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 areas of expertise are in Natural Language Processing and Computational Linguistics. The long term goal of my research is to develop methods and systems that automatically discover the
meaning of written text and understand its content. To that end, I am developing techniques that
(1) cluster short text snippets based on the similarity of their content, (2) discover word meanings in large corpora of text, (3) assign meanings from a dictionary to words in running text, and (4) measure the semantic similarity and relatedness between concepts. I place a high priority on releasing and maintaining free open source software packages to enable others to reproduce and extend our work.

All aspects of theory of computation, including crypto, Streaming Algorithms;
Routing and Network Algorithms;
Metric Embeddings;
Search and Classification Problems on High-Dimensional Data.
For more infomration see:

http://www.cs.ucla.edu/~rafail/Rafail-research-statement.pdf

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.

I am directing Data Mining Group in the Center for Computational Science at the University of Miami. Overall goals of the group is to develop methods for for understanding, spatial/temporal, heterogeneous data.

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.

Come join a large well-funded research effort in cloud computing! Over 10 Carnegie Mellon University professors and 4 Intel Labs researchers are teaming up to drive a common research agenda in cloud computing. Contrary to the common practice of striving for homogeneous cloud deployments, we are exploring the use of specialization as a primary means for order of magnitude improvements in efficiency (e.g., energy), including the design of new platform configurations based on emerging technologies like non-volatile memory. Other research themes include automation at cloud scale (e.g., resource allocation/scheduling, problem diagnosis), big data analytics beyond search (including over live data feeds), and new paradigms for meshing client devices and cloud. Our research lab is a great place to do research. It combines the resources of a well-funded industrial research lab (e.g., our lab’s cluster has 1500+ cores and 600+ terabytes of storage) with the academic freedom of a university. As for me, I am the Intel PI for the cloud computing efforts. I have over 120 publications (cited 10,000+ times), including co-authoring award-winning papers at ICDE, ISCA(2), NSDI, PLDI, and SIGMOD, as well as 13 other papers that were selected for “best papers” journal issues for their respective conferences (including ICFP, PODC, PODS, SIGCOMM, SPAA, and VLDB). I have served on 50 program committees for international conferences (including program chair, vice-chair, etc), and have helped place junior collaborators on a number of top committees. I am on the editorial board of the Journal of the ACM, and an ACM Fellow.

Theory and algorithms for machine learning. Topics include:
- Design and analysis of algorithms for clustering data streams
- Climate Informatics: a new subfield on machine learning for climate science
- Online learning, and learning from data streams (including in semi- or unsupervised settings)
- Privacy-preserving machine learning
- Active learning

My research group (Advanced Data Management Technologies Laboratory) focuses on user-centric data management for network-centric applications. In a nutshell, my research tries to remove the optimize-for-one-global-metric mentality of modern data management systems and, instead, bring users into the loop, by allowing them to specify preferences, especially in the presence of multiple metrics that are in direct trade-off with each other (for example Quality of Service versus Quality of Data). This is particularly useful in periods of high load (or other resource “pressure”), since the system is able to allocate crucial system resources according to users’ preferences, thus increasing the overall user satisfaction by meeting their goals.

We are working on multiple different projects under this broad philosophy, from web-databases (NSF CAREER award), to data stream management systems (NSF IIS award), to energy-efficient data management (NSF EAGER award).

Lately, we have also worked on scientific data management, with particular emphasis on annotations and workflows, under two domains: biological domain (NIH-funded Center for Modeling Pulmonary Immunity) and the astronomy domain (NSF CDI award). These are done through collaborations with the Medical School and the Department of Physics and Astronomy of the University of Pittsburgh.

Building behavioral/content based user profiles and recommender systems

We are seeking highly qualified research fellows in content analysis, text mining, machine learning, recommendation systems, and data mining. Example areas include (but are not limited to) classification, similarity analysis, clustering, feature selection, search, text mining, personalization, user modeling, collaborative filtering, novel information processing and extraction methods, and related areas. The candidate will make contributions in a strong team of diverse backgrounds (computer science, information management, statistics, econometrics, mathematical optimization, and marketing science). The charter of this team is to generate knowledge and build technologies to spur the growth of HP’s businesses.

