentations, initially on information retrieval, including computational linguistics and reasoning under uncertainty. Also worked on multimedia, educational technologies, digital repositories, and extending services related to electronic theses and dissertations (including analyzing references and classifying works and parts, see www.ndltd.org), with machine learning. One of the founders of the digital libraries area, developing formal foundations, prototype systems, user studies especially on interfaces and information visualization, and applications including to archaeology, physics, and education. Leading the Crisis, Tragedy, and Recovery network (www.ctrnet.net), including working with ontologies, focus groups, twitter, focused crawling, filtering.

My work lies broadly at the intersection of HCI, interactive IR and cognitive psychology. The overarching goal is to use the implicit user data in personalization of interaction with information systems.

Current areas of focus include application of cognitive psychology and neuroscience to information seeking (IR and web navigation). The approach is to use rich user interaction logs, eye-tracking and psycho-physiological signals (such as EEG, GSR) to infer user’s states (e.g., cognitive load, affective states) and user’s reaction to information (e.g., decisions).

There are opportunities to :
* study information seeking behavior in novel ways;
* work on creating novel search and navigation interfaces;
* work with large psycho-physiological data sets and apply signal processing and machine learning techniques.

Worth noting: My university is just about an hour away from New York City.

The primary focus of our lab is understanding the work practices of IT professionals to inform the design of improved computational support. As such we have engaged a wide range of domains including healthcare, aerospace, network security, public education and online community management. We take a sociotechnical perspective and engage the full life cycle of research projects, from ethnographically-informed fieldwork through systems development, deployment and evaluation. We are currently engaged with a multi-faceted empirical investigation of the processes virtual organizing, especially with regard to virtual worlds and alternate reality games.

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.

My laboratory is interested in key challenges in Systems Biology and Medicine. Our larger objective is to decipher biological mechanisms, reconstruct networks, predict phenotypes and build quantitative systems models. Towards this end we continue to develop computational and some experimental methods for integrative analysis of multiple types of biological data and for reconstruction of biochemical networks. It is increasingly become clear that time series data in cellular biology and longitudinal data in mammalian in vivo biology are essential for understanding complex phenotypes and we are developing methods to analyze these data. And most importantly, I subscribe to the view that context-free biology is content-free biology!

My work centers around the study of machine learning and inference methods to facilitate natural language understanding.

We are pursuing both fundamental questions in learning and inference and how they interact — most recently: Structure Learning, Constraints Driven Learning, Integer Linear Programming formulations for NLP, Constrained Conditional Models — and a range of natural language processing (NLP) problems — moving in the direction of semantics and natural language understanding, as well as ESL and Psycholinguistically motivated language acquisition.

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.

We’re developing general systems for scalable Bayesian inference. We’re exploring a mixture of sampling and point estimation strategies including Hamiltonian Monte Carlo and variational methods.

General purpose tools under development include a general posterior sampler (along the line of BUGS), multiple imputation for missing data, and post-stratification for prediction.

We’re particularly interested in multilevel regression and factor models, with applications to prediction problems in epidemiology, climate modeling, and social science. I’m also interested in large scale probabilistic RNA alignment, expression estimation and pathway modeling.

Other team members include Andrew Gelman, Ben Goodrich, Matt Hoffman, and Michael Malecki.

I am interested in theoretical modeling and experimental understanding of multi-instrument measurement systems that deal with multi-dimensional multi-variate data, as well as in automation and design of computer-assisted systems that deal with large volumes and computational intensive processing of heterogeneous data. The main goal of my research and development is to automate information processing of repetitive, laborious and tedious analysis tasks, to scale computations of automated tasks, and to build decision-making systems that operate in automated or semi-automated mode.

Post-doctoral students and graduate students interested in working with me would be focusing on research and development of new algorithms, especially those that automate visual inspections and understanding scenes from spectral imagery, and those that scale with large volumes. The research of algorithms is based on deep understanding and modeling of instruments generating the images, the phenomena represented by images, and the processes applied to raw images. The contributions of the joint work would be in building bridges from raw data to information and to knowledge, as well as in understanding computational and algorithmic challenges for automated data-centric operations.

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.

My recent research interests focus on crowdsourcing and on mining user-generated content on the Internet. I am especially interested in combining human intelligence with machine learning, and in building systems that provide quality assurance in crowdsourcing applications.

Research in my group deals with various aspects of the Web and increasingly the social Web, or Web 2.0. This includes analyzing and modeling the diffusion of information in social media, and building better applications for Web search, recommendation, navigation, and annotation. We study crowdsourcing applications and their incentives, such as games and services with purpose, to leverage the wisdom of the crowds for social computing tasks. Finally we study collaborative and distributed approaches to make the social Web less susceptible to abuses such as spam and manipulation.

