Our group develops algorithms for analysis and interpretation of genome sequences. Current projects include the study of structural variation in human and cancer genomes using next-generation DNA sequencing technologies, network analysis of somatic mutations in cancer, and evolution of the human genome. Further details available at the group website.

I am interested in all aspects of multicore processor architectures; current and future focus is on the memory hierarchy design and the interaction between computer architecture and the operating system. We have also worked on efficient simulation techniques to evaluate multicore systems with hundreds of cores.

I am also interested in applying new memory technologies, such as phase change memory, to small- and large-scale systems. Focus is on developing new low-energy main memory architectures for data centers and researching new hybrid-technology storage systems.

My work centers on two areas:
1. Often domestic interactions involve technology and center around gender norms, which makes homes highly gendered spaces. Gender is critical not just in our interpersonal relationships but in how we interact with our environment. The objects and technologies in our homes themselves are gendered, and we respond to social norms regarding gender in our interactions with them. We have additional gender norms surrounding programming of technology devices in the work place, which in turn affect programming of these gendered technologies in the home. As we increasingly introduce programmable technologies into the home, gendered attitudes become enmeshed in how we discuss and use these technologies.

I use ethnographic approaches to create grounded theory that examine gender and domestic end-user programming for computer security, amongst other things. My work discusses how the relationship between technology and identity is negotiated, especially gendered identity and the presentation of an individual’s technical ability. Further, I explore what this negotiation process means for design and evaluation of new technologies.

2. I enjoy working with teams to engage in participatory design in the ubicomp and tangible computing space trying to take the next step beyond fieldwork to create prototypes and engage in iterative interface design. I believe there is work to be done to discuss how the UCD process for ubicomp differs from GUI design, and this needs to be documented and discussed further in the academic literature. This innovative work has led me to recognize the limited applicability of existing usability evaluation methods (UEM), which were largely developed for GUIs. Thus I have developed a strong interest in adapting existing techniques and establishing new methods for evaluating these emerging technologies.

My primary research interests are in understanding the approximability thresholds of NP-hard optimization problems. I am also interested in some aspects of algorithmic game theory.

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.

1. GPU computing (GPGPU)
2. Massively parallel algorithms
3. Heterogeneous computing
4. Computational Intelligence, especially accelerated by GPUs
5. High-performance computing.

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

Our multidisciplinary team at Cornell is interested in using mobile devices to influence healthy behaviors. The goal of our research is to refine and evaluate behavioral interventions to reduce obesity. We are looking at the effect of positive affect, social networks (co-PI on NSF HCC Kleinberg, PI), social support systems, and cell phone apps on creating sustainable eating and physical activity.
We are using mobile devices and feedback tools to collect data on mood and activities and designing visualization tools for participants to reflect on their own patterns and behaviors over time. These tools could be applied to help monitor, change behavior or create awareness in multiple contexts.

http://idl.cornell.edu/index.php

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.

My research interests are in the design and analysis of approximation algorithms for NP-Hard problems as well as online algorithms for scheduling problems. Research touches upon various aspects of discrete and combinatorial optimization, mathematical programming techniques, and graph theory. Specific topics that I have recently worked on include:

* Network Design
* Routing in Graphs and Networks
* Submodular function maximization and related topics
* Broadcast scheduling

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

In recent work we have developed apprenticeship learning algorithms, which leverage human demonstrations to teach a computer how to perform challenging tasks. This has enabled us to solve some challenging real-world control problems. Our results include the first autonomous helicopter to perform human expert level aerobatics, far beyond the capabilities of any other autonomous helicopter to-date. Currently, we are investigating furthering the apprenticeship learning paradigm and also developing algorithms for autonomous learning. Application area of particular interest are personal robotics, medical robotics and autonomous flight. We also investigate motion planning and computer vision with applicability in these areas.

* 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.

My research focuses at the CyLab Usable Privacy and Security Laboratory focusses on usable privacy and security. My current projects fall into several overlapping areas: privacy decision making (including applications of P3P), user-controllable security and privacy (including location-sharing privacy and file access control in the home), and usable cyber trust indicators. See the CUPS website for more information.

This is joint work with Lada Adamic (Computer Science, School of Information, Complex Systems). We have an on-going project looking at question-and-answer communities (e.g., Yahoo Answers, Stack Overflow, Baidu, Naver) and social search (e.g., Aardvark, kgb). We are looking for a post-doc who is interested in (1) combining ethnographic and large-scale social network analysis techniques in the analysis of these systems, and/or (2) developing mechanisms and components for augmenting the expertise-finding and question-routing in these systems. For the analysis, we are especially interested in cross-cultural issues.

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.

Mobile and wireless networks, peer-to-peer systems, network security, Internet architecture, virtualization.

matrix theory and computation, large-scale scientific simulations, image data analysis, image reconstruction, algorithm-architecture co-design, mathematical software

I work in the general area of computer networks, distributed systems, and wireless/mobile computing. My research group explores novel network architectures, protocols, and algorithms that ensure reliable, secure, and efficient operation of large-scale computer networks and enable new, flexible network services. Our approach is often driven by real network and traffic measurements and analysis.

My current interests include:
1. Programmable measurement primitives and architecture that can support real-time traffic analysis and classifications for wide range of applications (e.g, billing, security, traffic engineering, etc.)

2. Measurement-based characterization of online social networks (OSNs) & applications and their implications on network design

3. Provisioning for cloud services

Please refer to RUBINET (Robust & Ubiquitous Networking) Lab website for more details.

A range of parallel computing: applications to large scientific problems (such as climate
modeling and high-energy physics), performance analysis, and parallel algorithms
for physically realizable models such as mesh-connected computers.

