An important theme in my work is the development of data management technology to address new problems introduced by emerging applications, including the Web and scientific applications. My recent research has focused on two main topics: scientific data management and Web mining. I work both on the development of core data management techniques and on applications that use these techniques. This gives me the opportunity to work with scientists in cool areas such as Environmental Sciences, Biology, Physics, and Graphics/Visualization. Some specific problems I am interested in include: provenance management (storage, querying, visualization), graph indexing and mining; infrastructure to support reproducible papers; large-scale (and Web) information integration; focused Web crawling; hidden Web crawling; data mining.

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

My my main interests involve social systems informatics. The this interdisciplinary program in Social Systems Informatics is directed towards advancing and disseminating computational approaches involving the measurement, modeling, and testing of how social interactions affect a broad range of human outcomes. These social interactions range from face-to-face, media communications, or other social actions and are often massive in size. Key outcomes involve major characteristics of our lives related to education, health, work, and play; attitudes, behaviors, performances in these areas are not simply products of individual factors but are fundamentally shaped by the social contexts and interactions in which people live. Computationally, social systems informatics relies on methods in system science, including social network analysis, agent based modeling, data mining, intelligent data analysis, and biostatistical methods, to study how individual and social system interactions affect health and other outcomes.

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

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

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

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

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

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

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

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

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

HP Labs Immersive 3D

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

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

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

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

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

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

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

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

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

2010 Sundance Film Festival

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

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

HP Newseum announcements

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Interface Ecology Lab imagines and develops integral and sensitive human-centered computing to support nuanced and exciting aspects of life, including how we form and express ideas and intentions, how we learn and innovate, how we gesture and communicate, how we coordinate and cooperate, how we participate and share, how we function under stress, and how we respond to crises. We connect diverse methodologies, engaging an interface ecosystems approach to engage computing in holistic avenues of human experience.

We have just developed an innovative multi-finger sensor, ZeroTouch. We are now poised to integrate ZeroTouch into interactive environments, developing new embodied bi-manual and free air interaction techniques and experiences.

We have released the open source meta-metadata language and architecture, for authoring platform-independent wrappers for heterogeneous information sources. Meta-metadata supports information extraction and knowledge integration. It facilitates writing programs that enable new human experiences of information visualization, physically-based modeling, and embodied interaction. Research here involves new knowledge semantics and modeling, on the one hand, and new interactive visual applications, on the other. Social media and digital libraries are connected.

We are developing special relationships with disaster response organizations. Texas Task Force 1 engages in major disasters, such as 9/11, and hurricanes Katrina, Rita, and Ike. TEEX Disaster Preparedness and Response runs an internationally-renowned academy for first responders. We are engaged both in developing zero fidelity simulation games for education, and in creating innovative multi-surface information systems for crisis response.

The typical search interface is great for finding a single element of information, but weak for information-based ideation tasks, in which the human goal is to to develop ideas with support and stimulus from information. The combinFormation platform reconceptualizes information-based ideation support as mixed-initiative information composition, integrating browse-search-collect-visualize-and-organize, while representing each collection as a connected whole. To validate composition, information-based ideation metrics and grounded theory are extended and synthesized, developing formative and summative evaluation methodologies. Application contexts include thesis writing in computer science, and design in architecture and mechanical engineering.

My laboratory is interested in key challenges in Systems Biology and Medicine. Our larger objective is to decipher biological mechanisms, reconstruct networks, predict phenotypes and build quantitative systems models. Towards this end we continue to develop computational and some experimental methods for integrative analysis of multiple types of biological data and for reconstruction of biochemical networks. It is increasingly become clear that time series data in cellular biology and longitudinal data in mammalian in vivo biology are essential for understanding complex phenotypes and we are developing methods to analyze these data. And most importantly, I subscribe to the view that context-free biology is content-free biology!

I work to understand motion and change in video — separating changes in seasons from changes over a day in outdoor time-lapse, capturing the motion patterns in an MRI video of a heart, or parsing the surveillance video of a intersection into traffic cycles. I mostly work on data-driven methods that could scale to work with (for example) all the webcams connected to the web, or within large scale, image based Citizen Science applications.