At the technology, open organizing, & learning sciences laboratory (toolsLAB) we examine how digital tools afford new ways of reorganizing knowledge sharing and knowledge building and impact learning and work practices. Through a fundamental understanding of the change occurring within the workplace and learning envirnments, we aim to design tools and technologies that can support creativity and innovation. Our current projects are looking at the use of Tablet PCs in engineering design, the use Wiki-based platforms for student advising, social network analysis of research collaborations, and learning practices in open source communities. Another major thrust of our lab is examination of communication and collaboration in virtual and globally distributed teams. This position will provide experience with field studies, technology development, and an opportunity to shape the education of future engineers.

Additional areas of interest: ICT for development, design for social empowerment.

Crowdsourcing, human computation, and collective intelligence are having profound implications for computing, posing many tantalizing research questions. Not only does the topic provide fertile land for research, it is also a topic where the time frame of a CI Fellowship can permit results with the significance and recognition that can help propel a career forward.

As one example direction, we now face micro-labor markets such as Amazon Mechanical Turk that allow us to write algorithms whose “subroutines” are people performing tasks at pennies a shot. How do we program this kind of “social computer”? What are the crowdsourcing analogs to the programming languages and IDEs that we rely on in traditional programming? I’m particularly (but not solely) interested in the role that machine learning can play here. For example, what’s the right way to do classification learning for problems that no longer need to assume that obtaining human labels on data is excessively costly, where people are now instead cheap albeit unreliable or differently motivated? This area not only poses new questions but also offers answers of relevance not just to the computational but also the behavioral sciences.

usability, creativity, design methods, participatory design, design rationale, community networks, mobile community networks, online community, virtual organizations, common ground, social capital, awareness, activity awareness, case-based learning,

My primary research interest is in the study of large-scale complex networks (such as technological, informational, and social networks) in order to build predictive models of such systems. Problems of interest span from knowledge representation issues to network classification and clustering, to attribution of macro-level behavior to micro-level elements. For more information, visit http://eliassi.org/pubs.html.

Broad research interest in information theory and systems and the theory biological networks. Specifically, in the area of information theory and systems, exploring data representation schemes for multi-level flash memory as well as for novel substrates. In the area of biological networks, studying the computational power of stochastic chemical reaction networks and how they relate to more conventional models of computation. In addition, developing mathematical abstractions to enable the analysis of stochastic biological networks; specifically, generalizing the notion of logic design to probabilistic logic design we consider relay circuits where deterministic switches are replaced by probabilistic switches.

The goal of our research is to understand the impact that children can have on the design of new technologies and to understand the impact of that these technologies can have on children’s learning experiences. Our interdisciplinary lab of computer and information scientists collaborate with a variety of partners (e.g., The U.S. National Park Service, UNICEF, Sesame Workshop, The Mongolian Ministry of Education through the World Bank, Microsoft, Discovery Communications, etc.) to develop new educational technologies that range from digital libraries to mobile storytelling experiences.

Our newest work focuses on developing mobile technologies for the “iChild.” The iChild is international, expects to be interactive, is information-active, and intensively social. The mobile technologies we are creating are not for children to consume information, but to create and share information. We are currently working with Nickelodeon Inc., Carnegie Hall, and the U.S. National Park Service to create new mobile technologies that integrate the physical and virtual experiences to tell stories, explore the physical world, and enhance the traditional web experience. Our mobile app building is oniPhones and Android phones.

Information assurance, cybersecurity, social networking

My interests are in the application of information and computing technology in organizations, particularly unconventional applications in the service sector. My work has mostly been in applying both quantitative and qualitative modeling to various aspects of an organization, so that we can gain insights from the large volume of data that we are now able to collect. Possible topics of joint investigation include mathematical/empirical models for business performance scorecards, models for organization design, numerical solutions to Markov models.

George J. Pappas is the Joseph Moore Professor in the Departments of ESE and CIS at the University of Pennsylvania. He is member and former director of the GRASP lab. He currently serves as the Deputy Dean in the School of Engineering and Applied Science. His research focuses on control theory, robotics, hybrid systems, embedded systems, hierarchical and distributed control systems, with applications to unmanned aerial vehicles, flight management systems, distributed robotics, and biomolecular networks. He has published extensively, is a Fellow of IEEE, and has received the National Science Foundation Presidential Early Career Award for Science and Engineering (PECASE).

This is a general invitation on behalf of the Information Systems Research Community at NJIT. We have a large variety of NSF-funded and other research projects in HCC and III (human-centered computing / information integration & informatics) topics. Details are on individual faculty pages, accessible through http://is.njit.edu/people/faculty.php – see the research keywords listed here and on the faculty pages.