Imre Solti is an Assistant Professor in the Division of Biomedical Informatics, Department of Pediatrics at Cincinnati Children’s Hospital Medical Center at Cincinnati, Ohio. He received his M.D. in 1992 from Albert Szent-Gyorgyi Medical School, Szeged, Hungary, his Ph.D. in Health Services Organization and Research from the Medical College of Virginia, in 2006, and his M.A. in Computational Linguistics from University of Washington, Seattle in 2011. He received a K99 Career Development Award from the National Library of Medicine (NIH) in 2009. He is a current NIH R00 Pathway to Independence Award recipient (2010). His recent research interests are based on information extraction from electronic health records to automate the clinical trial eligibility screening process, develop clinical Natural Language Processing and Clinical Decision Support Systems for patient safety, and predictive models for clinical outcome.

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).

My primary research interest is in lifelong machine learning, including:
- statistical methods for knowledge transfer,
- methods for scaling transfer learning to long sequences of tasks,
- sparse representations for transfer learning,
- self-directed goal-driven learning,
- interactive learning in collaboration with a user, and
- the application of these methods to problems in medicine, search & rescue, and space exploration.
I am also conducting research on community detection in relational networks, and AI education.

My current research interests lie in the intersection of three fields: database; privacy and security; XML and web. In particular, I’m interested in:

• Information security and privacy
• Privacy and security for smart grid systems
• Healthcare informatics

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

I have broad HCI and CSCW interests. I am interested in cognitive/social design principles underlying the design of systems in the following areas:
- large scale social and collaborative systems (e.g. communities)
- personal information management
- digital support for memory/lifelogging
- interactive information retrieval

My university is just 5 mins from the Pacific in a beautiful part of n. California

My research interests cover a wide variety of topics including Smarter Web, Web Task Modeling, Web Mining and Web accessibility. The distinguishing aspect of my research is that I seek efficient and theoretically sound techniques to solve a problem in a principled way and validate the techniques by implementing them in real systems. One of the key features of my research is to improve the usability of a Web based system by identifying and presenting relevant information to the user. I have developed information presentation techniques (e.g., contextual browsing, model-directed navigation, conversational interface) to improve usability of such a system. I applied those techniques to reduce information overload problem in different areas, such as Web Access using different modalities, Web automation and testing, information retrieval, Email and collaborative task management, social media analysis. Another important aspect of my research is that I have applied machine learning algorithms to get efficient solutions in many instances, e.g., learning relevance model of a Web user, Web task models, Web accessibility models, learning models for website testing.

Data mining, information retrieval, databases, algorithms and computational geometry, especially in ranking, top-k query processing, and sampling problems in databases, and data management problems in social networks, blogs and web communities.

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.

I am broadly interested in the area of data mining, machine learning and Information retrieval in application areas such as Bioinformatics, E-Commerce and Social Sciences. In data mining, I worked on generic pattern mining, graph mining, clustering, and nearest-neighbor query. In Bioinformatics, I worked on protein docking and currently working on subgraph mining from PPI networks. In e-commerce, I have worked on query suggestions and query segmentation. In information retrieval area, I have worked on ranking of patent documents. My doctoral thesis on subgraph sampling got ACM SIGKDD dissertation award in 2010.

Ginkgo BioWorks is a young company out of MIT with the mission of making biology easier to engineer. We engineer organisms to solve challenges across a range of industries from fuels to pharmaceutical production. We aren’t trying to study biology, we are trying to build it – constructing, editing, and redesigning the living world. Our bioengineers make use of an in-house pipeline of synthetic biology technologies to design and build new organisms.

We are looking for a computing postdoc who is passionate about developing CAD tools for the engineering of organisms. You probably have a background in comparative genomics, computational biology, metagenomics, or other similar areas. You have likely been building computational tools to study natural biology — but at Ginkgo you would have the opportunity to apply your skills and tools to the challenge of engineering new organisms.

At Ginkgo, our organism engineers have immediate demands for expanded tools to support organism design. We believe that we provide a unique environment for someone to cut their teeth on real CAD problems in biological engineering. Lastly, we’d be happy to serve as mentors for CIFellows if you are pursuing that fellowship.

Our group at MIT aims to further our understanding of the human genome by computational integration of large-scale functional and comparative genomics datasets. We develop algorithmic, statistical, and machine learning methods to interpret the functional elements encoded in the human genome, reconstruct the regulatory circuits they define, and understand their evolutionary mechanisms.

(1) We use comparative genomics of multiple related species to recognize evolutionary signatures of protein-coding genes, RNA structures, microRNAs, regulatory motifs, and individual regulatory elements.

