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

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.

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.

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.

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.

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.

Seeking postdoctoral fellow to work on predictive dynamic models of language and nonverbal behavior and integrating them into Embodied Conversational Agents. The candidate should have a background in Computational Linguistics (NLG, Dialogue); should be willing to do significant reading into current approaches (both symbolic and probabalistic) to generating dialogue, should be capable of incorporating recent linguistic and psychological insights into the dyadic, dynamic nature of dialogue, and the integral role played by eye gaze, gesture, posture; and should be willing and able, therefore, to develop more innovative and adequate approaches to generating dialogue. The result will be a radically new way of generating embodied dialogue moves, for an embodied agent that can be more tightly integrated into collaborative scenarios with humans. Applications will include embodied peers that teach literacy and scientific reasoning behaviors, embodied peers that scaffold social behavior in children with autism, and embodied agents that give directions in real and virtual environments.

Professor Cassell is currently faculty at Northwestern University, but will be joining the Carnegie Mellon Human-Computer Interaction Institute in Fall, and so the postdoctoral candidate would be located at CMU HCII in Pittsburgh, with access to colleagues in Natural Language Technologies, Machine Learning, and Graphics.

I recommend you go to the Healthcare Robotics Lab website (http://healthcare-robotics.com) to learn more about my lab’s research. There are many opportunities for postdoctoral researchers to make high-impact contributions. If you have any questions, please do not hesitate to contact me.

My lab’s research seeks to advance the capabilities of real robots so that they can provide valued assistance to people in the context of healthcare. Specifically, we work with semi-autonomous mobile robots that physically manipulate the world (mobile manipulators). Our current efforts include research into human-robot interaction, autonomous mobile manipulation, machine perception, machine learning, and haptics.

The following three large-scale efforts are especially exciting to us and we would welcome a postdoc to become involved:

1) Assistive Mobile Manipulation for Older Adults at Home: This is the PR2 Beta Program project at Georgia Tech for which I’m the principal investigator. It involves several initiatives with the overall goal of providing assistance to older adults in their homes. Our PR2 is currently in the Aware Home, where it will be interacting with older adults this summer (2011).

2) We have a new project that is not yet public that is very exciting in terms of helping people with severe motor impairments. Please contact me for details.

3) Haptically-guided Manipulation within Extreme Clutter: We are investigating the possibility of a new foundation for robot manipulation using compliant actuation and whole-body skin. This project is also new and not much is public, so please contact me for details.

My lab is multi-disciplinary with members from computer science, biomedical engineering, electrical engineering, and mechanical engineering. We are also collaborative. We are active in the Robotics PhD program and the Center for Robotics and Intelligent Machines (RIM@GT), and we work closely with other labs on campus, including the Human Factors and Aging Lab in the School of Psychology and the Socially Intelligent Machines Lab in the College of Computing. Although much of our research has a long time horizon, we actively engage industry and look for opportunities to make an impact.

We have several state-of-the-art robots, including a PR2 from Willow Garage, the custom humanoid robot Cody, and a variety of smaller assistive robots that we have developed. By the end of the summer (2011), we also expect to have an additional sophisticated human-scale mobile manipulator. Our other facilities include rapid prototyping equipment (3D printer and laser cutter), a machine shop, and a force and motion capture studio.

Best of luck with your application.

- Charlie

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-bcf.usc.edu/~mucoaco — and the MuCoaCo blog — http://mucoaco.blogspot.com — contain detailed project descriptions, video demonstrations, and lists of publications and awards. 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.

MuCoaCo affiliates have had backgrounds in Computer Science, Electrical Engineering, Industrial and Systems Engineering, Music Composition, Music Performance, Neuroscience, and Psychology.

The Linguistic and Assistive Technologies Laboratory (LATLab) at The City University of New York (CUNY), Queens College, conducts research in computational linguistics and human-computer interaction with a primary focus on accessibility applications and assistive technology for people with disabilities. In particular, we study the design of computer technology to address language and literacy impairments of people with disabilities – with a focus on two groups: people who have intellectual disabilities (ID) and people who are deaf.

Animations of American Sign Language for People who are Deaf:

Our research at CUNY has focused on sign language animation technologies for people who are deaf. American Sign Language (ASL) is a primary means of communication for one-half million people in the U.S, and it is a distinct language from English – with its own unique word-order, grammar, and vocabulary. A majority of deaf high school graduates in the U.S. have only a fourth-grade English reading level or below, yet many of these adults have sophisticated fluency in ASL. Therefore, software that can present information in the form of ASL animations would improve these individuals’ access to websites, communication, and information. Using our motion-capture recording studio, we are collecting a corpus of ASL performances, linguistically annotating them (with the help of a team of native ASL signers), and analyzing this data to create models to underlie ASL animation technologies (to make them more natural-moving and understandable for deaf viewers). We conduct experimental studies at the laboratory on a regular basis in which native ASL signers evaluate animations synthesized using alternative models. We have studies models of speed/timing, use of signing space, verb inflection, and we are beginning a project on facial expression in ASL. We are also interested in issues relating to linguistic generation of ASL and English-to-ASL machine translation. Further, we are interested in studying other forms of signing communication, including various forms of Signed English used by people who are deaf or hard-of-hearing in the U.S.

Text Readability Detection / Text Simplification for People with Intellectual Disabilities:

Many people with ID have limited English literacy and could benefit from software that could help them automatically identify information sources or websites that are at an appropriate level of difficulty or that could automatically simplify complex texts. Based on linguistic features of a text that can be automatically calculated through NLP software (the part-of-speech of different words, the syntactic parse-tree of the sentences, etc.), we are designing software to assign a difficulty score to a text to indicate whether it would be accessible for an adult with ID to read. We are also experimenting with various experimental designs to gather ground-truth data about how difficult-to-read texts are for these users; this is non-trivial because it can be difficult for these users to participate in a traditional comprehension experiments.

