Our research group develops advanced methods and software tools to quantify and mine the rich information present in cellular images to yield biological discoveries. Our laboratory is best known for our open source software packages CellProfiler and CellProfiler Analyst. See http://www.broadinstitute.org/~anne/research.html for more details.

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 research group works on two different areas
• Spatial Audio and Computational Sound Scene Analysis:
I have been fascinated by the way humans effortlessly use spatial sound for making sense of the environment. A desire to understand the mechanisms used and to provide machines with the same capabilities has been a driver
in this research. The use of computing, mathematical physics and experiments have characterized this work.

• Large Scale Scientific Computing
In scientific computing I focus on very large problems using approximation algorithms and parallel computing. The work has been driven by applications in audio, speaker ID, vision, physics, and machine learning. The focus here is on extending algorithms based on the fast-multipole method, and the use of data-parallel architectures to achieve orders of magnitude speed-ups.

I am interested broadly in computer vision and multimedia processing for human-centric interactive applications involving home-based healthcare, smart environments, and educational applications.

wireless networks, mobile computing, cloud computing, networked detection, sening and fusion.

My Carbon group within CSAIL MIT is working on a new operating system for massive multicores and cloud computing called Factored Operating System (FOS). We are also working on multicore architectures for 1K cores. Postdoc positions are available in both these areas.

I look at how to apply queueing-theoretic analysis to improving the performance of computer systems. These days I am very interested in power management of data centers, and capacity provisioning for data centers, particularly under transient load conditions.

We have many projects that span multi-agent AI, robotics, and biology. Please see our website for more information.

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.

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

Research in high-performance low-power VLSI, temperature-aware circuits and architecture, embedded systems, nanoelectronics, spintronics, printable electronics, SOC design, subVolt switch design, aging and wearout.

Hierarchical graphical models for object and activity recognition; Max independent set and max clique formulations of vision problems, such as image matching and tracking

Intra-die process variations in nm technology nodes pose significant challenges to robust design practices. Geometric variations along with random dopant fluctuation effects have had significant impact on Logic/Memory functionality/yield. The inaccuracies in the models and variabilities in the process are more pronounced and key is to understand the variability effects. Innovative analysis techniques as well as methodologies are needed to counteract the variability issues. Our team is heavily engaged in this activity.

Smart statistical and numerical techniques are key for understanding the variability in VLSI circuits. These techniques need to be highly efficient to be used by designers. New algorithms to achieve speed-up in these techniques are essential. Extension of these smart algorithms beyond the VLSI domain would be critical to achieve accurate and computationally practical would benefit such fields (e.g application to medical field). We are exploring new algorithms and their applications to fields other than VLSI.

As the VLSI circuits scale power and performance remain the top issues. Developing efficient circuit techniques based on emerging devices (FinFet derived, nano-devices) needs extra attention.
We are involved in fabrication of such circuits. Also we are engaged in modeling of 3-D structures and the thermal management of multilevel domain.

Scaling and manufacturing of alternate memories such as MRAM and PCM would help to replace DRAM. Variety of activities from modeling to fabrication are explored in our team

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.

I am broadly interested in topics around cloud computing, including software for datacenter management, cluster file systems, techniques for managing data center power, hybrid cloud technologies, programming frameworks, operating systems, and cloud applications. As part of my research, I manage a 200-server cluster designed to enable experimentation with prototype system software, so our research facility provides ample opportunity for empirical evaluation.

Research in the Computer Vision Laboratory at Columbia University is focused on three broad areas, namely, the creation of novel vision sensors, the design of physics based models for vision, and the development of algorithms for scene interpretation. Research projects are motivated by applications in the fields of digital imaging, computer graphics, human-machine interfaces and robotics.

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

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

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

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

The main focus of my work is on energy efficient computing. Energy consumption has already become one of the most important design parameters in electronic systems. In large scale data centers it is because of the high cost of powering and cooling the equipment. We are currently working on design of VM level energy management strategies that integrate measurement of power, temperatures and workload characteristics, while monitoring QoS of applications running within VMs. In addition, we are evaluating how to integrate SmartGrid information with VM level energy management strategies and how to leverage accurate predictors of the availability of renewable resources that we developed. This work is funded by a large center on Multiscale Systems (www.musyc.org).

We recently also won an NSF Expedition on variability. The work here focuses on how to address variability issues in general purpose systems due to environmental, manufacturing and vendor differences. The current focus is on cooling and thermally aware scheduling techniques for 2 and 3D integrated server and mobile systems.

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.

Limited bandwidth, increase in power dissipation at longer communication distances create a major communication bottleneck in high-performance computing (HPC) systems, affecting not only their performance, but also their scalability. My research interests include designing interconnects for high performance computing systems at all levels: on-chip, chip-to-chip and rack-to-rack.

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.