(2) We use combinations of epigenetic modifications to define chromatin states associated with distinct functions, including promoter, enhancer, transcribed, and repressed regions, each with distinct functional properties.

(3) We use dynamics of functional elements across many cell types to link regulatory regions to their target genes, predict activators and repressors, and cell type specific regulatory action.

(4) We combine these evolutionary, chromatin, and activity signatures to dramatically expand the annotation of the non-coding genome, elucidate the regulatory circuitry of the human and fly genomes, and to revisit previously uncharacterized disease-associated variants, providing mechanistic insights into their likely molecular roles.

I do work research in social computing and social media. My approach to research typically centers around using computational methods (e.g., applied NLP) to both understand the social internet and build new systems for it.

Current topics:

* Building new email interactions
* Building new interaction techniques for social networking sites
* Analyzing diffusion through networks over long spans of time
* Analyzing online social behavior through theoretical lenses

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

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

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

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

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

- Privacy – how perceptions shape behavior

My 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.

My research is focused on engineering secure systems. In particular, I am interested in designing secure but highly efficient systems. Currently, I am exploring techniques for securing cloud computing and social networks. I am also interested in securing provenance, and trustworthy database and storage systems.

Large complex systems require real time operations management. Some example include domains such as managing transportation networks, managing large computer systems and massive data centers, managing electric grids, etc.

Since the underlying mathematical models for these complex systems are intractable, real time management of such systems require us to build systems that can process large amount of data and take decisions in real time. The system will need to be able to efficiently process stored and streaming data, structured and unstructured data, and event and sensor data.

Given the problem above, we are interested in building new algorithms and techniques in areas such as complex event processing, data mining and machine learning, information management. Besides the algorithmic challenge we are also interested in building, designing and optimizing a large scale system that can implement these algorithms and techniques for various industry verticals such as healthcare, education, energy, etc.

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!

I am interested in advancing the state of the art in information retrieval and natural language processing at Web scale. I am also interested in using large-scale repositories of collaboratively-generated content (e.g., all of Wikipedia) for improving text processing and artificial intelligence applications.

For additional details, please see my Web site.

There are several potential research projects within the linqs group at UMD (www.cs.umd.edu/linqs) ranging from core machine learning and probabilistic modeling, to applications in social network, personalized medicine and information alignment. We have a large research group and a CI Fellow would gain experience in mentoring undergraduate and graduate students, grant writing, and teaching (if interested).

The functioning of a living cell is governed by intricate networks of physical, functional, and regulatory interactions among different types of molecules. My research builds phenomenological and predictive models of these networks to investigate the relationships among the molecules in a cell, how these elements are organized into functional modules, how these modules interact with each other, and how different modules become activated or de-activated in various cell states. I develop algorithms and computational tools based on graph theory, data mining, and machine learning. This work is driven by collaborations with life science researchers spanning diverse fields including biochemistry, biophysics, infectious diseases, plant pathology, and tissue engineering.

Novel approaches to computationally-driven experimental biology are a major new thrust in my group. The goal is to develop computational methods that have the explicit goal of suggesting and prioritizing new experiments, to collaborate with biologists who will perform these experiments, and to incorporate the resulting data to refine the computational approaches. We are doing this research under the aegis of the Center for Systems Biology of Engineered Tissues. Current projects fall into two major areas:

(i) Link top-down and bottom up models of the regulatory network controlling the budding yeast cell cycle.

(ii) Use transcriptomic data to predict protein signaling pathways that mediate inter-cellular communications in bioengineered mammalian tissues.

machine learning and applied cryptography to improve privacy, security, and fraud detection in the cloud, in social networks, and in ubiquitous computing applications

I have a broad interest in almost all disciplines of Computer Science, and have shifted my focus several times during my research career. My major of B.S., M.S., and Ph.D are all in control theory, but I spent most of my Ph.D time on the application of control theory to computer systems, especially deadlock avoidance in multithreaded software. I become a compiler and program analysis specialist after this exercise. My work at HP Labs was initially around the application of control theory to business process management systems. We focused on Service-Oriented Architecture and studied the service composition problem in great length. Lately I am working on Intelligent Transportation Systems. We are interested in building an IT platform that can collect and analyze large-scale sensor input in realtime from various sources, e.g., GPS probes, loop detectors, and close-circuit cameras.