I am working on exciting computational biology projects related to synthetic biology, designing algorithms and tools that enable life scientists to create synthetic genomes. Working with virologists, we have already designed and synthesized virus genome sequences to serve as vaccines. Our work was reported in Science and Nature Biotechnology. We expect the next generation of tools to help design artificial chromosomes and cells performing unique functions, using existing genes, transcription factors and pathways as optimized design components.

I am involved in a number of other bioinformatics projects, including genomic sequence classification using machine learning, repeat mapping from sparse data, and algorithms for optimizing protein encodings adhering to a variety of constraints, including secondary structure, codon bias and other factors. I am also exploring the area of copy number variation detection from fragmented genomic sequence data, as well as using grid computing for time/space consuming applications, such as short read genome assembly.

I also have an active collaboration in the field of video streaming and video pattern matching. We are exploring efficient algorithms for simultaneously streaming compressed video through fixed bandwidth channels. We are also working in identifying patterns in the information content of video streams.

Please see my web page.

We study all aspects of Computational molecular biology as it relates to proteins, from sequence to structure to function. We have particular interests in remote homology detection and in protein-protein interaction networks. The toolbox we need to solve these problems comes from algorithms, machine learning and even graphics/ visualization. I am happy to supervise anyone with a strong background in any of these three areas even if prior experience with biological problem domains is limited. Many opportunities to learn about this area and develop new interdisciplinary collaborations.

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

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

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

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

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

Projects of current special interest in my group include:

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

Dr. Ungar’s research group develops scalable machine learning and text mining methods, including clustering, feature selection, and semisupervised and multitask learning for large bioinformatic and web-based problems. Example applications include semi-supervised methods for information extraction, multi-view learning for gene expression and fMRI data, and use of document and link structure for informing feature selection or transfer of knowledge between tasks.

Students work closely with co-mentors in the Schools of Medicine or of Management (Wharton).

Projects available:
Spectral methods for capturing word meaning and
Modeling the dynamics of scientific micro-communities

My research interests include computer system security and embedded systems with emphasis on applications to implantable medical devices and low-power, RFID-scale computers. We publish in venues such as IEEE Oakland, USENIX Security, ASPLOS, SIGCOMM, USENIX FAST, and MobiSys. We also publish in computing journals, medical journals, and workshops such as IEEE Trans. on Computers, ACM HotPower, and USENIX HealthSec.

A postdoc would interact with a half dozen energetic PhD students and be provided the resources to increase his or her publication pipeline. He or she would be provided training helpful to establish funding for one’s research career. The research would also involve regular interactions with collaborators at MIT, Berkeley, and the Harvard Medical School—providing the postdoc with special opportunities to interact with influential researchers from several schools.

The postdoc would also have access to about a million dollars worth of equipment for embedded systems research: differential power analysis workstations, scopes, signal generators, logic analyzers, FPGA boards, software radios, custom-built tools for measuring power, custom-built programmable RFID tags, explanted medical devices, etc.

About the Security & Privacy Research (SPQR) Lab: http://spqr.cs.umass.edu/

The SPQR Lab focuses on research topics of interest to the security and embedded systems communities, often with emphasis on matters of social relevance. Half of the graduated students are women. Student honors include best paper awards from USENIX Security and Oakland.  Half of the PhD students have NSF graduate research fellowships. The research results have been featured in the New York Times, NPR, and MIT Technology Review. The publications also prompted rethinking at the Food and Drug Administration, the Federal Reserve system, and the Federal Trade Commission.

