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 current research interests are in applying neuroergonomics to information search and information retrieval. The approach is to use rich interaction logs and neuro-physiological sensors (such as EEG) to infer user’s states (e.g., cognitive load) and user’s reaction to information. This requires applying signal processing and machine learning techniques. The overarching goal is to use the implicit user data in personalization of interaction with information systems.

Join me if you want to work on cutting edge applications, and help build the future of interactive systems.

Worth noting: We are just about an hour away from New York City.

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.

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

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).

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.

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.

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.

Online Communities

Community Lab project conducts research to understand what leads people to become committed and contribute to online communities and how to design these communities to be more successful.

Social impact of computing

I have been investigating the way people use the Internet in their daily lives and the effects it is having on their psychological well-being and on their social relationships.

Virtual organizations

Groups are inherently different from individuals performing the same task because of a need to coordinate. I have been looking at coordination in groups in both laboratory and field settings and in groups as diverse as research collaborations, managerial teams, military crews at NORAD, projects in Wikipedia and software development teams.

The Department of Computer Science at Trinity University is looking for a CIFellow who has a passion for teaching in an undergraduate institution and doing research with students. Any area in computing is welcome. Our Department has eight tenure-track or tenured faculty and three instructors. We provide excellent liberal arts and science education to a highly selective student body. During the CIFellow’s tenure, we will provide him/her with opportunities such as:
1) Teach a variety of introductory CS courses such as CSCI 1311 (Intro to Programming Logic) and/or CSCI 1320 (Principles of Algorithm Design – I).
2) Create new elective course(s) in the individual’s area of expertise.
3) Do active research with our talented students.
4) Create his/her own interdisciplinary First Year Seminar to enhance the Common Curriculum in the liberal arts and sciences at Trinity.

The CS Department currently has several NSF funded programs that can help the CIFellow to achieve his/her teaching and research goals in an excellent computing environment, such as REU Site (Research Experiences for Undergraduates) and MRI (Major Research Instrumentation). Our faculty collaborate significantly with other departments in research and teaching, such as Biology, Physics, Sociology, Neuroscience, Economics, and Communication. For example, the DIAS lab (Distributed Intelligent Agent Systems) is currently developing a multi-agent system in social environments. This multidisciplinary project involves Computer Science, Sociology, and Neuroscience. Because of this, AI and related areas are of particular interest to us.

Also, though it is not an active area of research in our Department, San Antonio is a major center for Information Assurance & Security (IAS). NSA and the military have major installations here; and it was just announced that the new Air Force Cyber Command Headquarters will be located in San Antonio. So there would be ample opportunities for IAS work locally as well as course development in our curriculum. These are just two examples; graduates in all areas of computing are welcome to apply.

The CS Department anticipates one to three tenure-track openings over the next few years, so it is possible that the postdoc position could become tenure-track at that time.

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.

I am currently working with colleagues on a cloud storage system and a big data system. The cloud storage system offers a key-value interface, global namespace (i.e., spans multiple data centers), flexible/tunable/degradable consistency (i.e., atomic consistency or eventual consistency), and efficient high-reliability (i.e., erasure coding across data centers with appropriate integrity mechanisms). The big data system is based on DataSeries, an efficient record format for streaming analytics.

There are a number of research projects that nicely complement these research projects. A post-doc interested in any of the following topics would be a great fit.

Cloud Storage
* What is the right programming model to exploit tunable/degradable consistency? The prototype we have built can degrade consistency (eventual instead of strong) in the face of failures to avoid becoming unavailable.
* Develop additional decentralized protocols for the prototype. Contributions can be made on the theory side or systems side. E.g., we need a protocol to robustly migrate data within a cloud, a protocol for scaling the metadata service around the globe, and others.
* Other ideas? We are broadly interested in cloud storage.