In response to the increasing challenges in maintaining technology advancements through traditional scaling at a pace consistent with Moore’s law, alternative methods to achieve enhanced system level performance are becoming increasingly important. 3D Technology has the potential to provide significant performance enhancements for several generations. Realization of this technology will require collaborative research and development across system architecture, design, and technology. Significant changes to the basic circuit design, layout procedures and tools flow, as well as the routing of global signals and supplies (power/ground distribution, clock distribution, and I/O), will be required to accommodate the 3D technology features in stacked active chip designs. These changes can only be understood through a detailed evaluation of the impact of each of the unique 3D technology elements on the design. The Research Division is involved in leading the exploration of these new aspects of 3D VLSI chip design for future systems.

Mary (Missy) Cummings received her B.S. in Mathematics from the United States Naval Academy in 1988, her M.S. in Space Systems Engineering from the Naval Postgraduate School in 1994, and her Ph.D. in Systems Engineering from the University of Virginia in 2003. A naval officer and military pilot from 1988-1999, she was one of the Navy’s first female fighter pilots. She is currently an Associate Professor in the Aeronautics & Astronautics Department at the Massachusetts Institute of Technology. Her previous teaching experience includes instructing for the U.S. Navy at Pennsylvania State University and as an assistant professor for the Virginia Tech Engineering Fundamentals Division. Her research interests include human interaction with autonomous vehicle systems, modeling human interaction with complex systems, decision support design for time-pressured, uncertain systems, and the ethical and social impact of technology.

My research spans networked systems and theory. Ongoing projects include:

- Low-latency networking: Even simple application-level tasks in today’s networks, like retrieving a small web object, take many times longer than the underlying network latency. How can we design the Internet’s protocols to approach speed-of-light responsiveness? We are exploring novel low-latency designs in naming and transport protocols, and in scalable routing on flat names. There is interesting work to be done ranging from theoretical analysis to fundamental protocol design to applications including Content-Centric Networking, Internet architecture, and ad hoc networks.

- Network debugging: We are building a tool, Anteater, that examines data plane state of networks in order to find bugs in a flexible, unified manner. A paper on Anteater will appear in SIGCOMM 2011, and there are very exciting opportunities for further work in this area.

- Data center network topologies: With researchers at UIUC and UC Berkeley, we are studying how to build high-bandwidth data center networks that allow incremental expandability, and are simultaneously more efficient than traditional topologies like fat trees. A short paper on our initial design, Jellyfish, will appear in HotCloud 2011.

- Future Internet Architectures: The Internet has become the basis of most of humanity’s remote communication. Its rapid growth and the fact that we use it in so many unexpected ways attest to the groundbreaking flexibility of its design. Yet because of this growth, the Internet’s architecture has encountered a range of limitations. What would an Internet look like that provides the dependability, security, manageability, and efficiency to enable innovative applications for decades to come? With collaborators at Berkeley, Georgia Tech, MIT, Princeton, and Stanford, we are designing and developing an architecture that answers that question.

- Flexible Networks With Source Control: Giving users some control over packet routing is a promising technique to dramatically improve reliability, efficiency, and flexibility of networks, but it is also a radical shift from today’s Internet. How can a routing and forwarding architecture flexibly support source control, while respecting the interests of network owners? How can users quickly pick reliable, efficient routes that are appropriate for the application, given limited information about the dynamic state of the network? What is the effect of source control on denial of service vulnerability? Answering these questions critically requires tools ranging from planetary-scale systems building to game-theoretic analysis.

- In addition to the above topics, my past work deals with randomized algorithms, peer-to-peer systems, incentive compatibility, distributed systems, and more; I maintain my interest in these and other areas of networked systems and am open to collaborations on various topics.

A particular feature of my research is to integrate practical network systems design with theory. This approach leads to systems built on firm foundations with guarantees on their behavior, as well as interesting and difficult theoretical problems that can expose fundamental principles. As such, I’m actively seeking fellows who are system builders, theoreticians, or interested in bridging the two areas.

My research concerns the development of efficient algorithms and software
for matrix algebra problems This work began with the development of
innovative memory-efficient algorithms and, more recently, has moved
toward tools to aid in programming of matrix algebra software. I have
recently been collaborating with experts in compiler technology, focusing
on compilers that create fast numerical software. Our initial focus
has been on making efficient use of the memory hierarchy on a single
processor but we are moving into multicore and GPU implementations.
I am also interested in usability of scientific software. To that end,
I am working with collaborators on a tool to automate the construction
of numerical software. Given a problem specification, the tool will
find and tune appropriate routines for its solution.

I was co-developer of an award-winning, NSF-funded undergraduate
curriculum in high-performance scientific computing and have continued
to work on innovative approaches to education in my field. I have
also conducted research on factors that influence women’s interest in
computer science.