The Department of Computer Science by the Numbers: The CS department has 42 faculty, 230 graduate students, 294 undergraduate majors, and 1,600 classroom students. Our partnerships within UMass and the Five College System provide unique collaboration opportunities (and jobs). For the 2009 fiscal year, CS research funding exceeded $15.9 million. CS researchers consumed 16,026 cups of coffee (caffeinated) and 2,601 cups of decaf followed by tea and
mocha. Various types of espresso have a cult following.

http://www.cs.umass.edu/

About the Amherst area: The Amherst/Northampton area is beautiful, cosmopolitan, liberal and cited as #1 of the top 5 college towns in the USA! The area offers diverse cultural activities, excellent restaurants and amazing natural surroundings. Home to the Five Colleges—Amherst College, Hampshire, Mt. Holyoke, Smith, and the University of Massachusetts—the area is full of educated folks. 42% have graduate degrees—nearly the highest percentage in America. Proximity to Boston and New York provides further options for collaboration and recreation.

The University of Massachusetts is an Affirmative Action/Equal Opportunity Employer. Women and members of minority groups are encouraged to apply.

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

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

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

Noshir Contractor (http://nosh.northwestern.edu) is the Jane S. & William J. White Professor of Behavioral Sciences in the McCormick School of Engineering & Applied Science, the School of Communication and the Kellogg School of Management at Northwestern University, USA. He is the Director of the Science of Networks in Communities (SONIC) Research Group at Northwestern University.

Professor Contractor is investigating factors that lead to the formation, maintenance, and dissolution of dynamically linked social and knowledge networks in a wide variety of contexts including communities of practice in business, translational science and engineering communities, public health networks and virtual worlds.

Research in the SONIC (Science of Networks in Communities) http://sonic.northwestern.edu which he directs seeks to understand and enable multidimensional social networks. Multidimensional networks include different types of nodes (such as people, documents, data sets, and tags) and the different types of relations that connect them.

In order to understand the emergence and dynamics of multidimensional social networks, research in the SONIC lab combines (social science) theory driven and data driven (statistically modeling, data mining and machine learning) approaches.

In particular the lab is interested in the statistical modeling of networks using p* /Exponential Random Graph Modeling (ERGM) techniques and stochastic actor orient modeling techniques (implemented in SIENA). In addition, there is a strong emphasis on conducting these analysis on large scale networks. The lab has access to many large scale server-side sources of data such MMOs (Massively Multiplayer Online games) and the Web of Science. The insights gained from understanding the emergence and dynamics of multidimensional networks is used to enable networks by building sophisticated recommender systems that provide individualized suggestions on who to collaborate with or what team to assemble.

His research program has been funded continuously for over 15 years by major grants from the U.S. National Science Foundation with additional current funding from the U.S. National Institutes of Health (NIH), Air Force Research Lab, Army Research Institute, Army Research Laboratory and the MacArthur Foundation.

Professor Contractor has published or presented over 250 research papers dealing with communicating and organizing. His book titled Theories of Communication Networks (co-authored with Professor Peter Monge and published by Oxford University Press, and translated into simplified Chinese in 2009) received the 2003 Book of the Year award from the Organizational Communication Division of the National Communication Association. He is the lead developer of C-IKNOW (Cyberinfrastructure for Inquiring Knowledge Networks On the Web), a socio-technical environment to understand and enable networks among communities, as well as Blanche, a software environment to simulate the dynamics of social networks.

We’re developing general systems for scalable Bayesian inference. We’re exploring a mixture of sampling and point estimation strategies including Hamiltonian Monte Carlo and variational methods.

General purpose tools under development include a general posterior sampler (along the line of BUGS), multiple imputation for missing data, and post-stratification for prediction.

We’re particularly interested in multilevel regression and factor models, with applications to prediction problems in epidemiology, climate modeling, and social science. I’m also interested in large scale probabilistic RNA alignment, expression estimation and pathway modeling.

Other team members include Andrew Gelman, Ben Goodrich, Matt Hoffman, and Michael Malecki.

Our goal is to develop methods that will allow us analyze and ultimately to modify the computations that cells have evolved to carry out in response to signals from their environments.