Big Data / Data Intensive Super Computing
* We have developed DataSeries, a high-performance record format that allows efficient streaming analytics. We are working on parallelizing this platform. We believe Parallel DataSeries will be able to outperform platforms such as MapReduce and Dryad for some specific workloads. There are many research avenues that could be pursued on this work.
* We have developed advanced erasure codes that may be useful in Big Data storage systems such as HDFS. I would like to work with a post-doc interested in implementing the codes, adding them to HDFS, and modifying the Hadoop scheduler to take advantage of the properties.
* Other ideas? We are broadly interested in big data.

My interests are in the application of information and computing technology in organizations, particularly service organizations. My work has mostly been in applying modeling to various aspects of an organization, such as business performance modeling, business value modeling, business process modeling, and business strategy modeling, so that we can gain insights from the large volume of data that we are now able to collect. Because service organizations are human driven, I am also interested in incorporating models of human performance into an organizational performance model.

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).

I enjoy working with very large corpora such as the Associated Press newswire (1 million words per week, which was considered big in the 1980s) and larger datasets such as telephone call detail and web logs (1-10 billion records per month). I have used these datasets to address problems in many areas of computational linguistics including: web search, language modeling, text analysis, spelling correction, word-sense disambiguation, terminology, translation, lexicography, compression, speech (recognition and synthesis), OCR, as well as applications that go well beyond computational linguistics such as revenue assurance. Since there is no data like more data, the crux is scale. Some scaling-up methods come from statistics (such as random projections and sketches) and others follow advances in computing architectures (such as cloud computing for embarrassingly distributed applications).

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 )

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,

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.

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 linked data and processes, such as Web traffic, as well as 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 social, collaborative, and distributed approaches to make the Web less susceptible to abuses such as spam and manipulation.

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.

My research is about X-informatics, where the X stands for document, hydro, geo, bio, medical image, or sensor. 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. One could view the contribution of my work in building bridges from raw data to information and to knowledge where the raw data come from ubiquitous multi-instrument measurement systems, as well as in understanding computational and algorithmic challenges for automated data-centric operations.

Image processing, computer vision, machine learning, information systems, information science, information retrieval, data mining, scientific/medical informatics 

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.

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.

We live in a world supported by large-scale IT system and network infrastructures. Many of these systems and networks are mission critical infrastructures and are engineered with tremendous complexity. My research interests focus on the following
topics across multiple disciplines in computer science:
1. Understanding the nature of system complexity by combining data analysis and mining with first principles.
2. Modeling system properties based on system and network knowledge.
3. Developing automated and intelligent solutions based on statistical inference, information theory, optimization, system and control theory.

Signal and image processing, pattern recognition, machine translation, natural language processing, source coding, digital libraries, language modeling

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 recent focus has been in statistical learning theory with applications to video analytics. I am also interested in fundamental decision theoretic aspects of information processing. I work at the intersection of information theory, statistical signal processing and systems theory.

PROJECT 1 is developing tooling and analysis techniques to enable
performance optimization in support of large commercial workloads in
the massive multicore era in which systems have hundreds or thousands
of processor cores. PROJECT 1 involves working with large-scale,
commercial applications with an eye towards dramatic improvements in
the performance of critical enterprise software deployments. This
project would benefit from postdocs whose research focuses on such
issues and who have experience with deployment and performance
analysis of multi-tier web applications, and more particularly
experience with Java programming, scripting (Javascript, Awk, Perl),
Java EE, database setup and tuning.

PROJECT 2 also focuses on future system architectures with large
numbers of multicore processors, but with a particular focus on how
such systems incorporate solid state disk / memory. PROJECT 2 is
analyzing how software can best take advantage of SSD and multicore
advances and what algorithms and architectures are best suited to
these systems — with approaches again focused on critical enterprise
workloads. This project would benefit from postdocs whose research
focuses on such issues and who have experience in storage systems and
disk modeling and with good knowledge of system benchmarking,
performance modelling, flash memory design, and interfaces such as
Sata and PCIe.

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.

I’m broadly interested in formal methods for building scalable and adaptive distributed systems. I have a particular interest in scheduling and resource allocation and in the application of control theory to construct solutions. Part of the application of control theory is a blending with software engineering so that practitioners can exploit these techniques.