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

- 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? (One answer is our work on Pathlet Routing, in SIGCOMM’09, with Igor Ganichev, Scott Shenker, and Ion Stoica.) 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.

- Scalable Routing on Flat Names: Current networks use a pastiche of ad-hoc scaling
mechanisms and generally require location-dependent addresses, complicating mobility, management, and multihoming. Building on theoretical work in compact routing, in collaboration with Intel Labs we are developing distributed routing protocols which simultaneously guarantee scalability and nearly-shortest paths while routing on “flat” location-independent identifiers. 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.

- 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.

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 work on many topics in core operating systems, distributed systems, Web systems, Web browser architecture, and systems security. My current interests include:

1. Understanding and architecting the Web of the future, particularly reconsidering what a Web program is, what the browser’s future role as a fully-fledged operating system should be, and how to manage (or provoke!) the convergence of desktop OSs with browsers.

2. Exploring the impact of new technologies on operating systems abstractions and structure. One of my recent projects was to build operating system support for the deterministic execution of multithreaded and multiprocess applications, even on multicore processors. I’d also like to understand the long-term implications of persistent memories (such as phase-change memory) on how we should build operating systems and applications.

My interests cover all aspects of computer optimization and design, from advanced search and optimization algorithms to powerful meta-representations for open-ended design. In addition, I’m interested in developing a Science of Scalable Design, along with measures of Complexity. I am also working on building a next-generation, Interactive Evolutionary Design system.

My current research focuses on the design and engineering of mobile ubiquitous information systems, from software architecture to application design and user studies. My NSF CPS project “CitiSense – Adaptive Services for Community-Driven Behavioral and Environmental Monitoring to Induce Change” comprises a multi-disciplinary team including software engineering, HCI, embedded systems, AI, security, and medicine. We are designing, implementing, and deploying a mobile sensor system for community-based environmental monitoring. This project will offer excellent collaborative opportunities to a post-doc in software engineering or ubiquitous computing.

In the past I have focused on high performance computers. I and my colleagues developed some of the first prototype computers that exceeded 200 MHz. However, in the past decade I have focussed on various aspects of low power computing. My research group developed the “Intelligent Energy Management” system used in many ARM cores. I was a co-inventor on the original razor patents, a circuit technology that is being deployed in many next generation low power cores to combat the growing effects of variation that occurs in smaller technology nodes. My research group has developed a series of ultra-low power signal processors targeted at mobile wireless baseband processing. They evolved into a commercial prototype developed by ARM, which led to a commercial spinoff, Cognovo. They will commercialize the processor and provide wireless software stack.
My group were among the first to propose replacing DRAM with non-volatile memory to reduce memory power, cost and footprint. His group has also been active in exploring new server architectures that combine “near threshold” technology and 3D chip stacking to drastically reduce system power consumption. They recently complete the tape-out of a 128-core multiprocessor that integrated the cores, caches and DRAM into a single 3D stack. The fabrication run was funded by a DARPA grant.
Finally, my group is exploring ways to build low power compact interconnect fabrics. We recently completed a compact 128 x 128 crossbar architecture that can sustain a bi-section bandwidth of about 1.3 terabits / second while consuming less than 130 mW.

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

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

Henry Lieberman has been a Research Scientist at the MIT Media Laboratory
since 1987. His interests are in the intersection of artificial intelligence
and the human interface. He directs the Software Agents group, which
is concerned with making intelligent software that provides assistance to users in
interactive interfaces.

Many of his current projects revolve around applying Common Sense Reasoning
to interactive interfaces. He is using a large knowledge base of Commonsense facts
about everyday life to streamline interfaces, provide intelligent defaults,
and proactive help. Application areas include predictive typing, multilingual communication,
management of photo and media libraries, product recommendation and e-commerce tools.

***SMARTGEO***
SmartGeo (an NSF IGERT project) is an interdisciplinary program in the development of intelligent geosystems – enabling engineered and natural earth structures and environments that sense their environment and adapt to improve performance. Research efforts focus on using wireless sensor networks to advance intelligent geoconstruction, intelligent earth dams & levees, and remediation of contaminated soil and water. In one project, we research sensing and monitoring techniques to assess early onset of internal erosion of an earth dam by fusing geophysical, geotechnical and remote sensing data. In other project, we are developing a system that integrates multiple forms of sensed data, models, and techniques to develop a closed-loop uranium bioremediation system. A CI Fellow interested in wireless sensor networks, data mining, graphics/visualization, or machine learning would be a welcome addition to this project.

***NETWORK SIMULATION CREDIBILITY***
Simulation is the research tool of choice for a majority of the mobile ad hoc network (MANET) community. However, while the use of simulation has increased, the credibility of the simulation results has decreased. This work focuses on the development of tools that researchers can use to improve the credibility of their simulation work. For example, one focus of our work is to provide researchers with models that allow them to easily construct rigorous MANET simulation scenarios. The input to our models is the desired values for three metrics (e.g., average shortest path hop count); our models then output parameters for a simulation scenario that approximately meet the researchers target values for the metrics. Examples of other tools developed include visualization and realistic mobility models based on GPS trace data. A CI Fellow interested in impacting the way that researchers do network simulation would be welcome.