Bo Begole is a Principal Scientist and Manager of the Ubiquitous Computing Area at the Palo Alto Research Center. He is an applied computer scientiest who aims to create novel systems and capabilities for end-user applications. His past work includes systems that provide synchronous collaboration of single-user applications, computer-mediated communication, distributed interpersonal awareness, sensor-based interruptibility detection, presence modeling and prediction, media device interoperability and control, reducing information overload and context-aware mobile systems.

Bo’s unifying research interest is in the area of Behavior Modeling — Using sensor and data inputs, along with inference algorithms, to identify and predict an individual’s current activity and interest in particular information. He likes to work with people who have broad interests and creative approaches to solutions.

Bo received a B.S. in 1992 in Mathematics from Virginia Commonwealth University, an M.S. in 1994 and a Ph.D. in 1998 in Computer Science from Virginia Tech. Prior to his studies, Bo served in the US Army as an Arabic language translator specializing in Egyptian, Libyan and Iraqi dialects. Bo was recently a co-Chair of the 2008 conference on Computer-Supported Cooperative Work (CSCW 2008) and was the Technical Program chair of CHI 2011.

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

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

My current research is focused on basic and applied research in computer vision, image understanding, pattern recognition, and quantitative microscopy. The mission of our group has been to develop novel methods and systems for image and scene analysis that are capable of making human-like decisions. Our passion has been in the pursuit of scientific excellence and innovative engineering, enabling pragmatic solutions to problems of societal impact. Current research initiatives of the group are in wide-area video surveillance for behavioral analysis and biometrics and biomedical image analysis for morphological characterization with applications in medicine.

Our work focuses on how a robot can autonomously learn the foundational domains of higher-level knowledge — space, objects, actions, affordances, and so on — from unguided experience with the “blooming, buzzing confusion” of pixel-level sensorimotor interaction with an unknown world.

We have done work on learning about sensors, constructing models of large-scale space, abstracting foreground objects from the relatively static background, and building a hierarchy of progressively higher-level actions starting from simple motor signals.

Some of our work starts at a higher level, developing a robot that can serve a person as a mobility aide. It needs to build its own cognitive map of the environment from its experiences, but it also needs to learn to communicate effectively with the human driver, who is the ultimate source of authority in its world.

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

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

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

Prototyping? is pivotal to design innovation, collaboration, and creativity. My group’s research empowers more users to design interactive systems, expert designers to be more creative, and programmers to engage in more design thinking. Our research has introduced?techniques for users to demonstrate interactive behavior, sample existing design elements to create new ones, and tightly integrate the creation and evaluation aspects of design. In much of this work, examples – both created and harvested – play a powerful role. We explore these issues on mobile, desktop, web, and sensor-based platforms. We seek to understand the fundamental psychological and social ingredients of design excellence in order to create more effective design tools and practices. We create prototypes that envision future interface styles; a current project looks at the future of mobile interaction.

BIO?·?Scott is an Assistant Professor of Computer Science at Stanford University, where he co-directs the Human-Computer Interaction Group. Organizations around the world use his lab’s open-source design tools, and several books and popular press articles have covered his research. He is a co-recipient of the CHI and UIST Best Paper Awards, Microsoft Research New Faculty Fellowship, and Sloan Fellowship. He received a dual BA in Art-Semiotics and Computer Science from Brown University, and an MS and PhD in Computer Science from UC Berkeley.

DEMO focuses on origins and principles of organization for complex systems. If we can create chain reactions in complexity with a universal computational substrate, then certain hard problems should become easier to solve.

Co-Evolutionary Learning

Most learning takes place as optimization of a fixed environment or fitness function. This requires that the learner be “pre-adapted” to that environment in order to learn anything, or that the environment be “gradient engineered” for the particular learning mechanism. This inductive bias usually makes the researcher the primary cause of learning. We focus on dynamically-changing learning environments, often composed of competing learners, where the complexity of the task gradually and automatically increases without human intervention. We work in games, language learning, and computational optimization tasks.

Evolutionary Machines

The long term goal is the co-evolution of machines and their brains, first in simulation, then, through advanced computer-aided manufacturing, into actual hardware. Our initial experiments simulated LEGO structures and evolved groups of simulated robot agents. The GOLEM project through 5 generations evolved designs which could be built into reality using automated manufacture. The frontier is to co-evolve form and formation, to evolve manufacturing plans which can overcome noise and errors.

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.

I have broad interests in systems spanning applications, system software, networking, and architecture. Current interests include: 1) Cloud/Grid applications, system software, and architecture; 2) Computer Architecture for Exascale computers; and 3) Programming models and tools for post-Moore’s Law computing substrates.

Carlos is interested in developing efficient algorithms and methods for designing, analyzing and controlling complex real-world systems. Our application domains have been typified by sensor networks, where limited resources, unreliable communication, failures, and large problem scale are prevalent. These characteristics require us to develop theoretically-founded algorithms, to unify methods from statistics, optimization theory and machine learning, and to exploit problem-specific structure. Moving forward, our focus will be on designing methods for managing information overload, and tackling the challenges of machine learning over parallel architectures.