My research is in mobile networking and distributed real-time algorithms based on data mining, with a specific application area in Intelligent Transportation Systems (ITS). Currently in Anchorage, many vehicles are transmitting data gathered from the vehicle’s OBD port back to a central server hosted at the University. This data includes over 200 vehicular parameters. Gathering data from individual vehicles instead of from hardware installed in the roadways at discrete locations allows applications that require much more precise and granular data to be developed. To gather data from individual vehicles requires specialized architectures, such as vehicle-to-infrastructure (V2I) or vehicle-to-vehicle (V2V). The applications that can be developed are endless, with a few examples being fastest paths, traffic congestion improvement, incident identification, roadway slippage determination, and emergency vehicle routing, among others too numerous to include. My research spans the areas of software and network architectures, mobile networking, and distributed real-time algorithms, so an emphasis in any of those areas is acceptable.

I will consider candidates in any of my research keyword areas.

Projects of current special interest in my group include:

* active learning to choose drug candidates during drug discovery
* active learning to fit scientific models, especially in astrophysics
* methods for finding groups or pattern of anomalies
* planning/navigation for active data collection in an anomaly finding task
* matrix completion methods in demand forecasting and collaborative filtering
* methods of smart regularization in learning
* learning dynamic models from scientific data where time/trajectory information is missing
* finding events in time series data from sensor networks, in particular for the CTBTO

My research interests are in the area of algorithmic data mining with emphasis
on social-network analysis, analysis of sequential data, ranking and clustering.

Prof. Balazinska is developing tools and techniques for data-intensive scalable computing, cloud computing, sensor data management, stream processing, and scientific data management. Her research focuses on building high-performance data management systems but also building tools for making these systems easier to use (e.g., SQL auto-complete, performance debugging, etc.)

I am interested in a variety of topics related to database management system technology, mostly focused on database system architecture, algorithms, and performance evaluation. We are currently in year two of a large parallel semi-structured data management and analysis project at UC Irvine, ASTERIX, and I am interested in mentoring CI Fellows whose interests closely overlap our work. Roughly speaking, ASTERIX can be thought of as semi-structured data management meets parallel database systems meets Map/Reduce (and friends). We have completed a first version of the data-intensive run-time system for ASTERIX, called Hyracks, and are now building the upper layers of ASTERIX (including a new semi-structured data model and query language) as well. The ultimate goal is the creation of a robust, open source software system that will be shared with the research community. An appropriate CI Fellow would be systems-oriented, excited about building something “real” that others can use, and have experience with Hadoop and/or parallel DBMS technology and/or scalable distributed storage management (including data consistency issues, e.g., transaction and consistency models for large-scale, cluster-based data management). Because we are building a system, we are interested in CS Fellow candidates who are interested in implementation as well as design and evaluation.

I work mostly in database theory. In recent years, I have focused on data exchange, although I am happy to work on other aspects of database theory. I still have a deep fondness for finite model theory.

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.

My research interests are in data management, cloud computing, computer games, data mining, and data privacy.

Primary work and research interests are in the areas of query languages and query processing, data models, and index structures in object oriented databases, multimedia databases, and temporal databases. More recently, I have been working in Bioinformatics, more specifically on managing, visualizing and querying genomic pathways, efficient access methods and index structures for genomic sequences, and pedigree data management for efficient querying and efficient and scalable computations on pedigree data.

* Generic Stream Data Mining: mining, integrating and tracking pattern evolution in data that arrives in huge quantities under extremely stringent memory constraints, making it necessary to process the data in only one sequential direction. Examples: web clickstream data, network activity/log data, astronomical observations, sensor data, social and text data (tweets, blog posts, news)

More Specific Activity Examples:

* Web Stream Mining: mining web data in particular mining data from multiple stream sources, such as web usage data, text and social networking data within a streaming environment (massive evolving data that requires scalability and need for adaptation of the learning algorithms and outputs as well as tracking and characterizing evolution patterns)

* Text Mining in Social Data Streams: mining and visualizing the inherent topics and events and tracking their evolution in text data such as content of Web blogs, fora and social networks within a streaming framework

My primary research interests lie at the intersection of privacy, security, data analysis, and data management. I am very interested in the field of secure information sharing and its various applications; as also the application of secure computation technologies to business processes such as supply chain management and optimization. Our lab looks at different aspects of privacy, such as privacy in graph structured data, privacy in location based services, medical health record privacy etc. We are also interested in the use of data mining techniques to enhance security, such as in Role Engineering. As such, our lab looks at fundamental data mining problems such as boolean matrix factorization and the use of these for solving domain issues such as those in access control.