Other Projects:

We are interested in other projects related to applications of computational linguistics or human computer interaction to accessibility or assistive technology for people with disabilities.

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

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

I am working at the interface of control engineering and computer systems. Within control engineering, I work on modeling, control, and diagnosis of discrete event systems, a class of dynamic systems with discrete state spaces and event-driven dynamics. Finite-state automata and Petri nets are two popular modeling formalisms used in discrete event systems. My interests overlap with the emerging area of Cyber-Physical Systems (CPS).

Recently, we have applied discrete control techniques to address the problem of deadlock avoidance in concurrent software. Specifically, we model multithreaded C programs using Petri nets and then synthesize control logic that is used to instrument the program; the instrumented program is guaranteed to be deadlock-free. We also have a project on synthesis of safe control strategies for collision avoidance in intelligent transportation systems. Please consult the “Research” section of my website for further details.

I am interested in exploring applications of discrete control theory to computer systems, including but not restricted to concurrent software and security, as well as control problems in CPS, with a post-doctoral researcher.

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

We are active in software development for neural simulation and electrophysiological data acquisition tools. Beyond software development we use these tools for neural simulation and electrophysiogical data analysis projects. There are a host of individual projects available ranging from Labview programming for data acquisition to generating neural database software to analyzing neural and simulation data, to executing neural simulations. We are in particular interested to incorporate new platform-independent .xml model description standards in our work based on international initiatives such as NeuronML abd MorphML. One area of exploration is to also codify electrophysiological data with a standardized markup language.

My research interests include
a) Automatic speech recognition: I work on all apsects of automatic speech recognition and speech-based biometrics. I am also one of the project leads for the popular CMU Sphinx open-source suite of speech tools. I also work on noise-robustness, multi-channel speech processing, and estimation of paralinguistic and contextual cues in speech. I am also interested in retrieval of spoken data and retrieval with spoken queries.

b) Computer audition: My interest lies in helping a computer understand the world through sound. This includes aspects of sound classification, source separation, and various decomposition techniques, but also higher-level inferences such as context/location determination (e.g. have we heard this guy before, and “where was this recorded and what else was happening”).

c) Multimedia analysis: This relates to (b) above, and includes image analyses and determination of audio-video association, semantic associations, and retrieval.

d) Security and privacy: Speech is probably the most popular form of communication between humans. The majority of the *private* communication in this world has also probably done through speech. Yet, in the large literature on techniques for privacy and security speech remains largely unaddressed. In this project I work on developing privacy-preserving techniques for speech processing. My group’s work includes aspects of signal processing, secure function evaluation and cryptography.

My research and educational career straddles the border between computational theory and mechatronic engineering. Motivated by applications in confined spaces, my group pursues a comprehensive program in mechanism design, path planning, motion planning, and estimation. These research topics are important because once the robot is built (design), it must decide where to go (path planning), determine how to get there (motion planning), and use feedback to close the loop (estimation). Already, we have directly applied this body of work to challenging and strategically significant problems in diverse areas such as surgery, manufacturing, infrastructure inspection, and search and rescue.

Many of the research fundamentals support the development of snake robots, highly articulated mechanisms that can thread through tightly packed spaces reaching locations that people and conventional machinery otherwise cannot. We have developed snake robots for minimally invasive cardiac surgery; recently, we completed our first in human procedure. Current work includes further mechanism development for natural orifice surgery, and prescribing estimation/filtering approaches, based on Kalman and Bayes filtering, to map the internals of the body, i.e., it is SLAM on the inside.

We are also addressing the motion planning of snake robots and all underactuated systems. Our approach takes recourse to the fundamentals, drawing from advanced concepts in differential geometry to prescribe gaits. Examples of results in this work are applying Stokes Theorem to the local form of the connection on shape spaces to efficiently design gaits. Recently, we have shown that many biological systems, including fish and lizards, can be modeled this way.

By taking recourse to the fundamentals, we have been able to address other problems such as multi-agent planning. Recently, we have developed an efficient provably complete optimal multi-agent path planner that can plan paths for 40+ robots in large spaces (note that the size of the configuration space makes it impossible for A* to even search one step, let alone complete a path). We have also developed an algorithm for multi-agent manipulation. Current work includes applying these techniques to distributed manufacturing. We are also working with a biologist to use these concepts to model swarms.

It is the excitement of working with students that continues to draw me to academia. I am certain that a casual tour of my lab reveals a feeling of energy and productivity. My students, both graduate and undergraduate, work hard to provide fresh new insights within the framework of mathematical and experimental rigor endowed by my research program.

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

My group focuses on verification of embedded and cyber-physical systems, studying the question “how can we build computerized controllers for physical systems that are guaranteed to meet their design goals?” These questions are of crucial importance in many areas, including automotive, aeronautics, railway, mobile robotics, factory automation, and medical devices. After all, our society cannot afford to have these systems malfunction. In our research, we have developed powerful logic-based verification techniques that help producing reliable complex systems, e.g., in aeronautical, railway, and automotive applications. We have developed KeYmaera, the first theorem prover for hybrid systems. We also developed the first verification technique for distributed hybrid systems, which combine the challenges of hybrid systems with those of distributed systems. We have further developed the first logic and compositional verification technique for stochastic hybrid systems. My grou p also works on statistical model checking techniques. There are plenty of exciting challenges ahead in these and related directions of research.

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

Sue Kase recently did a PhD using a parallel GA on the TeraGrid to optimise the fit of a cognitive model. The data was for stressed and non-stressed and caffeinated and non-caffeinated subjects. The changes in the parameters told us how cognition changed between these groups (sometimes called docking), and her work shows that this approach can be productive but computationally expensive (http://acs.ist.psu.edu/papers/ritterKKBS09.pdf). This is an open area, with more data, more models, and more algorithms possible. We are also creating 1000-node cognitive model networks, and it should be an exciting lab for those interested in these areas of computation, intelligence, and cognition.