My research interests are in security and algorithmic number theory. In the field of security, my research is currently focused on wireless security (ad hoc networking, wireless standards, roaming and handover), privacy enhancing technologies (in particular fairness in privacy-preserving multi-party computations), biometrics (cryptographic key generation from biometrics based on secret sharing techniques), and security economics. In algorithmic number theory, my research is centered on lattice theory (in particular on developing new algorithms and heuristics for lattice basis reduction) and exploring the effective use of GPUs.

I am interested in algorithmic economics and game theory. I am particularly interested in understanding dynamic behavior, and in identifying settings which are well-behaved, e.g. markets which converge toward price equilibria. I am also interested in mechanism design.

My current research addresses two frontiers in design tools.
For experts, I am investigating how to create the ideal design studio of the future. New input technologies and platforms are rapidly proliferating.
These devices are very promising for professional work because of the richness and nuance of input they provide. I investigate how such technologies can be harnessed to augment professional design practice.

For amateurs, I am developing structured ways of sharing tool expertise and knowledge. Empirical data suggests that designers as well as programmers rely heavily on online resources such as tutorials, examples, and Q&A with online peers to gain expertise. We are working on integrating design tools with online platforms.

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.

Analysis of Boolean functions, Approximability of optimization problems,
Learning theory, Complexity theory, Property Testing, Probability

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).

HP Labs Immersive 3D

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

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

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

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

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

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

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

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

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

2010 Sundance Film Festival

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

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

HP Newseum announcements

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

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

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 research focuses on the development of mathematical models and algorithms for estimating evolutionary history in Biology and Historical Linguistics. The main objective is to develop methods that produce much more accurate estimations of evolutionary history than can be obtained using existing tools. Our group is distinguished from many other groups in computational biology due to our focus on ultra-large datasets, with up to 500,000 sequences. We use real data and perform simulations to evaluate the performance of methods that we develop. This research area inolves mathematics, probability, statistics, computer science, and intensive collaborations with domain specialists. My current research is funded by two grants from the National Science Foundation, one an Assembling the Tree of Life (ATOL) grant for simultaneous estimation of alignments and trees, and another for estimating species trees from gene trees. I also have an active interest in metagenomic analysis. No background in Biology is needed. However, good mathematical intuition, and skill in algorithm and software development are important.

Design, implementation and evaluation of distributed services and applications that adapt and exploit social characteristics; algorithms for efficient computation of social graph metrics (e.g., centrality measures, triad census); experimental systems approach combined with theoretical and analytical evaluations.

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

We are looking for a post-doctoral fellow for the XIA Future Internet Architecture project.

The eXpressive Internet Architecture (XIA) addresses the growing diversity of network use models, the need for trustworthy communication, and the growing set of stakeholders who coordinate their activities to provide Internet services. XIA addresses these needs by exploring the technical challenges in creating a single network that offers inherent support for communication between current communicating principals–including hosts, content, and services–while accommodating unknown future entities. For each type of principal, XIA defines a narrow waist that dictates the application programming interface (API) for communication and the network communication mechanisms. XIA provides intrinsic security in which the integrity and authenticity of communication is guaranteed. XIA enables flexible context-dependent mechanisms for establishing trust between the communicating principals, bridging the gap between human and intrinsically secure identifiers. The current focus of the proj ect is on novel transport protocols, network support for services and mobile users, and content centric networking.

More information on XIA can be found on the project web site: http://www.cs.cmu.edu/~xia

Current research interests include Bayesian and Bayesian nonparametric methods for:
- modeling nonlinear dynamical phenomena
- identifying latent group structure between multiple time series
- scaling to highly multivariate time series by inferring conditional independencies or harnessing lower dimensional embeddings of dynamics
- exploiting multiple sources and types of data
- efficient inference (optimization-based methods, data-driven MCMC, SMC methods for online inference)

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

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

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

With the rapid growth of mobile devices, sensing techniques under mobile conditions become more and more important. I am interested in sensor signal processing, analysis, and fusion for portable and real-time health care monitoring and clinical measurements. I am also interested in techniques for smart 3D sensing, interaction, and display. Other directions include security and biometrics, signal retargeting, enhancement, compression, and retrieval, driving safety monitoring, and smart and flexible robot training.

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

Within the fields of neuroscience and machine perception and learning, I work on ways to discover structure automatically in complex sensory environments, and on relationships between neurobiology, signal processing, and information and control theory. My early work in this field pioneered in the use of information theory to devise novel principles for neural network learning and for understanding biological sensory processing.

My current interests include the application of “deep learning” neural networks (e.g., multi-stage Restricted Boltzmann Machines and related algorithms) to detecting rare anomalous behavior in large financial datasets. A prospective mentee should have a PhD with substantial knowledge of and practical experience with neural networks. Some experience with deep learning NNs is especially desirable.