I’m interested in advancing the state of the art in developing environments that combine data management, computation, and collaboration to reduce the barriers to use of large-scale resources and to enable cross-disciplinary research and industrial design integrating heterogeneous data and models. I am looking for individuals interested in research coupled with application in support of scientific research, engineering design, or education; we have a number of projects involving collaborations with domain researchers in science, engineering, and the humanties that could provide concrete requirements and interested user communities for individual research topics.

Software Engineering, in particular, Requirements Engineering, Software Traceability, Software Reliability, Empirical Software Engineering, Software Evolution and Maintenance, Software Architecture and Design, Software Comprehension, Software Project Management, Study of Software Defects.

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.

Attacks and counter measures in newly emerging cognitive radio networks;
Protocols for cognitive radio networking and performance analysis;
Psycholinguistic data mining from text data for deception detection;
Video steganography and steganalysis

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 is in Human Centered Computing. The goal of my research is to design, implement and evaluate innovative solutions to real world problems. My research integrates people, culture and technology to address societal issues. In general, Human Centered Computing research is highly interdisciplinary and applied. My areas of specialization within HCC are Natural Interactive Systems, Advanced Learning Technologies/Intelligent Tutoring Systems, Ethnocomputing/Culturally Aware Computing, Human-Computer Interaction, Databases and Data Mining.

In Natural Interactive Systems (NIS), I am interested in creating user interfaces where the user interacts with the system using speech or multimodality. I am researching design, implementation and the evaluation of naturally interactive systems. One of my research projects in NIS is called Prime III. Prime III is a secure, multimodal electronic voting system. Prime III provides an easy to use multimodal user interface that allows greater participation in the electoral process. Voters that can’t read, hear, have visual impairments or physical impairments, can still vote using Prime III.

In Advanced Learning Technologies/Intelligent Tutoring Systems, my research aims to create and study applications that employ intelligent strategies that personalize instruction. In some implementations, this involves the use of spoken language systems and Animated Pedagogical Agents (APAs). I am researching the use and impact of culturally relevant environments that use culture in the education or training environment. This is a form of Ethnocomputing or culturally releveant computing. In my latest research efforts, I am researching game-like interfaces that provide naturally interactive instruction using animation, artificial intelligence, and speech. An example of this work can be seen at http://www.aadmlss.com.

In Ethnocomputing or Culturally Aware Computing, I am investigating the use and impact of culture in computing. Our research suggests that culture can be used to increase interest, user satisfaction and ease of use in computing applications. I am also working on information technology workforce issues. Specifically, I am investigating pedagogies and programs that broaden participation in computing for people in underrepresented groups. I am studying effective practices that help recruit, retain and graduate people from underrepresented groups in Science, Technology, Engineering and Mathematics (STEM).

In Databases and Data Mining, I am investigating data mining for human centered applications, e.g. applications where the data represents people, and tools that answer complex questions from business intelligence, education, and society in general. For example, I use clustering algorithms to process admissions applications in order to increase holistic diversity. This tool is called Applications Quest, http://www.ApplicationsQuest.com. I am also interested in educational data mining.

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.

For lots of applications like speech recognition and machine translation, it is recommended that the training set should be similar to the test set. But real life isn’t like that. What can we do when the test set doesn’t match the training set? In many practical settings the test document will be unique in lots of unanticipated ways (author, topic, speaker, genre). We need methods that are more robust to mismatches between the training data and the test data. We also need methods that can adapt to the unique properties of the test document.

I am interested in designing, developing and analyzing systems that can improve the accessibility of information for a wide spectrum of users, driven by the passion of making information systems usable. My current research towards this direction involves a number of disciplines, most notably databases, natural language processing, human-computer interaction and machine learning.

You can learn more about me from my website: http://www.almaden.ibm.com/cs/people/yunyaoli/index.html

Online Communities:
How can one structure early experiences in online communities to lead new members to become committed and contribute?

Social impact of computing:
What kinds of social media use by what kind of people influence their psychological well-being and their social relationships.

Virtual organizations:
Groups are inherently different from individuals performing the same task because of a need to coordinate. How can one organize work online to improve coordination?

Health support groups:
How do people generate social support in online support groups and what is its impact?

My research interests lie in data mining, high performance computing, and architecture-conscious algorithms. I am especially interested in designing algorithms and systems support to process and analyze large data sets, while ensuring efficient utilization of modern and emerging computer architectures.