The Bioinformatics and Intelligent Systems Lab at USM focuses on evolutionary computation and other artificial intelligence approaches as applied primarily to bioinformatics and intelligent agents.

Bioinformatics projects include:

GAMI, a genetic algorithms approach to DNA motif inference: In this project we identify patterns in noncoding DNA that have been conserved across evolutionary time; such elements are good candidates for affecting the function of genes and will be studied at the bench by our biological collaborators. This project is motivated by a study of CFTR, the gene for cystic fibrosis and has expanded to other environmental-response genes; among GAMI’s strengths is that it is designed to work with long sequences (e.g., 100k) and many of them (e.g., 100 or more) There are many subprojects here; one particularly important horizon is that functional elements in noncoding DNA tend to appear in modules (not in isolation), and we will design a new system to infer these.

Gaphyl, a genetic algorithms approach to phylogenetics, the inference of trees representing the evolutionary relationships among species or strains. We are just starting a swine flu project, which will also mean designing a new computational approach designed to work specifically with viruses.

My bioinformatics work is collaborative with researchers at the Mount Desert Island Biological Lab, Dartmouth College, the University of Maine (Orono), and the University of Illinois.

Intelligent agents:

Our recent intelligent agents work has focused on game-playing agents. We won first place in the 2008 Ms. PacMan competition at the World Congress on Computational Intelligence (Hong Kong), the 2009 Unreal Tournament competition at the Congress on Evolutionary Computation (Trondheim, Norway), and in both the Learning Track and Gameplay Track of the 2010 Computational Intelligence and Games Conference (Copenhagen, Denmark). Designing agents for interactive dynamic games is a very similar research problem to real-time robotics, where inputs must quickly be processed to determine appropriate outputs, and split-second decisions must be made. Our approach is adaptive rule-based systems.

I am also working with Jim Wilson and others at the University of Maine on a Complex Adaptive Systems project, using learning classifier systems to model cooperation and communication in the lobster, sea urchin, and groundfish fisheries.

Other projects include:

Lobster CyberCatch, an educational science game designed to teach middle-school students about math and science concepts in general and lobsters (and lobstering) more specifically.

VIEWER, a project centered at the University of Maine (Orono) to develop touch-screen visualization walls for remote conferencing for scientific collaborations, including informal “water cooler” conversations.

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

We have several interesting projects in computational science education that could use the efforts of a CIfellow to develop effective learning materials across the sciences and math, and with the use of parallel computing in the process.

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

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

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

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

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

My group works in two areas: Computational Structural Biology and Systems Biology. Our work in structural biology focuses on physics-based methods for modeling, simulating, designing, and analyzing biomolecular interactions. Specific applications include protein and drug design. Most of this work involves probabilistic graphical models. Our work in systems biology focuses on clinical applications in collaboration with physicians at University of Pittsburgh Medical Center and the Hillman Cancer Center. Specific applications include the development of statistical and mechanistic models of disease processes.

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

Applications-driven research in ubiquitous computing, with particular emphasis on home and health domains. I have a personal passion for research connected with autism. I have also been very successful at mentoring students interested in pursuing academic research careers.

My research involves the design, analysis, and practical realization in software of combinatorial algorithms for problems in the sciences and engineering. Recent work has involved: Algorithms and software for variant graph coloring problems to enable Automatic Differentiation; exact and approximation algorithms for matchings (for applications in scientific computing) and edge cover (to analyze flow cytometry data and identify leukemia); and the design of multithreaded combinatorial algorithms on emerging many-core architectures. My work is based in algorithmic theory, but is motivated by practical problems, and leads to software for massive graphs and data sets.

Active in the areas of next generation Internet architecture, Unmanned Aircraft Systems, wireless networking, network security, optical networking, traffic management and quality of service in data networks. A distinguishing factor of my research is its relevance to the Industry. Have actively participated in industry forums like RTCA, WiMAX Forum, IEEE Standards group, ATM Forum and Internet Engineering Task Force and have made over 200 contributions that ensured that my research was implemented and not just published as papers. My research has influenced the directions of Traffic Management and Testing working groups of ATM Forum. Rank among the top 50 in Citeseer’s list of Most Cited Authors in Computer Science. Google Scholar lists over 12500+ citations to my publications. Am a co-inventor of the DECbit scheme, which has been implemented in various forms in DECnet, OSI, Frame Relay, and ATM Networks, and TCP/IP networks (ECN).

Social and psychological aspects of human-computer interaction and computer-mediated communication in a variety of media interfaces, ranging from websites and social media to mobile devices and robots. Current focus is on studying cognitive processing of online information as a function of four classes of technological affordances (Modality, Agency, Interactivity, and Navigability), as predicted by MAIN Model, Interactivity Effects Model, and the Agency Model of Customization. Laboratory-based research emphasizing theoretical contribution to our understanding of how interfaces affect user psychology, with implications for design of future interfaces.

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

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

Our goal at the Systems Research Laboratory (SyLab) is to conduct research in the area of Operating Systems. While we are interested in all problems related to Systems, lately, we have been developing new capabilities for storage systems and virtualized data centers. Some of the capabilities that we have developed recently address energy, performance, and self-management for storage systems and resource isolation and performance guarantees within virtualized systems.

More information about our work can be obtained at:

http://sylab.cs.fiu.edu/

My interests lie broadly in the computer networking and distributed systems areas. My recent focus has been systems research in wireless sensor networks, with emphasis in wireless radio channel measurement and modeling, link quality estimation, routing algorithms, topology control, and programming models. I am also interested in Internet protocols and operating systems issues. In the past, I have been involved in active networking, mobile IP, and protocol design and verification research. My group develops and deploys sensor networks that address some of the grand challenges in science and engineering, including new distributed sensors for solar radiation mapping, energy and occupancy monitoring and control in smart buildings and wearable sensors for exercise physiology monitoring and modeling, among others.