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

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

The following NSF grant was recently awarded:

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

I am interested in theoretical modeling and experimental understanding of multi-instrument measurement systems that deal with multi-dimensional multi-variate data, as well as in automation and design of computer-assisted systems that deal with large volumes and computational intensive processing of heterogeneous data. The main goal of my research and development is to automate information processing of repetitive, laborious and tedious analysis tasks, to scale computations of automated tasks, and to build decision-making systems that operate in automated or semi-automated mode.

Post-doctoral students and graduate students interested in working with me would be focusing on research and development of new algorithms, especially those that automate visual inspections and understanding scenes from spectral imagery, and those that scale with large volumes. The research of algorithms is based on deep understanding and modeling of instruments generating the images, the phenomena represented by images, and the processes applied to raw images. The contributions of the joint work would be in building bridges from raw data to information and to knowledge, as well as in understanding computational and algorithmic challenges for automated data-centric operations.

It is now widely recognized that current commercial many-core systems are simply not good enough: most programmers can’t handle them. Therefore, alternatives must be developed. Anticipating this problem over a decade ago, the Explicit Multi-Threading (XMT) framework has been under development at the University of Maryland. XMT is a general-purpose many-core computing platform with the vision of a 1000-core chip that is easy to program but does not compromise on performance.

XMT is built to support the PRAM theory of parallel algorithm, which is second in its wealth only to the serial algorithms. Since four decades of parallel computing research provided no real alternative to the PRAM, the XMT project sought to draft specifications for the general-purpose many-core desktop of the future, by first inventing hardware and software support for the abstractions developed by PRAM algorithmics — a task deemed impossible by architecture researchers prior to the accomplishments of the XMT project.

A 2010 status report of XMT appears in U. Vishkin, Using simple abstraction for reinventing computing for parallelism, CACM, January 2011. Order of magnitude speedups, dramatic advantages on teachability from middle school to graduate courses have been demonstrated. And favorable student ranking for achieving speedups relative to standard platforms have been demonstrated.

So far, XMT has spanned applications, parallel algorithms, compilers, HW/SW and education of parallelism. Research opportunities building on this promising foundation include now also CS education, bioinformatics, machine learning and other applications, security, OS, and SW architectures.

There is so much more to the potential of many-core parallel computing than the horizons of commercial hardware offer!

My main interest currently is the boundary between statistics and statistical machine learning, both from theoretical and practical perspectives. The primary tools I develop are in spatially-based statistics and unsupervised machine learning, specifically clustering and regression. A major domain for applying this work is on large biological datasets, which is an extremely popular application area at UW-Madison. I thus collaborate closely with practicing physicians and researchers in our school of medicine.

My research group’s work also focuses on applications of optimization theory and operations research to machine learning. We are particularly interested in understanding how animals solve optimization problems that appear intractable and the inductive biases that make this possible.

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.

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.

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

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.

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

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

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

Electrical-engineering (ee) degrees. Learning in fields such as art/architecture.
Widely published in archival journals. Three edited books. Authored encyclopedia articles.
Doctorate: dissertation in stochastic control (ee); mathematics and statistics minors; reading knowledge of Russian and French. Now use Google-translate to write Russian.
Rand Corporation employment and UCLA projects on applied problems ranging from operations research to biomedical computing.

Emeritus Professor since 1994.

I am particularly interested in these topics: Pattern recognition applied to mobile health technologies; wellness technologies; context-aware mobile devices; personal health informatics; home sensing and activity recognition; wearable accelerometers; physical activity measurement; novel health interventions; persuasive computing; technologies that motivate behavior change; exergames; games for health. I moved to Northeastern recently from MIT to start a new Ph.D. program in Personal Health Informatics and I’m very interested in working with postdocs who want to work on that topic.

My research interests lie in human-computer interaction in scientific collaborations, collaborative creativity, cyberinfrastructure, eScience, information visualization, usability in daily life, and how social media and computer-mediated communication are changing scientific practice.

VLSI CAD for More Moore (nanolithography beyond 22nm) and More-than-Moore (3D-IC, nanophotonics, etc.);
Physical design and technology co-optimization;
Computational lithography;
Vertical integration of architecture/CAD/circuit/technology;
Design/automation of emerging technologies

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).