My main theoretical focus is on the potential connections between deep learning NNs and the neural organization of cerebral cortex. I seek principles that may account for aspects of known biological neural architecture, and insights from biology that may enhance the power of artificial NNs.

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.

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

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

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

Research in my lab focuses on understanding the information processing strategies employed by the visual system for tasks such as object recognition and scene analysis. Computer scientists have long sought to emulate the abilities of the visual system in digital computers, but achieving performance anywhere close that exhibited by biological vision systems has proven elusive. Our approach is based on studying the response properties of neurons in the brain, and attempting to construct mathematical models that can describe what neurons are doing in terms of a functional theory of vision. The aim of this work is not only to advance our understanding of the brain, but also to devise new algorithms for image analysis and recognition based on how brains work.

I’m interested in how users manage their digital information through their multiple devices. My work has studied how people re-find information on the web; how people’s mental workload is affected when they move from one device (desktop) to another (phone); and how people use phones in social settings to satisfy information needs.

The rise of social networks presents an opportunity for the management of personal information. Lately, we have begun exploring how to leverage a user’s social network to improve PIM. Emails in a person’s inbox, for example, are “shared” between the sender and the receiver. What if we could share the PIM practices within our inner personal circle? Could we leverage the power of our social network to be more organized? We currently leverage common email patterns to identify spam… we could leverage common PIM practices (tags, filing, organizing) to help others in our inner circle organize their information?

A second area of research, in education technology, is exploring the future of electronic textbooks, particularly as the intersection with in-class management of education. Imagine if lecture notes, assignments, student note taking, readings, and student-to-student discussions, were all managed from a tablet computer. What would our classroom interactions be like? What tools would teachers need to manage the classrooms? What impact on learning would this system have? Can an app be built that guides students towards “today’s” readings and manage their attention to “tomorrow’s” homework?

Developing and ensuring the reliability and security of software systems by developing innovative software architectures, languages, and tools.

I am interested in Programming Languages and Software Engineering, more specifically, in techniques for building reliable computer systems. My work draws from, combines and contributes to methods the areas of Model Checking, Program Analysis and Automated Deduction. In particular, I have worked on Software Model Checking, the design and implementation of languages and tools for building robust Distributed Systems, and analyses for multithreaded systems software. More recent interests include advanced refinement type systems for statically verifying sophisticated
program invariants (with low programmer overhead) and techniques to analyze information flow and other security properties in modern web applications.

My research typically involves both theory (“proving theorems”) and practice (“building tools”.) In addition to the topics above, I am happy to explore related ideas for potential projects.

I have broad interests in large-scale distributed systems. I’d be delighted to collaborate with somebody on either of my two main activities:

1. Cluster management – Google’s term for the systems that allocate work to our fleet of computers, solving what are effectively large-scale bin-packing problems under a raft of constraints, failures, and uncertainty. We are pushing the envelope of sophistication and capability in managing clusters of machines, and I’m open to collaborating on a wide range of topics, including scheduling, failure modeling, configuration management, and a raft of automation approaches.

2. Using SLAs (Service Level Agreements) to improve our ability to delegate control to automated decision-making systems when they face trade-offs. Work here includes control mechanisms, control systems, feedback loops, and system design. SLAs need a way to specify the consequences of meeting/not meeting their objectives, and economic feedback mechanisms seem a particularly powerful way to achieve this.

As you might imagine, Google provides a great many really exciting opportunities to try these ideas out at significant scale, on real production systems, with real-world problems and requirements. It’s an ideal proving ground for real systems work in support of a wide range of uses, including cloud computing. And we are looking to publish more papers in these areas, so that would be an explicit goal of supporting this program.

Please contact me if you are interested; I’d be happy to discuss opportunities. I’m based in Mt View, California.

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.

We recognize visual objects with such ease that it is easy to overlook what an impressive computational feat this represents. Any given object in the world can cast an effectively infinite number of different images onto the retina, depending on its position relative to the viewer, the configuration of light sources, and the presence of other objects in the visual field. In spite of this extreme variation, biological visual systems are able to effortlessly recognize at least hundreds of thousands of distinct object classes—a feat that no current artificial system can come close to achieving.

My laboratory seeks to understand the computational underpinnings of object recognition through a concerted effort on two fronts. First, we endeavor to understand the workings of biological visual systems using a variety of experimental techniques, ranging from microelectrode recordings to visual psychophysics. Second, we attempt to instantiate what we have learned into artificial object recognition systems, leveraging recent advances in parallel computing to build systems that begin to approach the scale of natural systems. By combining reverse- and forward-engineering approaches, we hope to accelerate progress in both domains.