Please visit my webpage for more information.

In robotics we are interested in motion planning with emphasis on high-dimensional systems and kinodynamic planning, planning from high-level temporal goals, assembly planning, reasoning with sensing and control uncertainty, flexible object manipulation, physical modeling, probabilistic methods in robotics, the geometry of motion, and the use of new enabling technologies. We also develop educational modules and distribute software for teaching motion planning.

In computational structural biology and bioinformatics we develop tools on high-performance systems to model protein structure and function, understand biomolecular interactions and help analyze, in the long run, the molecular machinery of the cell. We integrate sequence information with three-dimensional structural information to analyze and represent molecular flexibility and motion. Our work in computer-assisted drug design has also led us to investigations in systems biology.

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.

My background is in theoretical computer science, especially algorithms. My current research interests are in several areas of computational biology and bioinformatics. I am especially interested in genomics and the analysis and manipulation of genomic sequences. An example is the study of genomic signatures that can be used to identify which organism a “new” sequence comes from. Another example is an advanced kind of genome compression, supporting both efficient storage and transmission as well as efficient information retrieval and manipulation. Also, I am interested in analyzing or mining genomic sequences to identify evolutionary phenomena such as convergent evolution, gene conversion, and retrogenes.

Finally, I am contributing my expertise to the analysis of a metagenomics project.

The following NSF grant was recently awarded:

http://nsf.gov/awardsearch/showAward.do?AwardNumber=1062472

My research interests are on database indexing and data mining, database security, multimedia indexing, and probabilistic and uncertain data management.

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

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

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

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.

My research group focuses on developing representations and algorithms for learning causal models of complex systems of interacting components. These can be human communities (e.g., scientific communities, corporations, and government agencies), online social networks (Facebook, LinkedIn, and Twitter), peer production systems (Wikipedia, Stack Overflow), and complex computer systems (data centers, multiagent systems). We are developing radically new methods that assist human investigators in understanding the deep causal structure of these systems.

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.

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.

information integration, query-evaluation algorithms, temporal reasoning,
automata-theoretic algorithms, firmware validation, protocol synthesis, constraint satisfaction, discrete techniques in robotic motion planning

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.

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

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

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.

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.

I have three ongoing research interests. The first is ergalics, the science of computer science, specifically about generating and testing causal predictive models. I have focused on such models for database management systems, though I am open to work that strives to develp such models for other classes of software systems. This is my primary research interest. The second is temporal databases, in all incarnations, including conceptual design, logical design, physical design, query languages, query processing, storage structures, and XML. The third is compliant databases, that is, DBMS architectures and approaches to support trustworthy, tamper-proof data that is compliant with laws such as Sarbanes-Oxley. Please see the publications on my web site and the research projects also listed there for more information.

Our research is to advance state of the art in the area of biomedical information and knowledge extraction and management. The lab’s daily activities consist of learning, investigation, design, implementation, testing, and evaluation of efficient algorithms and techniques to solve challenging problems in support of a variety of applications including: i) advance NLP techniques in biomedicine, ii) ontology-based database integration, and iii) bioinformatics and computational biology.

Professor Xu’s research interest lies in the development of machine learning models and optimization algorithms for the problems in the field of Computational Biology and Bioinformatics. He is currently working on the following topics: protein sequence/structure alignment, homology detection, protein structure prediction, protein side-chain packing, protein-protein interaction prediction, and biological network analysis. Professor Xu has developed a popular protein structure prediction program RAPTOR, which performed very well in several CASP (Critical Assessment of Structure Prediction) events. His tree-decomposition algorithm for protein side-chain packing now is a major technique underlying the new version of SCWRL, the most widely-used side-chain packing program.

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

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

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

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.

Developing scalable data mining algorithms for processing massive volumes of data
Statistical machine learning
Text analysis
Data mining in streaming environments and distributed platforms
Time series analysis

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

Current interests are in Cloud Computing, Information Distribution and Higher Layer Protocols for Wireless Networks.

Cloud Computing
Energy Savings
Information Assurance
Applied Cryptography
Network Security
Trusted Hardware

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.