Virtual Command and Control Systems for Emergencies, Gray literature recommender systems, Collaborative Scenario Creation Planning, Systems, Structural Modeling systems, Risk Modeling, Design of Collaborative Intelligence Systems, Group Decision Support Design

We are open to several research topics with potential applications in text document analysis. Some of the approaches we have considered are text analysis and natural language processing but we are exploring other related approaches as well. We would like to collaborate on creating compelling demonstrations for technology and publish results in research papers.

My current research activities are primarily in the area of Social Computing / Computer Supported Cooperative Work (CSCW). I am particularly interested in how various social computing system designs can both enable and constrain interpersonal and group interactions, and how the use of such systems can lead to the expansion of social ties/capital. Gaining such an understanding should result in improved methods for using computer technology to enhance people’s navigation of their social environment so that they can more easily engage in social activities of interest and coordinate with others. questions my research group is exploring include the design and potential impact of trustworthy ubiquitous social recommendation systems, either in the form of location-aware social matching systems or social-interaction-space recommendation systems.

Static timing analysis and statistical timing analysis of VLSI circuits, process variation analysis, at speed structural testing of digital circuits, circuit analysis, signal integrity, optimization of digital circuits, coupling noise analysis in digital circuits.

My research interests fall under the broad areas of high performance computing and large scale distributed systems. In particular, I am interested in abstractions, mechanisms and tools that allow system non-experts to harness the power of high-performance systems in scalable, efficient, reliable ways.

I collaborate with researchers at the The University of Wisconsin and the Lawrence Livermore National Laboratory, and collaborations with the Sandia National Laboratory and Los Alamos National Laboratory and are in the formative stages. These collaborations have placed us in an exclusive and privileged position to work with world class scientists on the largest systems in the world.

Autonomous Systems:
Currently, we are studying autonomous (aka self-adaptive, aka self-managing) overlay networks that support in-network data analyses and aggregation. Such networks use non-intrusive mechanisms for monitoring health, performance and offered loads, and use online performance modeling to reconfigure and optimize overlay topologies dynamically. We are also studying the broader applicability of the tree-based computational model (described below) for scientific applications, information analytics, data mining and enterprise computing.

Our other interests include alternatives to contemporary fault-tolerance mechanisms and new programming models and paradigms for future large scale systems.

Tree-based Overlay Networks:
At the University of Wisconsin with Bart Miller, I studied the use of hierarchical or tree-based overlay networks (TBONs) for efficient, reliable data communication and analyses for scalable tools and applications. A major outcome of this work is a scalable method for using the inherent data redundancies of certain (broad) classes of data aggregation computations to make them robust to node and process failures while avoiding the non-scalable overhead of explicit state replication (e.g. checkpoints). MRNet, the multicast/reduction network, is the TBON prototype we developed and continue to use to evaluate most of our TBON-related research.

Scalable Application Debugging:
STAT, the stack trace analysis tool is being developed as a collaboration of researchers from the Lawrence Livermore National Laboratory, The University of Wisconsin, and The University of New Mexico. STAT was developed to explore lightweight debugging techniques for extremely large (thousands and millions of processes) applications. STAT identifies processes equivalence classes, groups of processes exhibiting similar behavior, so that single class representatives can then be examined in depth with full-featured (less scalable) tools like TotalView or DDT. The initial version of STAT used only stack traces to determine process equivalence classes; our current work explores lightweight program analyses to classify application processes based on various notions of progress.

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.

A sociologist by training and computing sciences scholar whose work focuses on “human centered” information systems, Roxanne is currently Distinguished Professor Emerita, College of Computing Sciences, NJIT. Research interests include Group Support Systems (virtual teams and online communities), Evaluation research methods, Asynchronous Learning Networks, Emergency Response Information Systems, Pervasive Computing, and the applications and impacts of “social computing” (“Web 2.0”) systems.