We study the design and fabrication of nanostructures as applied specifically to the fabrication of future computing and sensor systems: devices-to-computer architecture. The terms ‘nanocomputing’ or ‘molecular computing’ refer to the fabrication techniques (e.g., self-assembly) that have the potential to create devices with critical dimensions near the molecular scale (i.e., < 10nm). However, defects introduced during self-assembly require a change in the way we design and build these systems.

Self-assembly is a bottom-up fabrication technique that can be used to achieve molecular scale resolution. The goal is to use these structures to integrate active nanoelectronic devices into a fully self-assembled circuit technology – and to study the new forms of computer architecture that the technology enables. To do this we have adopted a broad and vertical research approach to cover topics in the synthesis and design of DNA nanostructures, nanoscale device and circuit modeling, and studies of emerging computer architectures.

Work in this area requires a deep interest in pushing the frontiers of computing and a traditional background in hardware/software design, computer architecture, or systems. Techniques specific to DNA nanotechnology and the "device side" are acquired here through hands-on laboratory work and analysis.

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

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.

***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.

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.

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.

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 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.

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 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 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.

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.

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.

I am interested in changing what we teach and how we teach. In large (300+) classes to small seminars. From CS1 and CS2 to a course that’s IP^2 = internet protocol X intellectual property for non-majors. I’d like to design courses and modules around problems first, techniques and skills seconds. Help change education at Duke and then across the country with the roll out/pilot of a new AP course being developed by the College Board with sponsorship from NSF. Problems are the heart of all these initiatives, but developing and using problems is a complex though rewarding undertaking

Titles of current projects:
* Robustness of Simulations in Model Based Design Environments
* Robust Testing for System Validation
* Temporal and Modal Logics for Dynamical and Hybrid Systems
* Hybrid System Synthesis from High Level Specifications
* Task and Motion Planning for Mobile Robots
* Natural Language Interfaces for Robotics

Jodi Forlizzi is an Associate Professor of Design and Human-Computer Interaction and the A. Nico Habermann Chair of Computer Science at Carnegie Mellon University, in Pittsburgh, PA. She is an interaction designer contributing to design theory and practice. Her theoretical research examines theories of experience, emotion, and social product use as they relate to interaction design. Other research and practice centers on notification systems ranging from peripheral displays to embodied robots, with a special focus on the social behavior evoked by these systems.

My research group works primarily in the area of neural computation.  We are especially interested in how intelligence and behavior emerge/self-organize in large scale brain models.  Most helpful for a postdoc in this context would be a person with a strong background in neural computation and an enthusiasm for working on cross-disciplinary research.

I am interested in machine learning and data mining for complex and large-scale scientific data, motivated by real-world application data and close collaborations with domain scientists. Recently, I am most active in ML problems related to networks — structure inference, prediction of network function or behavior, network dynamics, and so on. These studies have motivated me to examine the intersection of ML, statistics, and topology. But I also have been interested in decision theory (reinforcement learning and planning), user modeling, representation, and relational learning. I am not a theorist, but I do believe in the importance of mathematical foundations in the design of learning algorithms.

On the application front, I feel that many, if not most, important problems in ML have been motivated by interactions with new, complicated forms of data. I enjoy collaborations with scientists in disciplines including genomics, immunology, neuroscience, geology, computational chemistry, computer security, and behavior modeling. My goal is to help these groups advance their own fields, while developing novel ML methods and methodologies.

For more information on my research program, please check out my web page:

http://www.cs.unm.edu/~terran/

I have also recently become interested in the emergence and growth of complexity in systems like evolution or economics. While these systems bear some similarity to learning systems (in the sense of extracting information from the environment, encoding/compressing it, and using the new representation to improve performance in the future), they also exhibit important behaviors beyond those of traditional machine learning systems. For example, while all ML algorithms that I know of hit some eventual performance asymptote, these “natural learning systems” never have. I am interested in exploring the ties between these phenomena, toward developing unbounded learning systems.

My research interests include many aspects of file systems and storage. Ongoing and recent projects have explored archival storage systems, metadata and file system search, file system reliability and security, highly scalable storage systems for HPC, and the use of new memory technologies in the storage stack. My broader interests include distributed systems issues in operating systems and security.

UC Santa Cruz is a member of the newly-formed Center for Research in Intelligent Storage (CRIS), which is an NSF and industry-funded industry-university cooperative research center (I/UCRC). This center will strengthen existing ties that the UCSC Storage Systems Research Center has with industry in Silicon Valley (45 minutes away) and elsewhere, providing strong collaboration opportunities with cutting edge researchers in industry.

My research interests center on tools and techniques for building, understanding, and ensuring reliable computational systems. Much of my work centers on finding novel ways of interacting with the programmer to design more precise and practical program analyses to produce more effective programming systems.

The Xisa project is an instance of this approach that infers precise properties of complex data structure manipulations. The novelty of Xisa is that it extracts both the necessary invariants and reasoning rules from executable assertions. This approach allows the developer to focus the analysis to the properties of interest and without using a separate formalism for testing and static analysis.

Another project called Mix looks at an incremental combination of analyses with different precision, such as type checking with symbolic execution. The observation is that with user interaction, an analysis need not prove all the desired properties at once.

Have you ever felt a compiler/analysis tool misunderstood what you meant in your program? The Gradual Programming project examines this disconnect between the intent of a software developer and the meaning of the program according to the language semantics. In the end, we envision a programming system where the developer and tools interact to agree upon the precise meaning of a program.