I’m applying my bioinformatics research to solve problems in systems biology with implications to medical conditions such as cancers and addictions. My interest include cancer genetic and neurogenetic discovery based on SNPs, gene expression microarrays, protemics data, and biomedical ontologies. Specifically, I utilize machine learning tools, information theory, and holistic statistical constructs such as Bayesian Networks. My research involves both leveraging well established methodologies and developing novel algorithms to push the limits of biomedical informatics.

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’m interested in all types of network security and wireless networks, including core networking research and applications such as medical devices and cloud computing.

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

Our research interests lie in the cross-section of petascale distributed systems, eScience, Grids, Clouds, and collaborative computing with a focus on large-scale data-intensive distributed applications. Our research especially responds to the urgent need of scientists across a broad range of disciplines who have large-scale data generation, analysis, sharing and collaboration requirements. We develop a diverse set of algorithms, models, and tools for mitigating the data bottleneck in petascale distributed computing systems as well as supporting a broad range of data-intensive and dynamic data-driven applications.

Our current funded research activities can be summarized in these four subareas:

Data placement scheduling. We are working on priority-based data placement scheduling to provide capabilities such as planning, scheduling, resource reservation, job execution, and error recovery for petascale data transfer tasks. Specific tasks that we are working as part of this component includes development of priority-based scheduling with advanced reservation and provisioning; data aggregation and caching; integration with peer-to-peer and real-time data streaming technologies; and support for early error detection, classification, and recovery.

End-to-end data throughput optimization. In a data-intensive petascale system, effective use of available network throughput and optimization of data transfer speed is crucial for end-to-end application performance. Specific tasks that we carry as part of this component include development of application-level models to predict the best combination of protocol parameters for optimal network performance, including the number of parallel data streams, protocol buffer size and I/O block size; integration of disk and CPU speed parameters into the performance model to predict the optimal number of disk (data striping) and CPU (parallelism) combinations for the best end-to-end data throughput; and development of an estimation service for advanced reservations and provisioning.

Integration with workflow planning. The end-to-end performance of petascale distributed applications will depend on the integration of data transfer and scheduling services into higher level workflow planning and management services. Specific tasks that we carry as part of this research component includes development of a model to choose the best data access method specific to application; integration of the data placement scheduler with the workflow planning systems; and incorporation with the existing storage management systems.

Distributed storage management. We have been working on a novel distribute storage system which will enable transparent handling of underlying data sharing, archival, and retrieval mechanisms, and make data available to scientists across all participating sites for analysis and visualization on demand. The system provides scientists with simple uniform interfaces to store, access, and process heterogeneous distributed data sources. The stored data is catalogued using a novel cross-domain metadata framework which allows queries spanning multiple application domains.

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

Speech recognition, spoken language translation, situated spoken language understanding, machine learning

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 interests are in the area of computer security: how can we build secure systems, and how can we be confident that the resulting systems will be secure?

I am looking for a strong postdoc to work in one of the following areas:

Smartphone security, especially security of 3rd-party apps. The goal is to support 3rd-party smartphone apps securely and lay a solid foundation for smartphone security.

Usable security for phones. To build secure phone platforms, we need to take a user-centered perspective. I warmly welcome applications or interest from researchers in the area of HCI, usability, human factors, or usability evaluation. No prior background in security needed: you bring the usability knowledge, I’ll bring the security background.

Measuring security. Can we experimentally measure the effectiveness of various security technologies? For instance, it would be useful if we could measure the effect on security of programming languages, frameworks, and tools for developers.

Electronic voting. Can we improve the auditability, usability, or effectiveness of electronic voting technologies? Can we securely vote over the Internet?

I am also interested in general in usable security, software security, and systems security, and I welcome applicants. Also, feel free to contact me separately about paid postdoc positions, apart from the CIFellows program.

The Magnum Group at Virginia Tech is focused on investigating how the powerful processors and variety of sensors in new and planned
smartphones, such as Apple’s iPhone and Android-based smartphones, can be leveraged to build cyber-physical applications that collect sensor
data from the real world and communicate it back to cloud services for
processing and aggregation. The broad dissemination of these
smartphones, their accelerated processing power, range of sensors, and
pervasive cellular connections make them ideal platforms for building these
novel mobile cyber-physical applications. The Magnum Group investigates a broad range of topics in this area, such as augmented reality, crowdsourcing, communications middleware, cloud computing platforms, citizen scientists, traffic accident detection, modeling, and software product-lines. We are continually looking for and open to new ideas in this and related areas.