Mobile technology for resource-constrained environments. Open source software to help organizations build community-based grassroots information services. Open Data Kit (ODK) is our latest software now in use on 6 continents in areas as diverse as public health and human rights monitoring. We build systems that are easy to use, extend, and maintain from both commercial and custom components. Our group publishes in development technology conferences (ICTD and DEV), mobile systems (HotMobile, Mobisys), and HCI (CHI).

ANDREA: I am a scholar of Public Informatics. This is a term I have coined to best situate my work at the intersection of people, information and technology in a public setting. My work in Public Informatics has contributed to the understanding of the patterns, behaviors and barriers to coordination through technologies among public institutions and contributed to addressing some of the most critical social problems facing the world today, such as humanitarian relief, emergency response, and social and economic development.

DISTAER RESPONSE COLLABOATION: In 2007 I received a grant from the US National Science Foundation to study collaboration and decision-making through technologies among humanitarian relief organizations. This initial seed has spurred my research team to eventually gain three additional grants, and to published five articles and fourteen conference papers from this research. My early findings of this research point to unique network structures and barriers that exist in this form of technical coordination in this sector at various levels of participating organizations. It is in these collaboration efforts that I find global collaboration processes, projects and challenges that are both unique in their context and setting and similar to other IT collaboration efforts more universally. Within this context, information systems collaboration is itself a goal, but it also frequently serves a supporting role in fostering collaboration in other units by promoting enhanced information sharing.
The question that I seek to answer in all of these cases is in what form would technologies have to take to enable fruitful collaboration and information sharing among organizations to improve the delivery of emergency services and goods immediately post disaster.

MICROBLOGGING DISASTER RELIEF: Much has been written concerning the value of using messaging and microblogging data from crowds of non-professional participants during disasters. Often referred to as microblogging, the practice of average citizens reporting on activities “on-the-ground” during a disaster is seen as increasingly valuable. Data produced through microblogging is seen as ubiquitous, rapid and accessible and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. However, despite the evidence of strong value to those experiencing the disaster and those seeking information concerning the disaster, there has been very little uptake of message data by large-scale, international humanitarian relief organizations. The real-time message data being contributed by bystanders and those affected by a disaster has been deemed as unverifiable and untrustworthy, and it has not been incorporated into established mechanisms for organizational decision-making. Due to the perceived lack of authentication, large-scale responders have been reluctant to incorporate microblogged data into the process of assessing a disaster situation and the subsequent decision-making process to send aid workers and supplies to disaster locations. Committing to the mobilization of valuable and time sensitive relief supplies and personnel, based on what may turn out be illegitimate claims, has been perceived to be too great a risk. We propose three socio-technical solutions to surpassing adoption bottlenecks, namely bounded microblogging, data as contextual data, and/or use of computational solutions.

I study motivations and incentives in social media. Specifically, I look at why people contribute to social media systems, and how the underlying technology can influences those motivations and the resulting behavior. I have a major NSF-funded project studying crowdfunding websites (like Kickstarter.com), trying to understand why people contribute, and how we can better design such websites so that people participate. I’m also involved in studying why people make computer security related decisions, looking in particular at their mental models of what threats exist.

My research focuses on enhancing the security and performance of computer systems in practical situations. My work targets problems that impact millions of users or mitigates issues with emerging technologies. I build systems and, whenever appropriate, use extensive live deployments to validate their practicality in real settings. Conference and journal publications are an important aspect of impact. But in many cases much more can be done. I believe one should validate and demonstrate the applicability of the research to real world problems. This means that much of my work spans multiple fields, primarily systems and security.

I am interested in mentoring a bright and hard working post doc. My work tends to be quite broad, so the area of work can vary according to the post doc’s interests.

We conduct cognitive science and artificial intelligence research into computational design and creativity. The goals of this research are to model human creativity in practical tasks such as conceptual design of complex systems and to develop interactive tools for aiding people in creative tasks. Our research posits analogical reasoning, visual reasoning, and meta-reasoning as fundamental processes of creativity. Computer-aided design and computer-supported science learning provide two real-world domains for exploring analogical, visual and meta-reasoning. Current projects explore analogical reasoning in biologically inspired engineering design, visual reasoning on intelligence tests, meta-reasoning in game-playing agents, and learning about ecological and biological systems in science education.

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

In recent work, we have developed network coding techniques that allow us to exploit fundamental wireless properties to improve network performance. Traditionally, these properties )broadcast, interference, variable channels etc) have been considered to be hindrances, instead we show via analyses and implementation how they can be harnessed to significantly network throughput. Currently, we are working on designing new wireless systems that automatically adapt to channel and network conditions, hardware and software substrates that allow us to virtualize wireless networks, ultra low power and wideband cognitive radio systems, and systems for distributed mobile sensing.