***COOPERATIVE BEAMFORMING***
Cooperative beamforming (CB) is a novel technique that enables high throughput and power efficient communications in a secure manner. CB consists of two stages. In the first stage, the sources share their data with neighboring nodes via low-power communications. Various approaches for such information sharing are considered, with a goal to minimize queuing delays, conserve energy, and achieve high throughput. In the second stage, the cooperative nodes apply a weight to the signal received during first stage, and transmit. The weights are such that a specific objective criterion (e.g., signal to interference at the destination) is maximized. In CB, although each node uses low power, all nodes together can deliver high power to a faraway destination. This research project is interdisciplinary and combines concepts in signal processing, economics, decision theory, optimization, information theory, communications, and networking. Our efforts at the Colorado School of Mines is focused on synchronization of the wireless communications and other research issues associated with the MAC layer. A CI Fellow interested in signal processing, wireless security, or communications would be a welcome addition to this project.

measurement-driven networking research; dynamics of and over Internet-related connectivity structures (e.g., router- and AS-level topology, P2P systems and Online Social Networks); multi-resolution analysis for the Internet

UC Berkeley’s Laboratory for Automation Science and Engineering, directed by Professor Ken Goldberg of IEOR and EECS, is a center for research in robotics and automation, with current projects in networked telerobotics, computer assisted surgery, automated manufacturing, and new media artforms.

Grasping and manipulation, dexterous robots, robot hands and robot hand design, human subjects studies of dexterous manipulation, dexterity in animated characters, physically based character animation, generating natural motion for robots and animated characters.

My research interests cover a broad range of systems topics, centering on distributed operating systems but extending into networking, programming languages, and formal systems. The common motivating theme throughout my research is to discover ways to build computing systems that “just work” in the widest possible variety of practical scenarios: to rethink and generalize traditional algorithms, system structures, and protocols so as to eliminate technical restrictions that frequently limit the utility, applicability, or scalability of these systems. Current projects include:

1. Tng: a next-generation transport protocol architecture, which decomposes traditional transport layer functions such as endpoint naming, congestion control, and application-visible semantics to improve communication flexibility, performance, and security while remaining backward-compatible with the current Internet.

2. Determinator: an experimental multiprocessor, distributed operating system that creates an environment in which anything an application computes is exactly repeatable – even if the application consists of many cooperating processes running different executables in parallel, such as a parallel make. Atop a minimal microkernel that enforces this determinism guarantee, a user-space runtime uses distributed systems techniques to emulate familiar shared-state abstractions such as Unix processes, global file systems, and shared memory multithreading.

3. Dissent: a new anonymous peer-to-peer messaging protocol for small, decentralized online groups, which offers not only provable anonymity and integrity but also provable resistance to anonymous denial-of-service attacks (e.g., anonymous spam, “jamming”, or other disruption).

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

Here are some of the high-level research questions I am interested in:

Structured Output Prediction: In conventional classification and regression, the prediction is a single number. Many application problems, however, require the prediction of complex multi-part objects like trees (e.g. natural language parsing), alignments (e.g. protein threading), rankings (e.g. search engines), and paths (e.g. navigation assistant). How can one tractably model and learn to make such complex predictions?

Humans in the Loop: Much of the data used for machine learning is gathered by observing human behavior (e.g. search engine logs, purchase data, fraud detection). However, it is known that this data is biased (e.g. users can click only on results that were presented). How can one learn despite these biases? Or how can the learning algorithm gather unbiased data by not being a passive observer, but by actively interacting with the human?

Understanding Archives: We are capturing and archiving more and more data (e.g. email, blogs, photos). While search engines give good microscopic access to individual data item, much work is needed to get a more macroscopic view of the content of an archive. How can machine learning help understand and summarize content, trends, dependencies, and idea flows in such archives?

My chief research focus is programmer productivity. I develop theoretical and practical techniques and tools for helping people to create, understand, and modify software systems; I perform significant experiments at scale; and I distribute tools to programmers and researchers. My research spans software engineering, programming language design, type theory, static and dynamic program analysis, testing, security, and development environments. My results often stem from cross-fertilization between traditionally separate research areas: experimental vs. theoretical, syntactic vs. semantic, static vs. dynamic, exact vs. inferred, proven vs. statistically likely. Overall, I am to make it easier — and more fun — for programmers to build robust, reliable, secure, and correct software.

I briefly mention several current research thrusts. More details are available at my webpage, http://www.cs.washington.edu/homes/mernst/.

Programming language design and type systems: My research focuses on bringing new capabilities to real languages in a backward-compatible way (which is critical for evaluation and adoption), and building implementations to assess their utility. This pragmatic approach promises to aid both today’s and tomorrow’s programmers, and makes results more likely to have practical impact. The work includes type systems, type inference, and frameworks.

Security: My research addresses three distinct practical problems: measuring unintended disclosure of private data, creating exploits for unknown security vulnerabilities, and fixing bugs that underlie zero-day exploits.

Debugging: Dynamic analysis is complementary to static analysis: although dynamic analysis is often unsound, it can also be more precise, scalable, and applicable to legacy programs. My work applies dynamic analysis, with good effect, to problems that in the past have only been addressed statically, such as type inference and refactoring. It is also useful for reproducing crashes and for reducing false positives in bug-finding tools.

Testing: A common theme in my research is exploiting impoverished test suites. Users seem willing to write small test suites or to supply a few sample executions, but they are reluctant or unable to write more comprehensive or more focused test suites. My research automates these and other testing tasks. The research spans test generation, test selection, test classification, and test execution.

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

Mobile phone sensing, learning and social net apps.