My research interests lie in quantum computation and in quantum information, i.e., in quantum information science (QIS). I have published extensively in this field, including three books, with a fourth to appear this summer. I have written papers on such research topics as for example, quantum algorithms, quantum entanglement, quantum cryptography, quantum knots. I am especially interested in interdisciplinary research at the interface between QIS, mathematics, and physics. For more information, please refer to my webpage: www.csee.umbc.edu/~lomonaco

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

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

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

To achieve truly life-like telepresence, we must better understand key multimedia acquisition, processing, and display technologies. These include video segmentation and matting for background replacement and for gesture-based human-computer interaction, depth sensing, augmented reality, 3D rendering, and view synthesis. We must also understand how to embed these technologies in real-time systems to create seamless and highly intuitive interaction experiences.

performance modeling of computer systems; queueing theory; stochastic processes; stochastic/Markov modeling; stochastic scheduling in queues; SRPT; FB; resource allocation/ capacity planning; task assignment in server farms; power management; scheduling for supercomputing centers

My group works on modelling molecular systems that process information
through sensing, computing, and motion; our focus has been on
catalytic DNA systems, such as logic gates, circuits, and nanorobots.
We are interested in both formal and quantitative models. We
collaborate with experimentalists at UNM and other institutions. We
are also interested in programming languages (design and
implementation, molecular or not, especially of functional languages),
algebraic aspects of fluid mixing, and sensor array data analysis.

My research interests are in the application of formal methods to system-level design. Specifically, development of semantics and language-based techniques for representing, synthesizing and verifying complex, heterogeneous systems. Of specific interest is investigating system-level impacts of security requirements on high-assurance systems, and techniques for synthesizing such systems from specifications. My current applications include defining a coalgebraic semantics for the Rosetta specification language and using Rosetta to specify, synthesize and verify elements of high-assurance operating systems, software defined radios, and smart grid components.

My group carries out applied experimental systems research in Operating Systems, Computer Networks, and Distributed Systems. Most current projects in our group deal with various aspects of virtualization (as with system virtual machines), data center networking, and wireless networks. Each project invariably involves the design, implementation, and evaluation of a real-world system prototype. Some of our past projects are listed at http://osnet.cs.binghamton.edu/ . Current projects in our lab are funded by the following active NSF grants : “CAREER: Coordination Mechanisms for Performance-aware Virtualization in Clusters”, “A Miniaturized Robotic Testbed for Development, Testing, and Evaluation of Protocols for Multi-Hop Wireless Networks”, “Virtualized Cluster Testbed to Support Research In Large Memory And Data Intensive Applications”.

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

My principal research interests are in the area of software engineering, particularly in software development processes and methodologies, software design, and software measurement. My best known work is the goal-question-metric approach to software measurement, my work on the modular structure of software systems, and my work in software product-line engineering as a co-inventor of the Synthesis process, and its successor the FAST process.

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

Theory and algorithms for machine learning. Past and ongoing topics include online learning (including in active and unsupervised settings), active learning, and privacy-preserving machine learning. We are also working on new applications of machine learning, for example, to climate science.

We are looking for postdocs in machine learning, theory, Climate Informatics, privacy/security, or related areas.

We focus on creating tools to better capture and share visual information. The goal is to create an entirely new class of imaging platforms that have an understanding of the world that far exceeds human ability and produce meaningful abstractions that are well within human comprehensibility.

The group conducts multi-disciplinary research in modern optics, sensors, illumination, actuators, probes and software processing. This work ranges from creating novel feature-revealing computational cameras and new lightweight medical imaging mechanisms, to facilitating positive social impact via the next billion personalized cameras.

With more than a billion people now using networked, mobile cameras, we are seeing a rapid evolution in activities based on visual exchange. The capture and analysis of visual information plays an important role in photography, art, medical imaging, tele-presence, worker safety, scene understanding and robotics. But current computational approaches analyze images from cameras that have only limited abilities. Our goal is to go beyond post-capture software methods and exploit unusual optics, modern sensors, programmable illumination, and bio-inspired processing to decompose sensed values into perceptually critical elements. A significant enhancement in the next billion cameras to support scene analysis, and mechanisms for superior metadata tagging for effective sharing will bring about a revolution in visual communication.

Developing and evaluating educational games
Promoting creativity in computer science education
providing intelligent support for learning
Developing and evaluating data-driven methods for intelligent learning support
Developing and evaluating games for health and exercise promotion
involving undergraduates in research
broadening participation in computing
evaluating BPC initiatives
developing games and software for kids to learn computing

I’m a glaciologist who studies various parts of ice sheets with numerical models. These models require solutions of various PDEs on various platforms. One of my interests is a development of large-scale ice-sheet model as a part of a Global Climate Mode.

http://www.princeton.edu/aos/people/research_staff/sergienko/index.xml

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

Olin provides a unique opportunity to explore contextualized computing/engineering education with a strong design component. My research interests are broad within AI (most recently semantic web/web 2.0 but also including cognitive robotics, HCI, knowledge representation and reasoning, programming languages). I spent ten years on the faculty at MIT before leaving to help found Olin; I would like to share what I’ve learned in both of these contexts. A CI Fellow working with me would have the opportunity to pursue both a research agenda and explorations into curricular/pedagogic innovations, working in a close-knit and collegial community with regular conversation about learning, contextualization, usability. Current and new projects focus on the role of sketching/drawing, mobile devices/social networks, support for aging and/or cognitively challenged populations, and computational thinking/small footprint curricula; collaborations through Olin’s new Initiative for Innovation in Engineering Education would also be encouraged.