My current research interests lie in computer vision and machine learning. In computer vision, I am interested in facial processing and analysis, object tracking, event/activity recognition, and image segmentation. In machine learning, I am specifically interested in theoretical developments in probabilistic graphical models (e.g. Bayesian Networks and MRF, HMM) including learning, inference, and their applications in different fields, in particular in computer vision. I am also interested in decision making and information fusion based on graphical models such as the influence diagrams. Refer to my website for details

My research focuses on culturally appropriate uses of information and communications technology (ICT) to improve delivery of health information and health services in low-income countries. ICT offers many benefits, including access to online resources, electronic data collection, electronic storage and retrieval of patient data, etc. However, there may be social and cultural barriers to acceptance of the technology. Age, gender, class, education level, national or regional culture, level of urbanization, and political climate can all be potential barriers to acceptance. Careful understanding of social and cultural contexts is needed to avert such unintended negative effects. My research evaluates the acceptability and impact of handheld computers, mobile phones, websites, and other ICT in order to understand the social and cultural barriers to accepting ICT in healthcare settings in low-income countries.

My research builds on years of experience working with global health projects in sub-Saharan Africa. Currently, I supervise a program in Rwanda that offers medical and psychosocial care in two hospitals and four clinics throughout the country. I am also on the advisory board of a women’s health website with global reach.

Dr. Angelos Stavrou is Assistant Professor in the Computer Science Department and a member of the Center for Secure Information Systems at George Mason University, Fairfax, Virginia. He received his M.Sc. in Electrical Engineering, M.Phil. and Ph.D. (with distinction) in Computer Science all from Columbia University. He also holds an M.Sc. in theoretical Computer Science from University of Athens, and a B.Sc. in Physics with distinction from University of Patras, Greece. His current research interests include security and reliability for distributed systems, security principles for virtualization, and anonymity with a focus on building and deploying large-scale systems. He is a member of the ACM, the IEEE, and USENIX.

Over the past few years, Dr. Stavrou’s research has focused on two aspects of security: Systems’ Security and Reliability. In the context of the first, he is working with NIST as part of the DARPA “Transformative Applications” project that involves securing Android mobile phone devices against kernel-level attacks. Furthermore, Stavrou is the GMU PI participating along with Columbia, Stanford, and Symantec in the IARPA “StoneSoup” effort. In addition, the PI is funded by DARPA under the CyberGenome project to perform analysis on the phylogenetic origins of malware. Dr. Stavrou is currently supported by a NSF Trustworthy Computing (NSF-CNS- 0915291) grant on “Scalable Malware Analysis using Lightweight Virtualization”. This effort seeks efficient methods to collect and analyze the nature of Internet malfeasance.

Contact him at email obfuscated – click to reveal.

The following describes the projects I am currently most interested in.

The field of machine learning increasingly leverages graph representations and graph-based algorithms in a variety of applications and problem settings. For instance, semi-supervised learning, dimensionality reduction, and unsupervised clustering problems are frequently solved by casting data points as nodes in a graph and then applying a graph-theoretic algorithm. Similarly, probabilistic inference problems are solved by casting random variables as nodes in a graph and then applying graph algorithms such as message passing, tree approximations and other variants. Most of these learning and inference graph problems are NP-hard in general. Similarly, various combinatorial graph problems such as graph coloring, maximum stable set and maximum clique are NP-hard in general. However, in combinatorics, scientists have identified a large family of graphs known as perfect graphs where combinatorial problems become tractable and admit polynomial time algorithms. We hope to investiga te a variety of hard learning problems and, by compiling them into combinatorial problems on perfect graphs, identify situations in which they may be solved exactly or approximated well. We plan to investigate various algorithms for exact solution including linear programming, semidefinite programming and message passing.

The first type of learning setting involves inference in graphical models: graph-based representations for encoding a set of conditional independence properties that hold in a probability distribution p(x_1,\ldots,x_n) defined over a set of random variables. Two important canonical computational problems therein are MAP Estimation (finding the configuration that maximizes p(x_1,\ldots,x_n)) and Marginal Inference (finding the marginal configuration of variables, such as p(x_i)). Both of these are NP-hard in general. However, it is possible to compile the graphical model into another graph and solve an equivalent maximum stable set problem. If this graph happens to be a perfect graph, the inference problems are tractable. We hope to follow this general approach to identify sufficient conditions for tractable graphical model inference.

The second type of learning setting to investigate involves operations on data graphs: graph-based representations which encode pairwise similarity relationships between pairs of objects {x_1,\ldots, x_n}. Two canonical computational problems therein are Clustering (finding multiple disjoint subsets of the data with relatively high self-similarity) and Anomaly Detection (finding a single subset of data with relatively high self-similarity).

We aim to develop methods of solving these problems by ensuring that the data graph constructed from these samples is perfect. This allows the clustering problem to be mapped to a graph coloring problem and allows the anomaly detection problem to be mapped to a maximum clique problem. Both of these problems are solvable in polynomial time for perfect graphs using convex programming methods.

Please apply for a CI Fellow position with me and if you are in the New York area, please try to drop in for a visit.

My research focuses on two main topics and their interdependence: the role of physical constraints in processor design, and the architecture of future multi/manycore platforms.