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 group’s research is aimed at various aspects of parallel computing, with a major goal of improving programmer productivity while retaining high efficiency and scalability. We aim at both high-end clusters and supercomputers, as well as parallel desktops, although our research in recent years has focused on large-scale supercomputers. We also make a point of working in collaborations with real science/engineering applications, with the belief that that is the only way of arriving at abstractions and technical ideas that will have a long-lasting impact on the state of the art. NAMD, a widely used parallel program for iomolecular simulations, is one of our early successes; it was developed in collaboration with Prof Klaus Schulten’s biophysics group at Illinois. (Recent applications include OpenAtom for nanotechnology/materials, and ChaNGa for astronomy. A major focus, and foundational theme, in our work is adaptive runtime systems, which inspects the execution, and automates resource management, and tunes the execution by a variety of means. This has been embodied in Charm++ parallel programming system. More recent efforts have aimed at higher level languages, with the idea that “incomplete” but elegant languages can combine together to form an effective toolbox for parallel programming.

We are part of the team that is working towards making the Blue Waters multi-petaFLOP/s system at UIUC (which will have 300,000+ cores) productive. Please visit the group website for additional information.

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

Over the years my research interests have covered a broad set of topics, ranging from system design to theoretical formulations to economic analysis. My current interests fall into five main areas:

* New Internet architectures: in collaboration with Hari Balakrishnan, Nick Feamster, Brighten Godfrey, Teemu Koponen, Nick McKeown, Guru Parulkar, and Jen Rexford, my research group is pursuing a new approach to Internet architecture that is built around a minimal framework that enables innovation in the rest of the architecture. I am actively looking for fellows eager to work in this area.

* Software-defined networking: networking is on the verge of a revolution, going from complicated switches/routers running specialized control algorithms (routing algorithms, etc.) to a new model where simple switches/routers follow instructions sent from management applications built on a general control platform (such as NOX, a network operating system). My research group is exploring this new approach in collaboration with colleagues at Stanford (Nick McKeown) and Nicira Networks (Martin Casado and Teemu Koponen). We are investigating both the control platform itself and new management applications (such as traffic engineering for the WAN, control mechanisms for datacenters, etc.).

* New network algorithms and protocols: As always, I am interested in new network mechanisms of almost any variety, from network monitoring to congestion control to intradomain routing to interdomain policy enforcement.

* Systems infrastructure for datacenters: Datacenters are the new unit of computing, and my research group (in collaboration with many colleagues here at Berkeley) are exploring the basic pieces of infrastructure needed to fully exploit the vast computation power residing in today’s large datacenters. So far we have focused on new resource allocation mechanisms (Nexus) and a new programming framework (Spark).

* Hypervisor-based security tools: We are exploring a range of security measures that can be implemented in today’s hypervisors. Two examples of this are Practical Taint Tracking (which has improved the performance of taint tracking by almost an order of magnitude) and Virtual Dedicated Console (which enables secure access to online services).

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

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

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

The MASI research laboratory concentrates on analyzing large-scale cross-sectional and longitudinal neuroimaging data. Specifically, we are interested in population characterization with magnetic resonance imaging (MRI), multi-parametric studies (DTI, sMRI, qMRI), and shape modeling.

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

Application of complex adaptive systems to:
- economics
- ecology
- policy
- healthcare
- conflict resolution
- social issues
- education
- game theory

Understanding the minimal set of fundamental properties of complex adaptive systems that define the emergent properties of such systems

Understanding the relationship between energy, information, and entropy in complex systems

Defining and measuring the degree of complexity of complex systems

Understanding verification and validation of complex systems

Designing a general complex adaptive systems tool for developing applications in diverse areas

Developing nature/brain-inspired computational platforms

Understanding creativity

My research is to apply the methods, lessons, and ways of seeing and acting of design to the realm of Human-Computer Interaction research. Design is a central element in my career: I study the practices of design, I have created tools for design collaboration, I am an architect (registered in California), and I am an award-winning designer. HCI is rapidly changing with new paradigms of technology and use, the only way to teach students for life-long adaptability is for them to understand how they can design the process of design.

This unique approach produced the seminal computer supported cooperative work system known as the media space and contributed to the development of a number of shared drawing tools, culminating in the Drawstream Station, all of which employ video in novel ways. Being a student of media, as well as design, has led to instrumental applications of media, most recently in developing exhibits and the overall program for “XFR: experiments in the Future of Reading”. These have been reported in numerous technical publications and conference proceedings, as well as the popular press. The studies of process and the technological interventions I’ve created have resulted in a number of novel insights; “RePlacing Space” (Harrison and Dourish, 1996) is an oft-cited paper on the behavioral framing of real and virtual space.

Building on this, I am currently (1) looking at how to integrate design thinking and practices into computer science; (2) developing theories of meaning-making with regard to power structures (e.g. design for marginalized populations, the role of cheats in on-line games, and gendered forms of technology); and (3) investigating domestic media spaces. I invite researchers to contact me about opportunities to pursue these areas.

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.

The computational aspects of the research include signal analysis, modeling and optimization of procedures, improved methods of modeling to provide robust conclusions from experimental data.
The lab’s experimental research interests focus on the structural biology of protein domains in intracellular signal transduction, including SH2, SH3, kinase, phosphatase, PH domains, and many others and how natural ligands interact with them. An additional area of interest is the development of NMR and related methods for structural biology. Projects include segmental labeling, other novel isotopic labeling methods, and detection of protein-protein interaction surfaces and dynamics.

The main research theme of my group is to understand the relationship between protein sequence, structure, and function, which is of fundamental importance in life science and the health care industry. We address important questions and problems in proteomics, structural biology, and biomolecular dynamics, using computational biology and bioinformatics approaches. In particular, we develop methodologies and techniques for massively parallel supercomputers such as the IBM Blue Gene, and other high performance computing (HPC) platforms such as grid computing and cloud computing. The group pursues a broad but well grounded approach to leverage and expand upon our existing techniques for scientific and technological advancement.