My research mainly concerns the question: How can we help people make sense of collected information and create corresponding knowledge representations both individually and collaboratively? Especially, how can we help the creation of knowledge structures to formalize understanding and enable further decision-making and action? Answers to these questions at the individual level can shed light on the design of advanced information systems which facilitate users’ learning or investigative tasks: patients learn about treatments, researchers survey existing works on a topic, etc. Answers to these questions at the group or community level can lead to new designs of collaborative systems or mechanisms that will ultimately reduce the cost of knowledge production, thus enhancing the societal knowledge accumulation and sharing.

Several ongoing projects are:

1. Designing and implementing a web-based light-weight sensemaking workspace that facilitates literature search and comprehension.

2. Adopting social computing mechanisms to build ontology and related knowledge structures that support sensemaking of large amount unstructured information.

3. Studying the collaborative sensemaking process in social media

4. Community-based emergency response systems

The CompBio group at MIT is sitting with the Computer Science and Artificial Intelligence Lab, and is closely affiliated with the Broad Institute of MIT and Harvard. The lab consists of machine learning and algorithms graduates, with strong bacxkground and interest in biological systems. Our research spans three areas:

(1) Genome interpretation: We use comparative genomics and epigenomics datasets to discover all functional elements in the human genome, including protein-coding genes, large and small non-coding RNAs, enhancers, promoters, and regulatory motifs. We use multi-variate HMMs, Conditional Random Fields, SVMs, and related machine learning techniques for integrating diverse sources of data for each of these elements.

(2) Gene regulation: We study the sequence signals that are responsible for gene regulation during health, disease, and embryo development. This involves regulatory motif discovery using EM, Gibbs Sampling, word enumeration and refinement, discovering informative combinations of regulatory motifs, and motif grammars. We use these to construct pre- and post-transcriptional regulatory networks and study the dynamics of information flow through biological systems. Lastly, we have used probabilistic models to discover recurrent combinations of chromatin marks, or chromatin states, and used these to understand the epigenomic dynamics of multiple cell types in human, and various developmental stages in fly.

(3) Genome evolution: We study the mechanisms and principles of gene and genome evolution. We use phylogenomics techniques to infer gene duplication and loss patterns over thousands of gene families spanning dozens of genomes, by learning common properties of gene trees encoded as gene-rate and species-rate distribution priors, and topology priors based on birth-death processes. We have applied such methods to uncover a whole-genome duplication in yeast and vertebrates, to discover pathogenic genes in Candida species, and to uncover regions under positive and negative selection in the human genome.

In each of these areas, we work closely with experimental collaborators, both for the generation of large-scale genomic and epigenomic datasets, and for the directed validation of our computational predictions in a truly interdisciplinary environment.

Theoretical aspects of statistical inference, learning, security, and anonymous communications and networks. Information theoretic approaches to security and inference. Wireless networks. Distributed algorithms and optimizations.

My current research interests are in personalization, machine learning and data mining. Privacy is an important aspect of personalization as well. Previously I have worked in the areas of keyword bidding, behavioral economics, the economics of information, complex network theory, and my earliest work was in modeling the internet and web using tools from statistical physics and economics.

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

Develop a theory of smart systems. The adjective “smart” is applied to phones, the electric grid, buildings, cities, water supply, transportation and many other systems. This research explores the theoretical foundations underlying the variety of smart systems. The goal of the research is to apply computer science theory, operations research including statistics and machine learning, and control theory to develop a theory of smart systems, and to then apply the theory to design components of the smart grid and other cyber physical systems.

My research interests include theory and applications in the broad areas of data analysis, machine learning, information retrieval, signal processing, and system identification.

Currently, I’m working on…

* Development of theory and numerical algorithms for represenatation and analysis of high-dimensional data using randomization and diffusion techniques. This is a collaboration with a U.S. university.

* Development of network analysis algorithms/tools – including event detection and fault localization for real-world networks. We are investigating new models for cognitive computing for network management.