Despite continuing availability of plentiful transistors thanks to Moore’s Law, physical constraints—chiefly temperature, power delivery, energy efficiency, and reliability—now prevent straightforward scaling of processor performance. New design-time and run-time techniques are needed to address these limitations, as well as improved modeling and capabilities. Our group at UVA has a long track record of high impact work in these areas.

These physical constraints, combined with the memory wall, will constrain the types of designs that are possible, requiring both novel combinations of cores and novel memory hierarchies. Our group is exploring these issues, especially focusing on the memory hierarchy, how to organize cores around bandwidth limitations, requisite programming support, and how to incorporate these techniques within power, thermal, and reliability constraints.

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

The goal of the Galois project is to develop programming notations, compilers and runtime systems for programming multicore processors. We focus on irregular algorithms, which are algorithms in which the key data structures are sparse graphs, trees, sets, etc. This class of algorithms is very general, and includes mesh generation, refinement and partitioning algorithms, SAT solvers, machine learning algorithms, n-body methods etc. Our work has appeared recently in top conferences like PLDI 2011, POPL 2011, OOPSLA 2010, and ASPLOS 2011. Our group has six PhD students and three post-docs, and we are always looking for new talent!

My research interests are in reinforcement learning, structured prediction, transfer learning, and machine learning for planning and natural language processing. I am interested in a wide range of aspects of these problems from fundamental theoretical aspects to real world applications in real-time strategy games, emergency response, curriculum scheduling, and medical informatics.

My research primarily focuses on enabling synergetic autonomy in human-robot interactions in dynamic real-world application domains such as elderly care and surveillance. Each robot operates autonomously as far as possible, using algorithms to: learn environmental models based on sensory inputs; adapt the learned models in response to environmental changes; tailor sensing and information processing to the task at hand; and robustly merge the information obtained from teammates. At the same time, the robot acquires and fully utilizes limited high-level information from humans through a variety of interaction modalities, e.g., vision, natural language and touch-screen visual displays.

I am also interested in designing and adapting Bayesian reasoning algorithms to address real-world challenges characterized by a significant amount of uncertainty. Some (current) examples of applications are: medical data processing, HRI/HCI interface design, and adaptive learning technologies for training teachers and promoting learning among students with disabilities.

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

I am interested in how we can get robots and people to work more effectively together. Can we build control interfaces to robots that allow non-specialists to more effectively control them and get them to do useful work? Can we add social gestures to mobile robots and make them more efficient as they go about their tasks around humans? Can we decode signals from the human brain, and use these to control robots? In all of these areas, I am interested in the use of shared autonomy control, where the robots are as autonomous as possible, but are able to ask for help when this autonomy fails.

My research is in the areas of computer vision, image processing, and pattern recognition. I develop robust techniques and algorithms for image feature extraction and classification, and the application of these techniques to different real world problems. Representative projects I have worked on include document image watermarking and authentication, video scene analysis and classification, fingerprint image verification, license plate recognition, medical cell image analysis, 3-D object recognition, infrared target classification, road crack image analysis, and character recognition.

Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin. He received his Ph.D. in 1988 from the University of Illinois at Urbana/Champaign. He is an author of over 150 published research papers, primarily in the areas of machine learning and natural language processing. He is the current President of the International Machine Learning Society, was program co-chair for the 2006 AAAI Conference on Artificial Intelligence, general chair of the 2005 Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, and co-chair of the 1990 International Conference on Machine Learning. He is a Fellow of the Association for Computing Machinery and the American Association for Artificial Intelligence and recipient of best paper awards from the National Conference on Artificial Intelligence, the SIGKDD International Conference on Knowledge Discovery and Data Mining, the International Conference on Machine Learning, and the Annual Meeting of the Association for Computational Linguistics. His recent research has focused on learning for natural-language processing, connecting language and perception, statistical relational learning, abductive reasoning, transfer learning, and active learning.

I am broadly interested in topics at the intersection of HCI and AI and Machine Learning. Major areas of focus include:

– Systems that adapt the interaction to the abilities (motor, perceptual, cognitive) of their users; this is to accommodate differences due to activity, health condition, context, culture, and bias;

– Interactive machine learning: we are developing novel interactions and algorithms that enable machines to learn from a small number of interactions with the user;

– Creativity support tools: how can personalized software help people explore the design space more efficiently, create more diverse alternatives, and improve self-efficacy?

Design, modeling, measurement, and analysis of conventional and biologically-inspired high-performance computer architectures and their interaction with state-of-the-art optimizing compilation systems, modern operating systems, and scientific, commercial, and intelligent applications.

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.

Our focus is the development of theory and tools for processing of signals that arise in today’s growing array of different applications—and pose challenges for the traditional signal processing techniques. Our focus is the development of data-driven methods for the analysis and fusion of medical imaging data. Our collaborations include Johns Hopkins Medical Institutions and the Mind Research Network.

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.