The current research projects in our group include: (1). Development of novel methods for parallel multi-scale modeling of complex biological systems. (2). Free energy perturbation (FEP) methods for large scale protein-protein, protein-ligand binding affinity predictions. (3) Influenza modeling with both statistical models using bioinformatics tools and physics-based models using molecular dynamics simulations. (4) Protein folding, misfolding, and aggregation with massively parallel molecular dynamics simulations. (5) Interactions of nanoparticles with biological systems and the related mechanism of nanotoxicity. The group is also actively engaged in research projects on modeling of GPCR, and proteomics/biomarkers.

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

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

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

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

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

Students who have participated in MuCoaCo research have come from primarily the Departments of Computer Science, Electrical Engineering, and Industrial and Systems Engineering in the USC Viterbi School of Engineering, the Thornton School of Music, and the Psychology Department. MuCoaCo alumni have garnered awards at USC, nationally, and at international conferences.

My best known early result is the tight deterministic time hierarchy. It says that as soon as we allow a k-tape Turing machine to run a little longer, it can tackle a more di?cult problem. The classical hierarchy result has only guaranteed such a performance increase, when the time was multiplied by a factor of log n.

In Approximation Algorithms, my best results are on “Approximating Maximum Independent Set in Bounded Degree Graphs” with Piotr Berman and “Approximation of k-Set Cover by Semi-Local Optimization” with Rong-chii Duh. With Shiva Kasiviswanathan, I have the fastest approximation algorithm for the permanent of random 0-1 matrices.

On the graph isomorphism problem, I have obtained “Normal Forms for Trivalent Graphs and Graphs of Bounded Valence” with Walter Schnyder and Ernst Specker, I have shown how to do “Graph Isomorphism Testing without Numerics for Graphs of Bounded
Eigenvalue Multiplicity” and most importantly, I have shown the limits of combinatorial methods in “An Optimal Lower Bound on the Number of Variables for Graph Identification” with Jin-Yi Cai and Neil Immerman. On the other hand, I have shown that combinatorial invariants are at least as powerful as any spectral invariants.

On fixed parameter tractable problems, I have by far the fastest algorithm for computing the characteristic polynomial of bounded degree graphs. I also have results on moderately exponential time algorithms for NP-hard problems.

In the area of algebraic algorithms, I have found the fastest integer multiplication algorithm. Still, I would like to further improve its asymptotic running time.

I am interested in machine learning, automated planning, and the intersection of these two areas. I am particularly interested in problems that involve rich relational representations.

The Expressive Intelligence Studio (EIS) at UC Santa Cruz has four affiliated faculty: Michael Mateas, Noah Wardrip-Fruin, Arnav Jhala and Jim Whitehead. We do fundamental technical and design research to enable new kinds of interactive art and entertainment forms. Current research directions in EIS include automatic content generation, interactive storytelling, and advanced game authoring tools.

Automatic content generation. The current state of the art for game development involves the hand-creation of much of the content in a game, such as hard-coded scripts, level designs, models, sounds, and animations. As hardware capabilities and consumer expectations increase, ever larger team-sizes are needed to produce the number and quality of assets required, resulting in ever more expensive production costs. Automatic content generation resolves this production crisis in the games industry; additionally it enables games that dynamically adapt to the specific play styles and goals of individual players as well as making it possible for high-quality games to be produced on much smaller budgets, broadening game creation. Current EIS research in content generation includes generation of quests, based on an analysis of gameplay history, level generation for platform games, and scenario generation driven by learning goals for training games.

Interactive Storytelling. Games have the potential to be an incredibly powerful storytelling medium, combining the cathartic pleasures (and reflection on the human condition) of well-formed stories with the exploratory power and high-agency experience of interaction. Game-based storytelling, however, is still in its infancy. In almost all games and virtual worlds, the story itself does not change as a function of the choices the player makes; there are no game mechanics at the story-level that make the story itself playable. Fundamental research is required to enable the creation of rich, autonomous characters; dynamic, compelling storylines; and intelligent, automatic cinematography—so that character interactions and story arc depend on player actions, yet are presented as compellingly as today’s scripted experiences. EIS research in interactive storytelling builds on the foundations of the award-winning interactive drama Façade (www.interactivestory.net), to create next-generation autonomous characters and drama-management technologies.

Advanced Game Authoring Tools. Game designers currently lack software-based tools to facilitate the design process. While game engineers have game engines and middleware libraries to bootstrap game implementation, and artists have tools to facilitate the creation of 3D models, textures, and animations, designers have no tools that explicitly support their specialized work. In the early stages of professional game design, designers work out the interactions and implications of their game mechanics by building stripped-down playable prototypes, either computationally or with physical materials. But gaining design insights from such prototypes, particularly insights regarding game rule interactions, is notoriously difficult, even for expert designers. Further, there is increasing interest in supporting game development novices in creating game content, including supporting domain experts in creating serious games. Current EIS research in this area includes intelligent game prototyping tools that can reason about first-class models of game mechanics, design brainstorming tools for quest design, and storytelling tools that help novice authors create interactive stories.

General research area: Cognitive, social and computational perspectives on designing and evaluating software for learning, collaboration, and community.

Current focus: Representational affordances in computer supported collaborative learning. Specifically, designing software interfaces to enable learners to construct, discuss, and manipulate representations of their evolving knowledge, and the study of how the notations used in these interfaces affect discourse between learners and learning outcomes.

Uncovering how intersubjective meaning-making can take place through multiple notational tools in computer workspaces, and the roles of language-based and visual/symbolic representations.

Social affordances for online communities, including support for multiple communities and pathways by which participants can discover synergistic value in the larger community.

Prior Activity and Related Interests: Coaching agents, component technology and interoperability (especially at the semantic level), computer mediated communication, networked architectures for collaborative and intelligent learning systems, usability.