Mathematical theories of bounded rationality and metareasoning

Hierarchical reinforcement learning and planning, decision making over long time scales, with applications in robotics.

Unification of probability theory and first-order logic, expressive probability models for domains with unknown objects and events, with applications in information extraction, security, and global-scale seismic data analysis for the Comprehensive (Nuclear) Test Ban Treaty.

Temporal probability models (e.g., dynamic Bayesian networks and continuous-time models) and inference algorithms, with applications in real-time monitoring and diagnosis of patients in intensive care (joint w/ UCSF)

Multitarget tracking and data association, vision-based tracking, simultaneous tracking and context acquisition, probabilistic vision

Bandit problems, metalevel search control, decomposition of complex decision problems

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.

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

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

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.

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

Deborah McGuinness is the Tetherless World Constellation (tw.rpi.edu) Chair and professor of computer science and cognitive science at Rensselaer Polytechnic Institute. She has also just been named the director of the new Web Science Research Center at RPI, Prior to joining RPI in 2007, she led the knowledge systems laboratory (ksl.stanford.edu) at Stanford University and prior to that, was part of the AI Research Department at Bell Labs / AT&T Labs.
She co-directs the Tetherless World along with Jim Hendler and Peter Fox.

Her current interests center around making the next generation web more usable and useful. Projects focus on designing semantic tools and applications that help access, integrate, understand, and use data. Current projects include emphasis on semantic technologies, knowledge provenance, privacy, policy, and workflow transparency, semantic eScience, linked open data, trust, social networking, and collaboration technologies, ontology evolution environments, and ethical, policy, and social aspects of web use and usability.

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

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

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

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

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

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

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

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

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

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

Very large parallel databases on cloud computing platforms. Data integration of genomic databases. query processing on energy constrained sensors. Efficient algorithms for RDF statistics collection.

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

My research interests in natural language understanding largely focus on analyzing style and feelings encoded in texts. We are currently working on projects in fine-grained sentiment analysis and authorship attribution and profiling, as well as an effort to produce high-quality summaries of medical literature for use by practicing physicians.

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.

Our group focuses on modeling regulatory and signaling networks and on cross species analysis of high throughput biological data. We develop and use computational methods utilizing techniques from machine learning, statistics and combinatorics in order to solve important biological questions. We are collaborating with multiple experimental groups and most of our algorithms lead to new hypotheses which are experimentally validated by our collaborators.

The open positions are in two major lines of research:
1. Reconstructing dynamic networks in the cell. We are interested in developing methods that utilize graphical models and graph theoretic algorithms to combine dynamic (temporal) and static information from high throughput biological data in order to reconstruct the flow of information in the cell. Applications include modeling of stress responses, cell cycle, infections, predicting timing of binding of transcription factors, their combinatorial interactions and the pathways activating them.

2. Cross species analysis of interaction networks. While sequence is highly conserved, most interaction networks are much less conserved, even between close species. We are interested in developing methods to identify the conserved and divergent elements in interaction networks (including protein interactions, co-expression networks, genetic interactions etc.) and to use these similarities and differences to identify the mechanisms underlying various responses.

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.

Develop natural language processing (computational linguistic) methods for analyzing clinical free-text, emphasis on neuro-psychiatric text.

My current research interests include the visualization of data with associated uncertainty, multivariate visualization, volume rendering, information visualization, perceptual and illustration issues in visualization, artistic rendering, dynamic and interactive representations and interfaces, and the experimental validation of visualization techniques.

I am interested in a wide variety of application domains, including medical, atmospheric, environmental, educational, policy-making, and machine learning. I believe visualization research is most productive in collaboration with domain experts.

My research interests lie at the intersection of statistical signal processing and machine learning. I am interested in developing principled approaches, particularly for semi-supervised and active learning, that adaptively learn and harness the dependency structure between variables to enable efficient inference. Application areas of interest to me include networked systems and bioinformatics.