Deborah McGuinness is the Tetherless World Constellation (tw.rpi.edu) Chair and professor of computer science and cognitive science at Rensselaer Polytechnic Institute. She has also just been named the director of the new Web Science Research Center at RPI, Prior to joining RPI in 2007, she led the knowledge systems laboratory (ksl.stanford.edu) at Stanford University and prior to that, was part of the AI Research Department at Bell Labs / AT&T Labs.
She co-directs the Tetherless World along with Jim Hendler and Peter Fox.

Her current interests center around making the next generation web more usable and useful. Projects focus on designing semantic tools and applications that help access, integrate, understand, and use data. Current projects include emphasis on semantic technologies, knowledge provenance, privacy, policy, and workflow transparency, semantic eScience, linked open data, trust, social networking, and collaboration technologies, ontology evolution environments, and ethical, policy, and social aspects of web use and usability.

Our group is mainly interested in the research and development of VIEW, a well-known scientific workflow management system in the field. Our next thrust is to develop VIEW on top of the Cloud.

Most of our PhD graduates found faculty positions in other universities.
Therefore, this is an ideal environment for people who like to pursue an academic career.

Resource allocation in wireless networks, quality of information, mobility models, network security

Incorporating higher-levels of knowledge for speaker recognition;
increasing robustness of speaker recognition in unconstrained situations such as far-field;
investigating robust and rich audio exploration, extracting rich meta information beyond lexical information;
multi-model people recognition, integration of multiple human biometric modalities

Communication Networks, Network Economics, Game Theory, Statistical Learning, Networked Control Systems

My recent research efforts have focused on energy-efficient data management systems, query processing on new processor and storage technologies, main-memory transaction processing, and column-oriented databases (see my web page for more details: http://nms.csail.mit.edu/~stavros/).

In addition to continuing pursuing research directions in those topics, I am also interested in distributed transactional stores on main memory and/or flash, high performance data analytics on Hadoop clusters, and hardware/software co-design for energy-efficient data centers.

Parallel computing

Technology is rapidly changing the nature of work as we know it. These changes have made possible a variety of new organizational forms but increased the demands on human attention.



We are interested in understanding and optimizing allocation of attention in networked organizations. Our research includes: qualitative study of attention and coordination in networked knowledge work, experiments on task and social influences on direction of attention, quantitative analysis of log data on communication and task-switching behaviors in information centric work settings, and development of novel tools for self-regulation, productivity support, communication management, and coordination in creative work.

IT economics, strategy, and policy issues, including economics of network architectures (e.g., incentive-centered design, industry structure, competition and innovation policy, clean-slate design); economics of information security (e.g., rational behavior, interdependent security, insurance versus protection, risk management); peer-to-peer (p2p) incentive mechanisms and business models; information and communication technologies and development (ICTD).

Very large parallel databases on cloud computing platforms. Data integration of genomic databases. query processing on energy constrained sensors. Efficient algorithms for RDF statistics collection.

I am interested in the use of digital technologies, especially social technologies, by citizens to participate in civic and government activities including voting, activism, and political discourse. I am also interested in understanding voters’ strategies for searching information about candidates and issues and how various types of digital information influence the process of voter decision making. I combine empirical research methods in the usability lab and field with design research on novel interfaces and search systems.

My research interests and expertise are in how to build dependable distributed systems. I approach this problem from a systems perspective with liberal adaptation of results from the analytical side. The problems that motivate our research are why do distributed systems break when subjected to natural errors or man-made errors (malicious or operational). Knowing this, how can we build runtime mechanisms that will make it possible to prevent the errors from becoming user-visible failures. The work involves systems building, systems breaking, conducting rigorous experiments, and learning insights about real systems from these experiments. Specific applications so far have come from: grid systems, large-scale parallel applications, Voice over IP (VoIP) systems, web services, embedded wireless networks, and wireless sensor-actuator systems.

Dr. Stephen C. Hirtle directs the Spatial Information Research Group at the University of Pittsburgh, which conducts research on the structure of cognitive maps, navigation in real and virtual environments, and models for spatial cognition. In particular, our lab is interested in the role of semantic concepts, such as landmarks, neighborhoods, and familiarity, on navigation behavior and their role in designing navigational aids.

Interpretation of human activities using wireless sensor networks and their applications to elder monitoring, security and sensing.

Cyber-Physical Systems for energy management in next-generation intelligent buildings.

Sensor network architectures for the above mentioned systems.