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.

Areas: Artificial Intelligence, Intelligent agents, Multiagent systems, Security.

My research group focuses on intelligent agents and multi-agent systems, where large numbers of agents (including software agents, robots and people) may interact. We focus in particular on applying research in multiagent systems to real-world problems and advancing fundamental research to address these needs. As such, we explore the full spectrum of approaches in multiagent systems systems (distributed constraint optimization, Belief-desire-intention or BDI approaches, game theory, distributed markov decision problems), making necessary breakthroughs in the one that best suits the application at hand. Key examples of this use-inspired research include our ARMOR, IRIS and GUARDS systems. While ARMOR is focused on a game-theoretic application for security scheduling at Los Angeles International Airport (LAX), IRIS does game-theoretic scheduling for the FAMS (Federal Air Marshals service) and GUARDS for the TSA (Transportation Security Administration). In this work, we created new algorithms for solving a class of game-theoretic games known as Stackelberg games to address efficiency concerns with existing techniques. Only in overcoming these challenges could we successfully produce a practical application for real-world use. Today, the ARMOR system has received numerous accolades and has been in use at LAX since August 2007. IRIS has been in use since October 2009 and GUARDS is in use at the Pittsburgh airport and at LAX since Fall 2009.

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

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

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

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

For more examples of my work and the types of projects I do, please see my website at www.viscenter.uncc.edu/~rchang. Note that I’m currently with the Charlotte Visualization Center, but will be joining Tufts CS this fall.

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

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

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

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

My recent research focused on learning meaningful data representations from large amount of data, without requiring much human labeling, and apply them to image/video tagging and search. Another direction is building large-scale probabilistic models for analyzing networked data, with applications to collaborative filtering and social network mining.

My research interests are in data privacy.

Post-doc positions are available in the context of the following projects:
1) the development of methodologies and tools for the design and programming of heterogeneous multicore system-on-chip;
2) the design of energy-efficient high-performance networks-on-chip including also networks that leverage emerging silico-photonics technologies; and
3) the design of distributed embedded systems;
Strong industrial collaborations with leading companies are maintained for each of these projects.

Luca Carloni is an associate professor of Computer Science at Columbia University where he leads a research program in the areas of design technologies, computer-aided design, embedded systems, and high-performance computer systems.

Prof. Carloni received the Faculty Early Career Development (CAREER) Award from the National Science Foundation in 2006, was selected as an Alfred P. Sloan Research Fellow in 2008, and received the ONR Young Investigator Award in 2010.

While a PhD student at UC Berkeley he received the Demetri Angelakos Memorial Achievement Award in recognition of altruistic attitude towards fellow graduate students. He is an associate editor of the ACM Transactions in Embedded Computing Systems and the IEEE Transactions on Industrial Informatics and has served in the technical program committee of several conferences. He is currently serving as the technical program co-chair of NOCS’10, EMSOFT’10 and MEMOCODE’10.

For a complete list of publications please see http://www.cs.columbia.edu/~luca

1. Exploiting physical layer capabilities to design clean-slate (as well as 802.11 compatible) MAC and Network protocols. Interference cancellation, OFDM, smart antennas, cross layer rate control, and related topics are of interest.

2. Also interested in mobile social computing, where we are translating mobile phones into a platform for people-centric sensing.

Development of mathematical models for vector and matrix-valued image registration, segmentation and classification etc. Information geometry and its applications in static and dynamic image analysis.

Parallel computing

I work in the areas of computational electronics and quantum transport theory. My group uses a variery of numerical techniques to address the transport of charge and heat in semiconductor nanostructures. Some of our recent projects include:

(1) Electronic and thermal properties of nanostructures. Nanostructured thermoelectrics
(2) Monte Carlo simulation of quantum cascade lasers
(3) Decoherence in nanostructures
(4) Quantum Computing
(5) Global modeling of carrier-field dynamics in conductive media using EMC/FDTD
(6) Flexible electronic systems (e.g. semiconductor membranes and ribbons)
(7) Solid-state-based quantum information processing. Spintronics

For a detailed description of current and recent projects, please visit http://homepages.cae.wisc.edu/~knezevic/

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

My group works on securing cloud computing, enforcing powerful security policies using modern mandatory access control mechanisms, like decentralized information flow control (DIFC)
and SELinux. Our current work focuses on securing the OS from a virtual machine monitor. We welcome people interested in building secure systems.

We are also concerned with efficient and secure concurrent programming. We added transactions to the Linux system call interface and are extending that work and building larger systems
that use that interface.

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



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

More and more computer systems are built as integral parts of many of today’s systems and devices to monitor and control their functions and operations. These embedded/networked systems often operate in environments where safety is a major concern. These computer systems must be highly dependable and timely. My teaching and research focus on building these real-time, embedded systems, and cyber-physical systems. Research topics include: scheduling, formal verification, networking, power-aware techniques, real-time security, and software engineering.

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

Today’s interfaces limit our use of computers in two key ways: 1) they are optimized for short tasks rather than the long term activities we carry out in our everyday lives, 2) they constrain where and how computers can be used by not taking advantage of mobility, context, and natural human communication. Our research in activity-based computing addresses these issues. Our systems-oriented HCI research methodology is guided by an iterative design process that can be broken down into design methods, design tools, infrastructure, evaluation tools, and applications.

We are exploring topics in the above areas by developing applications in three domains: 2nd language learning, green behavior, and health and wellness. We are also interested in the cross cultural issues in designing for these domains.