The research thrust of the Nano-Computing Research Group, in the department of Electrical Engineering, lies predominantly in modeling, synthesizing, and harnessing the computing promises and logical aspects of innovative nano-devices. Technologies such as sub-45nm CMOS, and Magnetic Logic and memory devices, several flavors of Quantum Cellular Automata (QCA), to name a few, are systems that operate in a novel manner and could have substantial impact on the computing world. We at the NCRG see a significant need to investigate such devices, and therefore, endeavor into several key areas throughout the design automation process, such as model creation, low-high level architecture design, the fabrication of novel nano-devices, and the experimental characterization of those nano-devices. The theoretical foundation of our research framework is graph-based probabilistic models like Bayesian Networks, representing the dependencies in logic that characterizes the physical behavior of the device. We extensively use E-Beam lithography to fabricate our devices and analyze them using AFM, at our University’s Nano-Materials and Nano-Science Research Center (NNRC).

computational psycholinguistics; computational models of language production and language acquisition; incrementality; Bayesian models of language adaptation (implicit learning); bounded rationality; optimality in language processing; cue integration and maintenance; social network theory and language change

My current research focuses on stimulating the human senses in making virtual experiences more realistic. I am also interested in the topic of effective interaction techniques in immersive virtual environments. I am director of the Human Interaction in Virtual Environments (HIVE) Lab, where we are focusing on the use of vibrotactile feedback for various applications in virtual environments, simulation, and real environments.

semantic web, computing for human experience (specifically active perception, ambient intelligence), relationship/event web, semantic sensor web (including citizen sensors-participatory sensing, multilevel sensing, semantic applications to mobile web, trust issue, situational awareness), semantics-enabled social computing (esp. analysis of user generated content), very large RDF data management, provenance, complex entity and relationship extraction from biomedical literature, ontologies and folksonomies (esp. dynamic domain model creation from Wikipedia), spatio-temporal-thematic analysis, semantic web services and smart mashups, semantics-enabled networking and cloud interoperability, semantic visualization, collaboration with biomedicine and cognitive science

Research: Our lab researches how sociotechnical interventions affect personal health and well-being. To this end, we design interventions with the target population, develop prototypes, and evaluate the systems in their natural operating environments. Our research motivations are two fold – we want to empower all people, independent of skills and resources, with the ability to improve their health and wellness while assisting researchers in other disciplines study new, technical interventions. Our projects include looking at innovative mechanisms for collecting personal health information (ICT applications, ambient, wearable displays, games), viewing longitudinal personal health information through Personal Health Records, and understanding how this data will be shared with healthcare providers through Electronic Medical Records. More information is available here: http://www.wiilab.org/.

Teaching: From an education point of view, I investigate what it means to be a person in computing and how this idea changes when students are introduced to health and wellness informatics. I aim to provide students with a true interdisciplinary and transdisciplinary research experience in my health informatics classes. One health informatics themed course is a first year undergraduate engineering projects class that has students from all engineering disciplines working together on a semester long project. Another class I am redesigning to coordinate it with a HIT course taught in the medical school. The redesigned course would pair computing undergraduates and graduate students with medical students to learn about the different areas of health informatics and work on a semester project addressing a real problem in healthcare.

Service: I am actively involved in increasing diversity in computing (the “other” selection for Broad Research Areas). My service objectives are to (1) educate students, educators, and parents about the exciting opportunities in computing – especially in transdisciplinary fields and (2) share best practices with others in computing to increase the number of people we can reach. To this end, I have co-developed two successful Roadshow outreach programs and co-organized workshops to help universities create roadshows. I have worked with the Computer Science Teachers Association to help widely disseminate best roadshow practices. More information is available here: HTTP://CSTA.ACM.ORG/COMMUNICATIONS/SUB/VIDEOS.HTML.

You: If you are interested in working with my group, please email me your CV. It would be great to hear how you think our interests could align. Although experience in health informatics is not necessary, you should be willing to work with interdisciplinary, health-related teams.

Biography: Katie A. Siek is an assistant professor in Computer Science at the University of Colorado at Boulder where she leads the Wellness Innovation and Interaction Lab. Her primary research interests are in human computer interaction, health informatics, and ubiquitous computing. Her paper about the barriers experienced by low socioeconomic single caregivers to make behavioral dietary change was nominated for best paper at Pervasive Healthcare 2009.  Her research is supported by the National Institutes of Health, the Robert Wood Johnson Foundation, and the National Science Foundation including a five-year NSF CAREER award. Most recently, she received a Scottish Informatics and Computer Science Alliance Distinguished Visiting Fellowship. Prior to her appointment at Colorado, she completed her Ph.D. and M.S. at Indiana University – Bloomington in computer science and her B.S. in computer science at Eckerd College. She was a National Physical Science Consortium Fellow and was a Ford Apprentice Scholar at Eckerd College. Siek is a member of the ACM-W Council and on the College Board AP Computing Advisory Group. (More information: http://www.cs.colorado.edu/~ksiek)

I have worked in the area of high performance for over 10 years with an additional 4 years in the area of art, humanities and computing. I am interested in bridging these areas by using the arts and popular culture to increase digital literacy in the in under served populations.

My broad research interests are in speech and language processing, with a slant toward combining statistical and machine learning techniques with knowledge about language structure from linguistics and speech science.

My recent work involves several topics:

- Models of the articulators (lips, tongue, etc.) for speech recognition (especially for pronunciation modeling in conversational speech), implemented as graphical models.

- Audio-visual speech recognition, using dynamic Bayesian networks to account for the apparent asynchrony between the audio and visual streams.

- Multi-view learning of feature spaces for various speech tasks, using additional views at training time such as video or articulatory measurements.

- Additional modalities in speech processing besides audio and video. For example, one promising source of information appears to be from ultrasonic “microphones”, in which the motion of the articulators is detected via a Doppler shift in a reflected ultrasonic signal.

In addition to these, I am also interested in exploring similarity-based techniques for speech applications and model-based speech processing (such as denoising or reconstruction of degraded signals).

A note about TTI-Chicago: It is an independent institute dedicated to basic research and graduate education in computer science. We are located on the U. of Chicago campus, but are an independent institute with our own PhD program. The main current areas of research at TTI are machine learning, artificial intelligence and related applications (vision, robotics, speech, natural language), theory of computation, programming languages, computational biology, and scientific computing.

The Community Grids Laboratory, led by Prof. Geoffrey Fox, is looking for researchers to lead projects in Cloud computing (both systems design and large scale parallel application development), parallel computing, message-oriented middleware, and experience with streaming systems of all types (including sensor Webs and audio-video systems).