* Developement of theory and algorithms for information retrieval, filtering and classification for large document collections. This also includes cross-language retrieval.
One of the popular techniques for text IR – Latent Semantic Indexing (LSI) was invented here. URL: http://lsi.research.telcordia.com

Interested in probabilistic modeling and machine learning for web content analysis and data extraction. We expect to collaborate on using machine learning to extract, classify and understand text and multimedia contents in noisy web pages with high level of accuracy and robustness. We aim to construct running systems that embody research ideas and publish research papers.

Uncertain Data Streams.
The goal of this project is to design and develop a stream processing system that captures data uncertainty from data collection to query processing to final result generation. Such uncertain data stream processing is crucial to many real-world applications such as hazardous weather monitoring, object tracking and monitoring, and traffic monitoring. To achieve this goal, our project takes a principled approach grounded in probability and statistical theory to support uncertainty as a first-class citizen, and efficiently integrate this approach into high-volume stream processing. The project addresses technical challenges that arise in capturing uncertainty of raw data streams as well as in capturing uncertainty as data propagates through various query processing operators.

Complex Event Processing.
We study stream processing in the context of large-scale event-based systems that are gaining adoption in applications such as supply chain management, surveillance, network and application monitoring, and environmental monitoring. These systems create high volumes of events. End applications require these events to be filtered and correlated for complex pattern detection, aggregated on different temporal and geographic scales, and transformed to new events that reach a semantic level appropriate for the applications. We address issues involved in stream-based event processing ranging from the query language to computation complexity to fast implementation.

RFID Data Management.
Radio Frequency Identification (RFID) technology is gaining acceptance in an increasing number of applications for tracking and monitoring purposes. Despite its promise to provide unprecedented visibility in various domains, RFID technology presents numerous challenges, including incomplete and noisy data, lack of information about inter-object relationships, and high volumes. In this project, we design and develop an inference and query processing system over RFID streams. Our system offers accurate interpretation of incomplete and insufficient raw data by way of inferring locations of unobserved objects and inter-object relationships such as collocation and containment. It further performs complex query processing over the inferred data streams and scales such processing to multiple data centers and numerous objects.

Flash Databases.
Flash memories are in ubiquitous use for storage on sensor nodes and mobile devices, and are anticipated to soon play an important role in enterprise servers due to superior read performance and energy efficiency. However, flash memories have read and write characteristics fundamentally different from magnetic disks. In this project, we examine the benefits and challenges of building flash databases in several contexts. We first design a new sensor database on flash that supports power-constrained processing and multi-resolution storage. We further consider the use of flash in high-end computing such as in large data centers and address issues ranging from index construction to query processing to data aging.

The main focus of my research is designing systems that automatically analyze textual entities and their relationships in large document collections to 1. gain a better understanding about knowledge from text, and 2. apply the knowledge derived from text in various applications. E.g. predicting the validy of a medical research paper, and predicting student’s elearning performance from an online discussion message set.

Theory and algorithms for secure distributed networked storage, peer-to-peer video delivery without centralized infrastructure, wireless video camera networks, multi-user information theory with current focus on security.

Cryptographic protocols, secure multi-party computation, privacy-preserving computation, provable security, cryptographic functions and primitives, distributed consistency and decision problems, broadcast encryption, secure multicast, network security.

Min has a background in Text Mining, Bioinfomatics, Information Retrieval and Information Extraction. His recent work in Text Mining addresses automatic database selection, entity and relation extraction, context-sensitive text mining, algorithms that learn a person’s information needs from experience, intelligent analysis of large biomedical data corpus. Min is also interested in information and knowledge management in large organizations. Min is currently interested in applying Text Mining algorithms to Bioinformatics.

My interests are in the broad area of communications, information and coding theory, their applications to reliable and secure networking, with most recent activities in network and rateless coding.