My recent interests have been broadly interdisciplinary, with a particular interest in ways in which computing technology changes the ways we interact and work. Recent projects have included work with humanists in areas such as textual criticism but ranging to affiliated areas such as nautical archaeology and art history. A current project is examining the use of visual materials by scholars in a wide range of disciplines. In more traditional digital libraries, I am involved in a multi-institutional project that is bringing a computing portal to the National Science Digital Library. These research interests are reflected in my other activities, which include key roles in the international digital libraries research community; most recently program chair of the 2009 ACM/IEEE-CS Joint Conference on Digital Libraries.

The Social Analytics group at Carnegie Mellon’s iLab uses language analysis, text mining, information retrieval, and machine learning to investigate research questions related to how people use social media such as wikis and blogs. For example, how do opinion and knowledge diffuse through an organization? What is the relationship between physical network relationships and network relationships established through citation, replying and posting behaviors in blogs? and What are the alternative ways in which expertise can be characterized (e.g., bag of words) and how does the availability and access to such expertise determine outcomes, be they of organizations or of individuals? Research at the iLab is data-driven, and uses both data acquired from public Internet sites as well as unique datasets provided by corporate partners. This project is led by Jamie Callan and Ramayya Krishnan (http://www.heinz.cmu.edu/faculty-and-research/faculty-profiles/faculty-details/index.aspx?faculty_id=51).

My research falls into the areas of human-computer interaction (HCI), human-robot interaction (HRI), and computer-supported collaborative work (CSCW). I focus on designing social behavior for socially interactive systems, particularly humanlike robots and agents. This work combines technology development (including machine learning, computer vision, and natural language processing) with exploratory and experimental studies with people (including field and lab studies in which people interact with robots and agents). I am also interested in applications of robots and agents in education, collaboration, communication, disability, and autism therapy and collaborate with domain experts and researchers in these areas, mainly at the University of Wisconsin-Madison.

1. Designing efficient algorithms for networking, security, and database applications. My current focus is on efficient packet processing algorithms used on core Internet devices such as routers and IDS/IPSes.

2. Designing efficient privacy preserving protocols for practical problems. My current focus is on privacy and integrity preserving queries.

More information is on my homepage http://www.cse.msu.edu/~alexliu/

My research interests lie at the intersection of statistical signal processing and machine learning. I am interested in developing principled approaches, particularly for semi-supervised and active learning, that adaptively learn and harness the dependency structure between variables to enable efficient inference. Application areas of interest to me include networked systems and bioinformatics.

I am in the Information School and Human Centered Design & Engineering at the University of Washington, where I direct the Computing for Healthy Living & Learning (CHiLL) Lab. Our primary focus is in Human-Computer Interaction, Ubiquitous Computing, and Persuasive Technology. We are interested in designing, developing, and evaluating applications of computing technology that aim to promote healthy lifestyles and education. We design and develop mobile, persuasive, and collaborative technologies with the goal of helping with record-keeping, review, and motivation to pursue health and educational goals of individuals, families, and teachers. Current projects are focusing on health for families from diverse populations, therapists for children with autism, and individuals with sleep disorders. We are also researching related topics, such as effective persuasive computing and record-keeping techniques and improving the empathy in technologies for health. More information can be f ound at the CHiLL Lab website: http://depts.washington.edu/chilllab

Research at University of Dayton is aimed at signal/image/video processing and sensor designs that enable new capabilities in computer vision, camera designs, and surveillance. We emphasize on working from first principles, and there is a strong emphasis on math and statistics driven research. In particular, if you have keen interests in rigorous wavelets and/or Bayesian analysis, you will find many opportunities here.

Please check my webpages for details:
– academic.udayton.edu/hirakawa
– www.accidentalmark.com/research

One more point… Our research lab enjoys unusually high visibility among camera companies. Our work related to next generation camera processing pipelines have gained much recognition.

Personalization of interaction with information, and interaction in information systems, with special reference to interactive information retrieval. Studies of users in information systems, personal information management, and task-based information interaction. Evaluation of interactive information retrieval systems.

My research areas include (1) the development of information data management systems in support of clinical genotyping and personalized medicine, and (2) computational genomics in clinical and non-clinical organisms. In addition, I have experience in commercializing research findings in see-stage companies.

Tessa Lau is a Research Staff Manager at IBM Almaden Research Center, leading a group working on Smarter Web technologies. Dr. Lau’s research integrates techniques from artificial intelligence and human-computer interaction to build systems that enhance human productivity and creativity; areas of interest include end user programming, collaboration, and social software. Most recently, her team has developed and deployed the CoScripter system (http://coscripter.com), a platform for capturing, automating, and sharing tasks people do on the web.

Tessa has served on organizing and program committees for major AI and HCI conferences and journals. She also serves on the board of CRA-W, the CRA committee on
the status of women in computing research. Dr. Lau holds a PhD in Computer Science from the University of Washington. Outside of work, Tessa is an avid snowboarder, hiker/backpacker, gardener, and cook.