For a snapshot of lab activities, see our presentations and publications at http://grids.ucs.indiana.edu/ptliupages/presentations and http://grids.ucs.indiana.edu/ptliupages/publications/. We have a distinguished publication and funding record in parallel computing, Cloud computing, the application of Web technologies to scientific problems, and publish/subscribe systems.

The Community Grids Lab is part of the recently refunded Pervasive Technology Institute. We work closely with the Indiana University Research Technologies division, which provides world-class scientific computing infrastructure. Lab facilities are located in the IU’s new Incubator facility.

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

Interested in against-conventional-wisdom approaches to solve the most challenging problems in computer architecture. Currently am working on a novel execution model to program sequentially (i.e.,deterministic execution with no synchronization constructs), but run programs in parallel, on stock hardware. If you are interested in going against the tide of conventional wisdom in attacking one of the most challenging problems facing the computer industry, consider coming to Wisconsin.

Topic: Industrial Strength Cloud Services Composition

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

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

My research focuses mainly on enabling the efficient and reliable execution of scientific workflows in distributed environments. In my group at ISI, we have been working with a number researchers in variety of science domains, including astronomy, biology, earthquake science, physics, and others and developing technologies to support their large-scale science. As a result we built a workflow management system, Pegasus: http://pegasus.isi.edu and related technologies including resource provisioning capabilities.
Many scientific disciplines are using workflow technologies to manage their complex analysis. Workflows provide a representation of complex analyses composed of heterogeneous models designed by groups of scientists. At the same time, workflows have also become a useful representation that is used to manage the execution of large-scale computations. This representation not only facilitates overall creation and management of the computation but also builds a foundation upon which results can be validated and shared. Since workflows formally describe the sequence of computational and data management tasks, it is easy to trace back how particular results were derived by examining the associated provenance trail.
In order to use the system, the scientist needs to describe the workflow at a high-level, capturing the computations involved and the data that need to be processed. This description is usually independent of the underlying cyberinfrastructure. The workflow along with information about the execution environment (including data and computational resources) is used by Pegasus to generate an executable workflow and to run it in an efficient and reliable fashion.
Within Pegasus, the Pegasus mapper finds the appropriate software and computational resources where the execution can take place as well as finding copies of the data indicated in the workflow instance. The mapping process can also involve workflow restructuring geared towards optimizing the overall workflow performance as well as workflow transformation geared towards data management and provenance information generation. The result of the mapping process is an executable workflow, which can be executed by a workflow engine that follows the dependencies defined in the workflow and executes the activities defined in the workflow nodes. DAGMan, the workflow engine of Pegasus relies on the resources (compute, storage, and network) defined in the workflow to perform the necessary actions. As part of the execution, data are generated along with their associated metadata and any provenance information that is collected.
A part of our system development we conduct research in the area of workflow scheduling, resource provisioning, data management, cloud computing, and others.
We develop novel algorithms to schedule jobs onto distributed resources, to estimate the resource needs of applications and provision resources accordingly. Through simulation and experimentation we evaluate the applicability and costs of cloud computing for science applications.
Much of our research focus is also on scalability and robustness of our system. In one scientific workflow in earthquake science, we need to manage approximately a million of individual jobs.

The Center for Policy Informatics (CPI) leverages cutting-edge technology and cross-disciplinary theory to help individuals and communities make and evaluate policy choices. Established in January 2008, the center’s modeling, simulation and theoretical work supports and critiques activities at ASU’s Decision Theater, a world-class interactive 3d immersive visualization environment created to facilitate group decision-making (www.decisiontheater.org ). CPI’s goal is to develop policy-relevant models that balance the complexity of real-world phenomenon with transparent, actionable evidence. Both externally funded and community focused projects provide the center a forum for the exchange of ideas and resources for modeling and simulations.

For example, researchers at the Decision Theater recently developed an interactive table-top event that used both a system’s dynamics model and decisions support system to test coordination strategies between ASU and external agencies during a pandemic outbreak. The participants were leaders in Arizona’s health and education communities. The value of this exercise was immediately relevant when the swine flu broke out and participants referred to their experience with the decision theater as being instrumental in their response.

Future work will be in a number of context including challenges in public health, sustainability, urban growth, crisis management, and related policy decisions. For example, the challenge of discouraging adolescents from smoking must address a multitude of factors that influence an individual’s decision to smoke: peer influence, social networks, physical addictions, environmental factors, family behaviors, genetics, marketing, and individual decision?making. This project seeks to understand the decisions, environments, and pathways in which the major forces of addictive behavior interact. It will achieve this understanding through creation of a systems?based, social?network model for smoking behavior that will spur new scenarios for possible interventions and allow policymakers and pubic?health professionals to visualize the cascading effects of a multitude of individual decisions.

Please refer to the Decision Theater and Center for Policy Informatics websites for a sample of the range and types of research that is possible ( http://cpi.asu.edu/home and www.decisiontheater.org ).

Potential CI fellows will be expected to assist with both current research and to develop an independent research agenda consistent with vision of the Center for Policy Informatics and the Decision Theater at ASU. Fellows with a multidisciplinary background are particularly encouraged to apply.

Autonomic Computing, Self protection of networks and computers, self control and management of network centric systems, high performance distributed computing, autonomic computing programming, autonomic control and management of networks and applications, autonomic management of power and performance of large scale data centers.

Design Automation, Design Migration, Layout Analysis and Optimization, DFM, Array Automation

My group is working toward building systems that can visually understand and interact with their environment. Motivated by this goal, we think about roughly three different things: new visual sensors and methods to acquire meaningful information from their data; efficient representations of this information; and applications of these representations to computer vision and graphics tasks.

Current focus areas include shape and appearance capture, color image processing, human perception of colors and shapes, reflectance and illumination modeling, physics-based approaches to scene understanding, identity recognition, and socially-aware vision systems.