The ideal fellow would work jointly with myself and Dr. Pradeep Ravikumar of the UT Austin Department of Computer Science (http://www.cs.utexas.edu/~pradeepr). We are developing methods for analysis of functional magnetic resonance imaging data using machine learning and network analysis methods.

I am in the Information School and Human Centered Design & Engineering at the University of Washington, where I direct the Computing for Healthy Living & Learning (CHiLL) Lab. Our primary focus is in Human-Computer Interaction, Ubiquitous Computing, and Persuasive Technology. We are interested in designing, developing, and evaluating applications of computing technology that aim to promote healthy lifestyles and education. We design and develop mobile, persuasive, and collaborative technologies with the goal of helping with record-keeping, review, and motivation to pursue health and educational goals of individuals, families, and teachers. Current projects are focusing on health for families from diverse populations, therapists for children with autism, and individuals with sleep disorders. We are also researching related topics, such as effective persuasive computing and record-keeping techniques and improving the empathy in technologies for health. More information can be f ound at the CHiLL Lab website: http://depts.washington.edu/chilllab

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.

Ideally fellow would be joint with Prof. Russell Poldrack, Director of the Imaging Research Center, University of Texas, Austin. We are interested in developing and using statistical machine learning tools for problems in cognitive neuroscience.

My research focuses on the science and engineering of large knowledge base systems and spans knowledge representation and reasoning, deductive question answering, knowledge acquisition, and innovative applications. I am also interested in entrepreneurship, and transitioning research ideas into commercial products. I have worked with a wide range of knowledge representation and reasoning systems including LOOM, Ontolingua, Cyc, KM, and F-Logic. I was part of the team that developed the Open Knowledge Base Connectivity protocol for accessing knowledge bases. I contributed to storage, and multi-user software that underlies SRI’s EcoCyc project. Since 2000, I have been working on a system to capture knowledge from science textbooks, and to answer questions at the back of the book.

My research areas include (1) the development of information data management systems in support of clinical genotyping and personalized medicine, and (2) computational genomics in clinical and non-clinical organisms. In addition, I have experience in commercializing research findings in see-stage companies.

My research area is in the interface between computer science and medical applications with a special focus on computer-aided treatment planning of radiation oncology and radiology. Typically, my research involves the following aspects:

* Mathematical abstraction and modeling of medical problems to make these problems accessible to computer scientists and their expertise.

* Developing algorithmic solutions to the formulated mathematical problems. My research approach is fundamentally mathematical and uses techniques such as computational geometry, network flow, combinatorial optimization, and numerical optimization. I also rely on rigorous analysis of algorithms and proofs to quantify the quality and predict the performance of the solutions.

* Implementing, testing, and verifying the solutions in a clinical setting.

* Patenting, commercializing and publishing the results.

Dr. Ferris’ research is concerned with algorithmic and interface
development for large scale problems in mathematical programming,
including links to the GAMS and AMPL modeling languages, and general
purpose software such as PATH, NLPEC and FATCOP. He has worked on
several applications of both optimization and complementarity,
including cancer treatment plan development, radiation therapy,
video-on-demand data delivery, economic and traffic equilibria,
structural and mechanical engineering.

My research is at the intersection of Educational Data Mining and Human-Computer Interaction. I develop and use methods for mining the data that comes out of the interactions between students and educational software, in order to better understand how students respond to educational software, and how these responses impact their learning. I study these issues within intelligent tutors, science microworlds, and educational games.

In recent years, my colleagues and I have developed automated detectors that make inferences in real-time about students’ motivational and meta-cognitive behavior, using data from students’ actions within educational software (no sensor, video, or audio data). We have in particular studied “gaming the system”, off-task behavior, guessing, and slipping. We use these models to make basic discoveries about human learning and learners, and their cognition, meta-cognition, motivation, and affect. My colleagues and I have also developed detectors of
complex aspects of student learning, such as the development of
meta-cognitive strategies and detecting the moment of learning.

I would be interested in working with CI fellows interested in research in these areas.