My research focuses on social information and social media, with excursions to mobile and multimedia applications. What can we learn from the activity of people on social media services about the world, about society, and about people? How can we improve these services as information platforms?

Join me if you want to develop an understanding of what make social computing platforms (like Facebook, Twitter, Flickr and others) work, and help build the future of these socio-technical systems.

Anura Jayasumana is a Professor of electrical and computer engineering at Colorado State University, where he also holds a joint appointment as Professor of computer science. He directs the Computer Networking Research Laboratory at CSU, and is a member of NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere. At CSU, he has supervised over 15 Ph.D. and 45 M.S. theses, and taught courses ranging from freshmen undergraduate courses to specialized graduate courses in Electrical and Computer Engineering. He has published over 200 research papers and a book. He has served as a consultant to numerous companies ranging from start-ups to Fortune 100 companies.

My current research interests focus on applying software engineering methods, techniques, and models to the design and development of intelligent, complex, adaptive, and autonomous multiagent systems. My research in this area is currently focused on building the tools and techniques necessary to design and build cooperative robotic systems, where the robots work autonomously, but cooperate as part of a team. I am also interested in building and developing hybrid intelligent systems that include humans, software agents, and mobile hardware agents.

1. Problems that arise in intercultural ccmputer-mediated communication and tools to improve intercultural collaboration
2. Collaborative data analysis
3. Social computing approaches to increasing sustainable behaviors
4. Multilingual computer-mediated communication

See www.cs.cmu.edu/~sfussell for more areas of interest

My research focuses on security and privacy issues in data management. I also work on semi-structured databases, data mining, and applications of data management techniques on networks.

My research concerns the development of information infrastructures, especially in climate science and other Earth system sciences. I lead the Monitoring, Modeling, and Memory Project, a 3-year comparative, qualitative study of several scientific cyberinfrastructure projects. We use ethnographic and interview techniques to explore work practices, software development, and issues involving data sharing and metadata. I’m also co-PI on a new project, “Scaling Up: Introducing Commodity Governance into Community Earth Science Models.” Our goal is to understand and promote well-functioning sociotechnical infrastructure for governance of community Earth system models (climate models). The project team includes a mix of social scientists, software developers, and Earth system scientists from the University of Michigan, the National Center for Atmospheric Research, the National Oceanic and Atmospheric Administration, the Geophysical Fluid Dynamics Laboratory (Princeton), and the University of Colorado at Boulder. The project is funded by the National Science Foundation. My most recent book is A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming (Cambridge: MIT Press, 2010).

Our research team (http://www.sis.pitt.edu/~paws/) is interested in all kinds of personalized (user-adaptive) Web systems including adaptive hypermedia, personalized search, and social Web personalization.

We are also looking for researchers interested in personalized E-Learning and in developing advanced tools to support teaching and learning information science and computer science. Our university and Pittsburgh in general is a host for many top researchers in this area (5 out of top 7 authors! http://academic.research.microsoft.com/CSDirectory/Author_category_23.htm )

natural language processing, natural language processing applications to real life problems including policy problems, information extraction, named-entity recognition, paraphrase recognition, text summarization, natural language processing evaluation, medical language processing, natural language processing on clinical patient records, medical informatics, biomedical informatics, data mining, information retrieval. Machine learning, supervised and semi-supervised approaches, expert systems. Innovative applications of the above.

I’m interested in algorithms and models for analyzing human language (computational linguistics) and applications of those techniques (natural language processing) to problems like machine translation, information retrieval, and digital libraries.

For example, with students and others here at UMass, I’ve been looking at improved retrieval models based on parsing and machine translation techniques; learning translation models from non-parallel text using topic modeling; and improved joint inference for morphology, syntax, and semantics by merging message passing and combinatorial optimization. In a seminar this spring, we explored distributed text-mining algorithms (using Hadoop’s MapReduce) on a collection of a million scanned books.

scalable search technology

Methods for harvesting the collective intelligence of groups of people in order to realize greater value from the interaction between users and information.

This includes collective intelligence, incentive design for accessing resources, social networks and their implications for information dissemination and collective attention.

I am also involved in creating new information services that dynamically respond to fluid content creation and consumption.

We are interested in understanding how biological organisms process information using complex biochemical networks and how such networks can be engineered to program cellular behavior. The focus of our research is the identification of systematic design rules for the de novo construction of biological control circuits with DNA and RNA components. Our approach integrates the design of molecular circuitry in the test tube and in the cell with the investigation of existing biological pathways like the microRNA pathway. Engineered circuits and circuit elements are being applied to problems in disease diagnostics and therapy.