Large-scale business analytics and Seismic Imaging

My research in interests include image/video analysis (video search and retrieval, activity recognition, camera networks, multimedia databases and data mining), and more recently, malware analysis using signal/image processing techniques. I direct the NSF/ITR funded Bio-Image Informatics Center and the Vision Research Lab. I am also associated with the interdisciplinary Multimedia Arts and Technology program and the Computer Science Department. The Vision Researh Lab web page gives more details about the current projects in my group. Useful links: http://vision.ece.ucsb.edu, http://www.bioimage.ucsb.edu.

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

Automated planning: Model-lite planning, stochastic planning, human-robot teaming

Information Integration: Handling incomplete, imprecise and inconsistent data over autonomous data sources

My research focuses on machine learning with emphasis on developing fast scalable algorithms for massive data analysis. Towards this end our lab develops optimization algorithms and focuses on graphical models, structured prediction, and kernel methods. Application areas include computer vision, social network analysis. I am open to explore any area that is fun and is sufficiently challenging!

I’m interested in how one does data mining when the underlying space of data is non-Euclidean: maybe data lies on a surface in Euclidean space, or on a general Riemannian manifold. There are numerous applications where the underlying data exhibits such characteristics, and in order to solve such problems, we need a wholesale reevaluation of the tools of high dimensional approximate computational geometry. I’m also interested in a variety of large-data/streaming problems, especially those arising from data integration problems.

My research lies in the intersection of AI (ML, agents, text mining, Semantic Web) and Systems (Mobile/Pervasive computing, Web, distributed systems). As recent examples, our group has developed Policy controlled BGP routers that use agent negotiation for automatic fault detection and recovery, Context aware mobile systems for surgery, Social media analytics that factor in both network features and link semantics, Systems that base information sharing decisions on context and policies, System to determine context in mobile systems that respects privacy constraints, and MANETs/CyberPhysical systems whose security/intrusion detection posture is modulated by context.

Databases, Information Retrieval, Web Search: distributed search over text databases, desktop search, information extraction, text mining, social media

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

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

My research
interests are in developing geometric and optimization algorithms for
applications in robotics, automation, microsystems, and biotechnology. My recent research has spanned a variety of topics including developing algorithms for coordinating microdroplets in digital microfluidic lab-on-chip systems, performing collision detection and dynamic simulation of objects, and
coordination of multiple robots.

* Streaming algorithms and data mining
* Internet Ad Exchanges: Mechanisms, Dynamics, Optimizations, Data analysis. Economics and Game Theory.
* Online Approximation for Internet ad systems.
* Databases: Probabilistic and Stochastic databases.

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

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.

My primary research interests are in
data mining/machine learning, high performance computing and database systems.
In our lab we seek to develop efficient and novel
algorithms for managing and analyzing complex data. Our recent research
is particularly motivated by
applications that arise in the area of
network science (specifically biological networks and social networks).
Below we briefly describe two projects in these areas, for others
please refer to the personal and laboratory web pages listed.

1. Architecture Conscious Algorithms and Systems:
Here we have been looking at ways
in which various algorithms (XML indexing, Network motif mining, Frequent
pattern mining) can be re-designed to fully exploit the capabilities
of current day architectures ranging from GPUs to
multicores to supercomputing systems. Of particular interest
is the development of an effective infrastructure enabling such algorithms to
scale to very large data stores .

2. Algorithms and Systems for Network Science:
Here we seek to unravel common principles, events, algorithms and tools that
govern network behavior across different domains ranging from social
networks to biological networks. Of particular interest here are not just
algorithms for module discovery, link discovery,
anomaly detection and event detection
but also usable systems infrastructure that can enable
researchers to effectively
query, visualize,
and analyze such networks under various trust, probabilistic and
provenance models.

If you are interested to learn more about our activities in these areas please feel free to contact me as noted herein.

I am interested in designing and implementing large-scale distributed systems that are robust and easy to write applications against. I have worked on a variety of projects including a distributed persistent object store, a secure distributed serverless file system using Byzantine fault tolerance, diagnostics in wireless networking, object-relational mapping framework, and a distributed auto-partitioning lease manager for datacenter servers – the last two pieces of work shipped as part of commercial products/large-scale online services. I am currently working on another highly-scalable distributed system that can make certain aspects of Web programs much more efficient and easier to program.