N-body solvers, which calculate pairwise interactions among a large set of particles, are a vital tool in many simulations of physical phenomena. There is an opportunity to significantly increase the power and the applicability of this technology through the development of algorithms and software of unprecedented simplicity, efficiency, and generality. The basis for such an advance is a relatively obscure approach known as the multilevel summation method (MSM), which is s flexible unified methodology based on a hierarchy of grids (with the possibility of finer grids being localized) and well suited for modern computer architectures. Indeed, we expect multilevel summation to perform an order of magnitude better than other methods for important classes of problems, such as simulations of macromolecules, and expect it to be a formidable competitor in other situations.

The calculation of pairwise interactions and the solution of discrete elliptic equations are the time-limiting steps of applications that consume vast amounts of CPU cycles. Molecular dynamics, in particular, can require months of computer time. The project envisioned here is motivated by problems in computational molecular biophysics, which is being transformed by increasing computing power into a quantitative science with predictive value. And though the MSM will benefit many applications, there is a particular application that makes development of the MSM truly compelling: the use of the spherical and the generalized solvent boundary potential methods for modeling very large systems for which full atomic detail is not needed at a long distance from the active site. Such boundary potentials are implemented in molecular simulators but their use is limited due to the high cost of using standard methods for nonperiodic boundaries. This application is of great interest for very large-scale simulations on proteins that play a crucial role in human disease.

There are several key parts to this work: One is to achieve higher accuracy, using techniques inspired by those that are employed in competing fast N-body solvers. The second is to achieve accelerated performance on current and emerging computer architectures, e.g, using OpenMP or OpenCL (for multicore, multiprocessor CPUs and GPGPUs) and/or MPI (for clusters) for ensembles of smaller particle systems and/or for large particle systems. A third important goal is an adaptive version, e.g. using a pruned oct-tree, for problems with nonuniform particle distributions.

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).

Our research focuses on the development of algorithms for solving problems in molecular biology. Please see website for more information.

natural language processing, natural language processing applications to real life problems including policy problems, information extraction, named-entity recognition, paraphrase recognition, text summarization, natural language processing evaluation, medical language processing, natural language processing on clinical patient records, medical informatics, biomedical informatics, data mining, information retrieval. Machine learning, supervised and semi-supervised approaches, expert systems. Innovative applications of the above.

Our research interests are in the design and implementation of rigorous algorithms, and their dissemination as useful software tools, for fundamental problems in computational biology. Our current research is applying techniques from string algorithms, graph algorithms, combinatorial optimization, and machine learning to biological sequence analysis. Topics of interest include multiple sequence alignment, genome alignment, next generation sequencing, approximate repeat finding, motif discovery, RNA simultaneous folding and alignment, protein secondary structure prediction, and inverse parametric optimization. Recent work has lead to the release of the software tools AlignAlign, Opal, and IPA for multiple sequence alignment and inverse parametric alignment.

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.

machine learning ,data mining, bioinformatics, computational biology, medical informatics, statistical-relational learning, advice taking, information extraction

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.

Our lab works in the exciting new field of Systems Biology. We develop computational tools for modeling biological networks and also conduct experiments to gather data and validate our computational predictions. Please visit our lab webpage for more details.

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

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

My research group (Advanced Data Management Technologies Laboratory) focuses on user-centric data management for network-centric applications. In a nutshell, my research tries to remove the optimize-for-one-global-metric mentality of modern data management systems and to bring users into the loop, by allowing them to specify preferences, especially in the presence of multiple metrics that are in direct trade-off with each other (for example Quality of Service versus Quality of Data). This is particularly useful in periods of high load, since the system is able to allocate crucial system resources according to users’ preferences, thus increasing the overall user satisfaction by meeting their goals. I have primarily worked on user-centric data management across three different domains: web-databases, sensor networks, and data stream management systems; my group continues to have active projects in all three areas. I am currently leading the research effort in the web-databases area, with funding from my NSF CAREER award. My group is also working on scientific data management, through collaborations with the Medical School and the Department of Physics and Astronomy of the University of Pittsburgh.