Our approach is to observe target customers in the field, using both traditional and more novel means (e.g., context-aware ESM), and then use the resulting information as a basis for concept design. Designs are then prototyped and deployed on working devices in an iterative manner. Our current focus is on using activity and location inference technologies running on mobile phones to support new applications that help support customer’s everyday lives. We are also building the tools and design methodologies to support these applications as well as the evaluation methodologies required for deploying and testing these applications in the field over long periods (e.g., 3-6 months).

Postdoctoral researchers can explore topics in these areas in the field with me in Beijing, China for the first year and in Seattle for year two. I am happy to work with postdocs whose expertise might be in a range of different HCI subfields, e.g., technical HCI, field work, or design.

I am interested in extending methods from mathematical analysis (such as measure theory, fractal dimensions, ergodic theory, and Fourier analysis) to make them into suitable tools for attacking problems in the theory of computing, especially structure of complexity classes, algorithmic information theory, data compression, prediction, algorithmic self-assembly, and other inherently spatial models of computation.

robotic manipulation, locomotion, and motion planning and control; multi-agent systems; physical human-robot interaction (pHRI), functional electrical stimulation, and robotic rehabilitation

In general, I’m interested in applying information and computer technologies in solving real-world transportation problems and improving safety, mobility, fuel efficiency, and environmental benefits of various transportation modes. In particular, I’m interested in investigating potential applications of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. I’ve been studying theoretically and with simulations the interactions between transportation systems and communication systems. These efforts are in accordance with the US Department of Transportation’s general agenda in promoting IntelliDrive. Currently we’re studying green driving strategies based on inter-vehicle communications.

I work on the design and analysis of algorithms and data structures for geometric problems, especially those that arise in modeling molecules or terrain. Working in collaboration with biochemists or geographers not only gives practical application of theoretical research, but also gives new theoretical questions. And you constantly learn as you see from the eyes of another discipline.

My research explores hardware and software tradeoffs to improve system performance, system reliability, and system security. The use of digital integrated circuits (ICs) for mission-critical, safety, defense, and communication applications has highlighted the need for enhanced reliability and improved security. As fabrication technology advances, digital ICs will become more vulnerable to soft errors from strikes by ionized particles. However, mitigation strategies that focus only upon devices or logic gates will consume excessive amounts of area and power, and could limit performance. At the same time, there is a constant threat of hardware and software tampering through malicious activity. Inherent architectural vulnerabilities must be removed to reduce the probability of a successful attack. My current work includes: (1) hardware synthesis techniques to mitigate soft errors in the microarchitecture and (2) detection of hardware trojans in fabricated circuits.

My current research interests lie in the intersection of three fields: database; privacy and security; XML and web. More particularly, I’m interested in:
• Information security and privacy
• Healthcare informatics
• Web and information retrieval

My primary interests involves Machine Learning applied to hard problems in computer security, where adversaries are clever, and the data available is overwhelming. The range of problems spans the human aspects of security to advanced polymorhpic shellcode engines to privacy-preserving deep packet inspection.

The IDS website enumerates the ongoing projects. Several new projects are currently under development dealing with software source analytics, embedded device insecurity, and stealthy botnet discovery.

My research aims to improve software quality and programmer productivity by development and refinement of
modularization techniques. I have contributed to design, semantics, and implementation of aspect-oriented
language features, where my main goal has been to improve the regularity, orthogonality, and conceptual
integrity of aspect language features. I have also contributed to improving modular reasoning about
aspect-oriented programs.

Most of my current efforts are directed towards the design and implementation of The Panini language
(http://www.cs.iastate.edu/~design/projects/panini/). The goal of this language is to address problems
with explicit concurrency features such as data races, deadlocks and non-deterministic semantics.
Key idea is to design programming language features that exploit common interaction patterns available
in good, modular software design to expose potential concurrency implicitly and safely. In that sense,
Panini’s design reconciles modularity and concurrency goals.

In the context of the Panini language project many topics need to be explored ranging from language
design, semantics, implementation/optimization, type system, program analysis, and empirical evaluation.

My interests are in the area of computer security and electronic voting.

I am particularly interested in software security and systems security: how can we build secure systems, and how can we be confident that the resulting systems will be secure?

I am also active in the area of electronic voting, including how to build secure voting systems through cryptographic or other means.

I am also interested in how work from other fields of computer science, including program analysis, systems, software engineering, and HCI, can help advance our ability to build secure systems.

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 am interested in a few areas at this time – Agile methods for safety-critical software, software engineering education, open source software, technology for supporting engineering education.

My research is in the broad area of parallel and distributed computing, including cluster computing, grid computing, cloud computing, and volunteer computing. Within this area I focus particularly on questions pertaining to scheduling and resource management, going from theoretical contributions (novel complexity results, novel scheduling algorithms) to practical contributions (system designs and implementations, application deployment). A key technique for conducting quantitative research in this area is simulation of distributed systems. Consequently, some of my research also explores the issues of speed, scale, and accuracy for the simulation of distributed systems. All my recent publications are available on my personal Web page.

The broad theme of my research can be summarized as: “Dealing with network change amidst the inherent operational complexity of modern networks”. Previous and ongoing projects approached this from different perspectives including: specific new network services (e.g., cloud networking), structured approach to network management and operations (e.g., following a declarative language approach to network management), using network virtualization to enable new network management primitives (e.g., router migration and grafting) etc.

Projects at AT&T Labs – Research benefit from interaction with real world network operations as well as data from several large operational networks and services. We also operate and control a small slicable operational testbed that is “embedded” in the AT&T network and forms the platform for much of our experimental work.