G. Michael Youngblood, Ph.D. is an Assistant Professor in the Department of Computer Science at The University of North Carolina at Charlotte, Co-director of the Games + Learning Lab, and Director of the Game Intelligence Group. His work studies how artificial agents and real people interact in virtual environments including computer games and high-fidelity simulations in order to understand the elements and patterns of learning and interaction for the development of better artificial agents. He has worked with real-time computer games, intelligent environments, and robotics since 1997.

Key Research Areas of Interest:
* Using visual and graph-based data mining techniques to analyze and discover interesting patterns in interaction data in online and offline games and virtual worlds
* Ways to make the game/simulation AI-driven character creation process easier and more accessible
* Discovering new techniques and knowledge representations that allow interactive characters in games and virtual worlds to become more realistic (with an eye towards human-level behavior)
* Learning from the game character (agent) creation process at the meta-level
* Better knowledge representations for supporting intelligent decisions in interactive worlds
* Efficient 2D and 3D navigation mesh decompositions for agent navigation as well as information compartmentalization and network partitioning
* Exploring the development and integration of knowledge tools into the interactive, immersive world creation process to support better decisions and more realistic AI-driven participants
* Inferring world geometry from only the spatial movements of the actors in the environment
* Detecting and visualizing behavioral activities across large virtual worlds
* Exploring multi-agent or team-based character creation and behaviors with an eye towards reducing the creation burden, better supporting data structures, and developing a deeper understanding of group dynamics in games/simulation
* Understanding the interaction differences between the real and virtual worlds and how activities map between them
* Using AI as a core game mechanic
* Using AI and games in Computer Science Education
* In general, AI/Machine Learning in games/simulation environments in first-person, third-person, role-playing, real-time strategy, and massively-multiplayer (MMO) genres.

Some of Dr. Youngblood’s current projects include BehaviorShop and BEHAVEngine, which are aimed at making character creation and scenario setup for first and third-person mission rehearsal simulations possible in the field by minimally trained warfighters. The goal is to improve their operational readiness through relevant and always available training—leveraging a practice makes perfect mentality coupled with the idea that the individuals in the field know better than anyone what challenges they encounter daily, so training tools need to fit and support their realities. An extension of this project called CULTURE (Culture Understanding and Learning Tool Using Real-world Examples) focuses on creating cultural simulations to help promote cross-cultural understanding through a collection of simulation opportunities to walk in the shoes of people around the world. These projects leverage current and prior work in information representation and knowledge creation from Dr. Youngblood’s Common Games Understanding and Learning (CGUL) Toolkit.

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

Currently, my main focus is on realizing Claytronics, a form of
programmable matter. Programmable matter is an ensemble of computing
elements which can be programmed to work together to produce changes
in the physical properties of the ensemble. I am interested in
post-doctoral fellows who have broad interests. Particular projects
in the upcoming year: (1) realizing claytronics hardware (cm-scale
modular robots, mm-scale MEMS based robots, and bio-based units), (2)
Programming massively distributed systems (programmable matter, sensor
networks, etc.) using declarative approaches, (3) Ensemble based
applications (E.g., distributed planning, dynamic motion, etc.)

digital media, virtual environments, interactivity, art, humanities, gaming, language.

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

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

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

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

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

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

My research focuses on applying a computational mindset to molecular biology problems with the aim of advancing our understanding of human health and disease. Research in my group includes projects on understanding and characterizing gene networks; translational medicine and clinical pharmacogenomics (particularly as applied to human development); and integrating gene expression data with other genomic information sources to better understand the molecular mechanisms of life. Recent projects in my lab have relied upon computational expertise in machine learning, algorithm design, graph theory, text mining, statistics, and randomized algorithms.

Quantum Computation, Quantum Information Science, Quantum Algorithms, Topological Quantum Computing, Symbolic Computation, Differential Geometry, 3-manifolds, group representation theory, symbolic computation.
For more information, please refer to the URLs:
http://www.csee.umbc.edu/~lomonaco/Short.html

http://www.cs.umbc.edu/~lomonaco/Publications.html

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

My research interests lie on the boundary between machine learning and signal processing, especially as applied to audio. I’m particularly interested in sparse methods, non-negative decompositions, probabilistic models of sound and source separation.

My lab does primarily protein structure prediction, but is starting to do computational protein design. We use hidden Markov models, neural nets, and genetic algorithms for optimization of cost functions. Postdocs should already be comfortable probramming in c++ and perl.

I am interested in a broad range of computer music research, including interactive systems for music performance using advanced sensors and sensing technology to acquire real-time performance information, advances in music understanding from gesture and audio to acquire performance information such as tempo, phrasing, and score location, human computer interaction issues in live performance, interfaces for music adapted to rehearsal and performance, and new techniques for sound synthesis, especially using machine learning to acquire models of music performance and musical instruments. Other interesting directions are intelligent audio editing which automates manual “correction” of multi-track studio recordings (an almost universal production practice), high-quality models of string instrument tones for synthesis, and systems issues for real-time music (languages, run-time systems, strategies for future multi-core computers).

The research goals of USC’s Computational Emotion Group and ICT’s Social Simulation Lab are to study and model human behavior. Our work on human behavior modeling includes computational models of human emotion, decision-making, theory of mind reasoning and nonverbal behavior.

The Computational Emotion Group also studies the application of these models to the design of virtual humans, life-like facsimiles of humans that look and act like humans and can interact with humans using spoken dialog much as humans interact with each other. Current efforts of the Emotion Group include validating the EMA model of emotion against human data, machine learning approaches to deriving models of nonverbal behavior, the role of physiological processes in emotion and the animation of expressive behavior within the SmartBody virtual human animation system.

The Social Simulation Lab works on modeling and simulating human social interaction, at a more aggregate level (groups, cities, etc.). Ongoing research includes tractable approaches to modeling of theory of mind reasoning and decision-theoretic, descriptive models of human-like decision-making. We also study automated and data-driven approaches to validate and facilitate authoring of large scale social simulations.

power-efficient chip microarchitectures, reliable processors and systems, technology-aware design and tools, pre-silicon integrated modeling and validation methodologies.