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 lead a group that has interests in computer vision and medical imaging. We are currently focused on the problem of demonstrating the role of content-based similarity retrieval of multimedia data for healthcare decision support in cardiology. We work on non-rigid registration, content-based indexing and search of images, videos, and text.

Personally, I also have interests in using computer vision for functional genomics.

Project website: http://www.almaden.ibm.com/cs/projects/aalim

(still under development)

Ultimately saying, I am interested in how the brain works. Also I am studying about how to model the brain function and how to use the knowledge of the brain to build intelligent agents.

Irfan Essa works in the areas of Computer Vision, Computer Graphics, Computational Perception, Robotics and Computer Animation, with potential impact on Video Analysis and Production (e.g., Computational Photography, Image-based Modeling and Rendering, etc.) Human Computer Interaction, and Artificial Intelligence research. Specifically, he is interested in the analysis, interpretation, authoring, and synthesis (of video), with the goals of building aware environments, recognizing, modeling human activities, and behaviors, and developing dynamic and generative representations of time-varying streams.

He is motivated to build smart intelligent machines that can interact with people. He is also interested in machines that can handle large amounts of media and wants leverage social networks to build systems for large groups of people to interact and create smart media.

Machine learning, meta-learning, artificial intelligence, pattern recognition applied to problems in physics and astronomy.

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.

The most pressing and urgent problem in software development is the creation of software on which we can rely to perform as intended. Given how ubiquitous software is in every field of human endeavor, this is one of the most important problems facing mankind. My research focuses on the development of software systems for distributed and high assurance applications. Part of this work relates to the formal specification of software properties. Formal specifications can be used by a variety of software tools for creating and verifying software. These tools include model checkers, theorem provers, runtime monitors, and program synthesis systems. Formal specifications can be used to identify conflicting requirements, manage change in requirements (pre- and post-deployment), and detect errors during software execution.

I am engaged in the application of software engineering technology to real world problems, namely the development of application software for use by scientists and engineers. The goals of the development of this set of applications are two-fold: first, it is my intent to provide software support to important research, research that might not be possible without the availability of reliable, trustworthy software. As such, much of the work is multidisciplinary. Second, the development of production software provides numerous examples against which to test the cost-effectiveness of industrial software engineering approaches. Ideas on improving the reliability and reducing the cost of software abound; however, few are used in practice, in part because their efficacy remains undemonstrated. By applying techniques and practices to real-world software projects, we are testing the applicability and cost-effectiveness of these techniques and training a cohort of software engineers who can make reasoned decisions about these techniques in industrial practice.

Most prominent of this work is the development of the software currently in use by the space scientists in the analysis of data from the NASA Cassini mission to Saturn. Software developed for this mission aids in mission planning and rapid opportunity analysis as well as image analysis from the ISS, CIRS, and VIMS instruments on Cassini. The research group has investigated and developed a number of software refactorings that are used to migrate procedural IDL code to object-oriented IDL code.

Other applications under development include the PROSPEC property specification tool, which guides domain experts in the development of formal specifications for use by formal software analysis tools. Aligned with this is the development of a framework for model-checker based testing of formal specifications and the development of mechanisms for the generation of specifications in LTL. The PROSPEC work includes porting the software to the Eclipse platform and providing visual feedback for validation of the resulting specifications.

In the fall of 2008, I began working with Craig Tweedie of the Systems Ecology Laboratory in the Department of Biology at UTEP. This work will result in a system that allows climate researchers to access data from a network of sensors. A novel part of this system is the automated data quality flagging that identifies anomalous data on upload

Our lab includes advaced research in the areas of computer graphics and a broad array of visual computing topics from input sensors to amatuer content creation (3D model extraction from photos etc), to procedural representations of avatars and animations of same, there are some elements of Video and Image processing using complex statistical algorithms and explorations of challenges / new application scenarios made possible by the evolution of Mobile Internet Devices and the sensors that are expected to be attached, also Mobile Augmented Reality whereby such MIDs can add value to the experience by leveraging the sensor inputs along with direct connection to the internet.