My research interest has always been human-computer collaboration, in particular how to assist people in performing complex, knowledge-rich tasks that cannot be fully delegated to a computer program. This raises many research questions: How can people provide a computer with enough knowledge about the task domain and the user’s context to provide adequate assistance? How could we make it easier to provide the knowledge needed? How do we know that the computer understands the knowledge that it is given and can make effective use of it? How can a computer be proactive in asking questions about what it does not know and is needed to improve its behavior? All these questions span a number of research areas including knowledge acquisition (or knowledge capture), intelligent user interfaces, knowledge representation and reasoning, problem solving and decision making, and lie in the intersection of artificial intelligence, cognitive science, and human-computer interaction.

Many of my current projects focus on the knowledge-rich task of scientific data analyses that lead to discoveries. We cast complex analyses as computational workflows, which represent complex compositions of software components and the dataflow among them. Workflow systems can then support scientists by automating low-level aspects of the process, providing detailed records of each analysis and its products, and enabling rapid reuse of software compositions.

Other recent projects focus on information trust, knowledge collection from web volunteers, and learning complex procedures from human tutors.

My research is focused on stochastic approaches to various problems in mobile ad-hoc networks, delay/disruption tolerant networks, peer-to-peer networks, wireless sensor networks, to name a few. In the past the effort was on queueing theory, queueing network and its decomposition and congestion control for large networks. Recently I have been working on stochastic approach to peer-to-peer networks and mobile ad-hoc networks as an alternative to conventional fluid-based approach, mobility models and induced link-level dynamics and characterizations with applications to protocol design and mobility control for tactical networks in a heterogeneous, non-Poisson environment.

Signal processing and computational aspects of biosensors and biological networks.

I am interested in the problem of designing algorithms and interfaces to aid small teams to finding information addressing an explicitly-shared information need. The goal is to go beyond known-item and navigational search to support collaborative exploration and knowledge creation. In contrast to recommendation systems, this approach focuses on the implications of assumed rather than inferred shared information needs.

Open issues include the design of information retrieval interfaces that support awareness of team members’ present and past activities, searcher roles and co-ordination scenarios, algorithms that organize and rank the discovered information based on inputs from multiple searchers, and evaluation methods and metrics for assessing the impact of collaborative search. This work fits in the broader area of Information Seeking Support Systems, as described in a recent report to the NSF, available online at http://ils.unc.edu/ISSS/ISSS_final_report.pdf .

Our research focuses on software systems, broadly defined. Areas of interest range from building operating system mechanisms such as lightweight virtualized containers, multiprocessor deterministic record-replay tools, and process schedulers, to creating distributed system architectures for mobile, cloud and autonomic computing. For more information and recent papers, please visit the Network Computing Laboratory website: http://ncl.cs.columbia.edu.

I have broad interest in the area of database, especially in application domains with particular data management needs not easily met through traditional databases. I am particularly 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, information extraction, human-computer interaction and machine learning.

Our Group, which comprises two research laboratories and some additional research projects, is conducting research in several areas:
• User-Controllable security and privacy: Combines user-centered design principles with the development of novel technologies aimed at empowering users to more effectively and efficiently specify security and privacy policies. This includes the development of user-controllable machine learning techniques, explanation technology, auditing functionality and related user interface technologies along with the evaluation and refinement of these solutions through combinations of laboratory experiments and pilot studies. One of our pilot systems, Locaccino, is a mobile location sharing service available as a Facebook application. Pilot studies have involved up to several hundred users at a time.
• Expressiveness in mechanism design: A complementary area of interest involves the development of a theory of expressiveness in mechanism design along with its application in the design of expressive languages for security and privacy, looking at issues of efficiency and user burden
• Research of autonomous and mixed initiative trading solutions capable of effectively exploring a large number of alternatives spanning multiple dynamic markets
• Research and evaluation of novel supply chain management solutions, including mechanism design applied to the design of supply chain contracts and the study of novel ways of managing portfolios of flexible supply chain contracts
• Green supply chain management – Evaluating the impact of different supply chain management practices on the environment, including tradeoffs between traditional supply chain management metrics and environmental factors.

automatic metadata generation, metadata generation models, ontological integration, Semantic Web development, see MRC for more information on current research projects: http://ils.unc.edu/mrc/