(a) Bioinformatics and Computational Molecular Biology: Data-driven discovery of macromolecular sequence-structure-function-interaction-expression relationships, identification of sequence and structural correlates of protein-protein, protein-RNA, and protein-DNA interactions, protein sub-cellular localization, automated protein structure and function annotation, prediction of functionally important sites in proteins, modeling and inference of genetic regulatory networks from gene expression (micro-array, proteomics) data, modeling and inference of signal transduction and metabolic pathways, comparative analysis of macromolecular interaction networks.
(b) Data Mining: Design, analysis, implementation, and evaluation of algorithms and software for data-driven knowledge acquisition, data and knowledge visualization, and collaborative scientific discovery from semantically heterogeneous, distributed data and knowledge sources, Applications to data-driven knowledge acquisition tasks in bioinformatics, medical and health informatics, energy informatics, ecological informatics chemo-informatics, security informatics, social informatics, e-government, and e-science.
(c) Knowledge Representation and Inference: Logical, probabilistic, and decision-theoretic knowledge representation and inference; Representing and reasoning about preferences. Secrecy or privacy preserving query answering from knowledge bases; Federated knowledge bases and inference algorithms; Description logics; Neural architectures for knowledge representation and inference, Computational models of perception and action
(d) Machine Learning: Statistical, information theoretic, linguistic and structural approaches to machine learning, Learning and refinement of bayesian networks, causal networks, decision networks, neural networks, support vector machines, kernel classifiers,, multi-relational models, language models (n-grams, grammars, automata), Learning classifiers from attribute value taxonomies and partially specified data; Learning attribute value taxonomies from data; Learning classifiers from sequential and spatial data; Learning relationships from multi-modal data (e.g., text, images), Learning classifiers from distributed data, multi-relational data, and semantically heterogeneous data; Incremental learning, Ensemble methods, multi-agent learning, selected topics in computational learning theory.
(e) Semantic Web: Ontology-based user and query-centric approaches to information integration and acquisition of sufficient statistics for learning from data under different access and resource constraints from heterogeneous, distributed, autonomous, ubiquitous information sources; description logics, federated knowledge bases, modular ontology languages, web service composition.
(f) Other Topics of Interest: Biological Computation – Evolutionary, Cellular and Neural Computation, Complex Adaptive Systems, Sensory systems and behavior evolution, Language evolution, Mimetic evolution; Computational Semiotics – Origins and use of signs, emergence of semantics; Computational organization theory; Computational Neuroscience; Computational models of creativity, Computational models of discovery.

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

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

My 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.

I am interested cryptography and data privacy, and their connections to diverse fields such as information theory, combinatorics, quantum mechanics and statistics. Recently, I’ve focused on a few specific topics:

* Privacy-preserving publication of statistical data
* Cryptography based on noisy secrets (”fuzzy cryptography”)
* Efficient two- and multi-party protocols for tasks such as agreement, commitment and secure function evaluation.
* Quantum cryptography, and quantum information theory

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.

Protein function prediction, protein inference and quantification from MS/MS, post-translational modifications, predicting molecular basis of disease and gene/disease relationships, supervised learning, kernel methods in computational biology.

In Machine Learning, I have been working on developing theory and algorithms for understanding and exploiting new types of learning protocols that have become crucially important for modern machine learning applications. These include the ability to use unlabeled data in the learning process (i.e., semi-supervised learning), clustering, interactive learning, learning with kernel and similarity functions, multi-agent learning or learning in the presence of other learners.

A second major focus of my research has been on Algorithmic Game Theory. My work here includes the use of machine learning techniques to automate aspects of Mechanism Design and formally address the problem of market analysis, the development of pricing algorithms with improved guarantees over previous methods, and the development of techniques for understanding the overall behavior of complex multi-agent systems in which selfish entities are optimizing for themselves.

A third major focus of my research has been on developing fundamental connections between Machine Learning
and other areas of Theoretical Computer Science including Game Theory and Combinatorial Optimization. These connections are enabling both to advance Machine Learning and to solve important problems in Theoretical Computer Science via a fresh set of ideas and techniques.

Prof. Vitter’s research interests include the design and mathematical analysis of algorithms, especially dealing with massive data. He has worked extensively in data compression, string algorithms, external memory algorithms and I/O efficiency, computational geometry, caching and prefetching, machine learning, incremental algorithms, and order statistics. His work on the analysis of algorithms deals with the precise study of the performance of algorithms and data structures under various models. His work on I/O-efficient methods for solving problems involving massive data sets has helped shape the subfield of external memory algorithms. He is actively involved in developing efficient indexing methods for text and techniques for space-efficient processing of massive data. Other work includes sorting, information storage and retrieval, geographic information systems and spatial databases, clustering and geometric optimization, random sampling, and random variate generation.