Wireless Systems and Networks, Energy efficient algorithms and protocols, Resource management schemes, Medium Access Control, Cognitive Networking, Information Theory and Applications, cognition in network systems, distributed systems, stochastic control, mobile ad hoc networks, wireless mesh networks, cellular systems research. Emergency response networking, tele-medicince applications of networking.

cognitive mobile networks, machine learning in wireless networks, autonomous cognitive networking, distributed cognitive networking, cognitive wireless networks, cognitive radio, cognitive radio networks.

cooperative control, distributed algorithms, learning, optimization, nonlinear control, geometric control

My research interests include concurrent/distributed systems, reflective and adaptive middleware, multimedia systems and applications, middleware for mobile applications, formal reasoning of distributed systems.

At Google, Sergey works on developing data management solutions for web-scale data sets.

Cloud Services Composition

Advances in Cloud Computing have enabled service providers to offer business services in an economic way, and provided businesses customers incentives to access and outsource functions to cloud services. In most cases, a given cloud service covers only part of functions needed by a business, and therefore, businesses need to use a number of services. One major challenge to ensure seamless function of the business is the integration, composition and orchestration of these services. There has been extensive prior work on automated approaches for service integration and composition in the context of Web services. However, no industrial strength approach addressing or facilitating this problem has emerged.

In the context of a research project in HP Labs, we are developing technology to enable a service marketplace for service providers to offer services, and for service customers to find, compose and integrate services. We welcome a motivated, sharp and self-starter postdoctoral fellow with strong background in the theory and practice of service oriented computing, service compositions, business processes to work on the problem of composition of business services offered in the cloud. The candidate should be capable of formulating the research problem, proposing novel ideas and implementing ideas into prototypes and should demonstrate a strong publication record. HP Labs is located in Palo Alto, CA (SF Bay area) which offers a vibrating and active academic and industry environment.

Coding theory, distance geometry, combinatorics, cryptography

Topic: Industrial Strength Cloud Services Composition

Advances in Cloud Computing have enabled service providers to offer business services widely, and in a more economic way, and given businesses customers more incentives to access and outsource functions to cloud services. In most cases, a cloud service covers only part of functions needed by a business, and therefore, businesses need to use a number of services. One major challenge to ensure seamless function of the business is the integration, composition and orchestration of these services. There has been extensive prior work on automated approaches for service integration and composition in the context of Web services. However, no industrial strength approach addressing or facilitating this problem has emerged.

In the context of a research project in HP Labs, we aim at developing a service marketplace for service providers to offer cloud services, and for customers to find, compose and integrate services. We welcome a motivated, sharp and self-starter postdoctoral fellow with strong background in the theory and practice of software engineering, service oriented computing, service compositions, business processes to work on the problem of composition of business services offered in the cloud. The candidate should be capable of formulating the research problem, proposing novel ideas and implementing ideas into prototypes and should demonstrate a strong publication record. HP Labs is located in Palo Alto, CA (SF Bay area) which is a vibrating and active academic and industry environment.

• Studying adoption, uses, development, and implementation of innovative technologies, including search behaviors, digital libraries, lecture video, and specific educational technologies.
• User studies of web environments in which users implicitly learn concepts (implicitly: limited awareness that they are participating in a learning task)
• Understanding recruitment, retention, and advancement issues for under-represented groups in computing education and professional careers.
• Study of social aspects of computer science education (higher education).

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.

One of my recent research thrusts is on uncovering fundamental limits and algorithms for interactive and secure information processing in networks using tools from network information theory and communication complexity theory. In parallel I am also exploring information processing in visual networks using tools from statistical learning theory and multidimensional signal processing.

Jorge Cortés is an Associate Professor with the Department of Mechanical and Aerospace Engineering at the University of California, San Diego. He received the Licenciatura degree in mathematics from the Universidad de Zaragoza, Spain, in 1997, and the Ph.D. degree in engineering mathematics from the Universidad Carlos III de Madrid, Spain, in 2001. He held postdoctoral positions at the Systems, Signals and Control Department of the University of Twente in 2002 and at the Coordinated Science Laboratory of the University of Illinois at Urbana-Champaign from 2002 to 2004. He was an Assistant Professor with the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz from 2004 to 2007. He received a NSF CAREER award in 2006. He is the author of the book Geometric, Control and Numerical Aspects of Nonholonomic Systems (New York: Springer-Verlag, 2002) and the recipient of the 2006 Spanish Society of Applied Mathematics Young Researcher Prize. He currently serves in the Editorial Board of Systems and Control Letters and the European Journal of Control and in the Conference Editorial Board of the IEEE Control Systems Society.

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.