My research interests fall at the intersection of nonlinear dynamics, artificial intelligence, and control theory. I am deeply intrigued by the process of modelling: what you can learn — and accomplish — by describing something in the language of mathematics. My students and I have worked on a variety of analysis and synthesis problems whose solutions, at their core, are rooted in the modelling process. The applications involved have ranged from computer architecture to internet attacks, and meltwater ponds on the arctic ice sheets, and the mathematics involved has ranged from topology to time-series analysis, but the underlying questions are the same: what is the right abstraction to use for a given problem, when does it work, how does it fail, and what does all of that enable? Using the right mathematical representations, it becomes obvious, for instance, that computers are often chaotic, that fluids can be mixed by exploiting sensitive dependence on initial conditions, and that denial-of-service attacks can be handled gracefully if one works with their nonlinear nature.

Dynamical systems theory gives us a great way to think about human movement. In collaboration with Jessica Hodgins, we are using this mathematics to model and simulate dance. Along with scientific papers, this collaboration has produced a performance piece entitled “Con/cantation: chaotic variations,” which involves a human dancer and computer animations projected on three 10? by 10? screens. One potential postdoc project would be to explore whether movement — dance and other types — “lives” on some lower-dimensional manifold, and what techniques might be useful in finding that manifold. Another potential postdoc project would focus on the cognitive neuroscience angle of this problem, working with an international group of scientists that is just forming to explore how movement, dynamics, and the perception of beauty interact.

The focus of our AI work is on automating scientific reasoning. In collaboration with software engineers and climate scientists, we have developed computer tools that help geoscientists deduce the ages of landforms from cosmogenic isotope data. This tool is not simply a number cruncher; rather, it uses AI techniques to capture the expert’s knowledge and help him or her make sense of complex relationships in sparse, noisy data. We have just initiated a similar project involving another forensic scientific reasoning problem: the deduction of age models from ice and sediment cores. This would be a particularly appropriate postdoc project for someone whose interests fall at the intersection of computer science and climate science, as well as for someone who is interested in the techniques and ontologies involved in the automation of scientific reasoning.

My research concerns the practical issues in constructing robust, secure, and efficient computer systems — operating systems, P2P, networks, wireless, Internet measurement, and so forth. I see myself as a generalist — I am attracted to biggest problem I can find, regardless of area. We have active early-stage projects in: next generation privacy-preserving peer-to-peer systems as an alternative to cloud computing (BitTyrant, OneSwarm); architectures to improve Internet reliability (consensus routing, Hubble); rethinking the division of labor between the network and end hosts for improved security, flexibility and privacy; programming support for P2P and other wide area distributed systems (SEATTLE, Harmony); planetary scale Internet measurement systems (iPlane and reverse traceroute) and high-bandwidth/MIMO wireless protocols.

The two past post-docs in my group are now on the faculty at UMass and Duke.

My research interest lies in addressing the Internet’s enormous and
growing complexity. For example, the Internet’s network structure,
traffic processing behavior, and failure modes are all highly
complex. Consequently, applications often perform sub-optimally,
network operators often make grave mistakes, and failures are often
hard to diagnose. Addressing the fundamental complexity challenges in
the Internet will require the development of new models that provide
simple yet accurate abstractions, new algorithms that take advantage
of the simplifying models, and new systems that bridge the gap between
theory and practice. Example projects include network coordinates,
network operating system, and network self-verification that aim to
address the structural, behavioral, and failure-mode complexity
challenges.

Cloud computing promises to reduce the cost of IT organizations by allowing them to purchase just as much compute and storage resources as needed, only when needed, and through lower
capital and operational expense stemming from the cloud’s economies of scale. Yet, little is known about the performance of applications in the cloud, the response time variation induced
by network latency, and the scale of applications suited for deployment. Migrating enterprise networks to the cloud is viewed
as one of the major challenges.

The goal of our research is to develop frameworks and methodologies that enable application architects to (i) predict the performance of their applications when deployed in the cloud;
and (ii) plan how the applications should be architected so as to meet the target goals for performance. A particular focus of our work is enterprise applications, which are not only large-scale with complex component interdependencies, but also involve stringent requirements on availability and service latency. A major thrust of
our research is in characterizing real enterprise applications, as there is little understanding of their characteristics.

We are conducting research along various dimensions such as (i) models that estimate the benefits (or costs) of (re)deploying
services in the cloud, given performance and availability constraints; (ii) techniques for analyzing
and predicting the performance of cloud applications while accounting for network latency, throughput and demand variability; and (iii) algorithms to determine how best deploy a given
application among cloud and private compute resources.

We are also developing methodologies for assurable reconfiguration of security policies as enterprises are migrated to the cloud, and exploring ways to specify and configure QoS policies in cloud platforms.

The current projects in the Advanced Operating Systems group at IBM Research aim at exploring innovation on operating systems and hypervisors to:
(1) advance dynamic resource management in cloud computing environments;
(2) advance support for problem determination;
(3) advance system software support for analytical applications;
(4) leverage high performance computing platforms to advance large-scale commercial applications.

Please contact me for details.

Machine Learning, theory and applications: Interactive learning, causal filtering, unsupervised learning, cluster analysis. Information theory. Foundations of learning theory and statistical mechanics. Quantitative finance (econophysics). Robotics. Neuroinformatics. Analysis of animal vocalizations.

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.

Safety-critical, dependable &/or reliable software systems and product lines, evolving requirements, defect analysis, monitoring and diagnostics, compositional verification, automated requirements engineering

My primary interests are in the relations between cryptographic primitives and assumptions. I also have interests in new types of attacks the present themselves due to the changing nature of ubiquitous computing, and its infrastructure. Finally, I am interested in the use of cryptography (specifically applied forms of secure multi-party computing) in security heath care records and wearable sensors.