DePaul University is a private university in Chicago, Illinois. Founded by the Vincentians in 1898, the university takes its name from the 17th-century French priest Saint Vincent de Paul. In 1998, it became the largest Catholic university by enrollment in the United States. Following in the footsteps of its founders, DePaul places special emphasis on recruiting first-generation students and others from disadvantaged backgrounds.DePaul's two main campuses are located in Lincoln Park and the Loop. The Lincoln Park Campus is home to the Colleges of Liberal Arts and Social science, Science and Health, and Education. It also houses the School of Music, the Theatre School, and the John T. Richardson Library. The Loop campus houses the Colleges of Communication, Computing and Digital Media, and Law. It is also home to the Kellstadt Graduate School of Business, which is part of the nationally ranked Driehaus College of Business - the tenth oldest business school in the nation.The university enrolls around 16,150 undergraduate and about 7,600 graduate/law students, making DePaul the 13th largest private universities by enrollment in the United States, and the largest private university in Illinois. The student body represents a wide array of religious, ethnic, and geographic backgrounds, including over 60 foreign countries.DePaul's intercollegiate athletic teams, known as the Blue Demons, compete in the Big East Conference. DePaul's men's basketball team has made 18 NCAA tournament appearances and appeared in two Final Fours. Wikipedia.
Brooke J.S.,DePaul University
Clinical Microbiology Reviews | Year: 2012
Summary: Stenotrophomonas maltophilia is an emerging multidrug-resistant global opportunistic pathogen. The increasing incidence of nosocomial and community-acquired S. maltophilia infections is of particular concern for immunocompromised individuals, as this bacterial pathogen is associated with a significant fatality/case ratio. S. maltophilia is an environmental bacterium found in aqueous habitats, including plant rhizospheres, animals, foods, and water sources. Infections of S. maltophilia can occur in a range of organs and tissues; the organism is commonly found in respiratory tract infections. This review summarizes the current literature and presents S. maltophilia as an organism with various molecular mechanisms used for colonization and infection. S. maltophilia can be recovered from polymicrobial infections, most notably from the respiratory tract of cystic fibrosis patients, as a cocolonizer with Pseudomonas aeruginosa. Recent evidence of cell-cell communication between these pathogens has implications for the development of novel pharmacological therapies. Animal models of S. maltophilia infection have provided useful information about the type of host immune response induced by this opportunistic pathogen. Current and emerging treatments for patients infected with S. maltophilia are discussed. © 2012, American Society for Microbiology. All Rights Reserved.
Agency: NSF | Branch: Standard Grant | Program: | Phase: SOFTWARE & HARDWARE FOUNDATION | Award Amount: 515.90K | Year: 2016
Software and systems engineering projects accrue large amounts of development data including requirements, design, code, test cases, and fault logs. When combined with the power of software analytics this data could be used to provide actionable intelligence to project stakeholders. For example, a developer might ask to view all safety-related code which is likely to exhibit runtime faults. The proposed work will deliver a solution named Asked and Answered for Software Intensive Projects (AA) and will support a broad range of analytic queries. To foster the transition of AA to practice, the researchers will partner with industry collaborators throughout the project and develop an open-source framework facilitating the deployment of AA technology into an industrial environment. A series of natural language Query Challenges will be designed and disseminated and used to train Software Engineering students in a broad spectrum of software analytics.
Delivering the AA solution requires several non-trivial challenges to be addressed. First, a natural language (NL) query interface will be developed allowing stakeholders to issue queries in their own words and from their own perspective on the project. These queries will then be transformed into a structured, executable format. Heuristics and statistical inferencing techniques will be adopted and interactive mechanisms will be designed to seek clarification from the user when the query cannot be disambiguated automatically. Software analytics will be integrated into the query mechanism so that AA can respond to a wide range of analytical questions. AA will support the dynamic composition of primitive functions into execution flows in order to service a wide range of analytical queries. Finally, AA will deliver a query engine capable of generating optimized query execution plans which take into account the nuances of the domain - namely its heterogeneous data formats, distributed tools, and the dynamic runtime requirements of analytic functions.
Agency: NSF | Branch: Standard Grant | Program: | Phase: AISL | Award Amount: 213.27K | Year: 2016
This is an Early-concept Grant for Exploratory Research supporting research in Smart and Connected Communities. The research supported by the award is collaborative with research at the University of Colorado. The researchers are studying the use of technologies to enable communities to connect youth and youth organizations to effectively support diverse learning pathways for all students. These communities, the youth, the youth organizations, formal and informal education organizations, and civic organizations form a learning ecology. The DePaul University researchers will design and implement a smart community infrastructure in the City of Chicago to track real-time student participation in community STEM activities and to develop mobile applications for both students and adults. The smart community infrastructure will bring together information from a variety of sources that affect students participation in community activities. These include geographic information (e.g., where the student lives, where the activities take place, the student transportation options, the school the student attends), student related information (e.g., the education and experience background of the student, the economic status of the student, students schedules), and activity information (e.g., location of activity, requirements for participation). The University of Colorado researchers will take the lead on analyzing these data in terms of a community learning ecologies framework and will explore computational approaches (i.e., recommender systems, visualizations of learning opportunities) to improve youth exploration and uptake of interests and programs. These smart technologies are then used to reduce the friction in the learning connection infrastructure (called L3 for informal, formal, and virtual learning) to enable the student to access opportunities for participation in STEM activities that are most feasible and most appropriate for the student. Such a flexible computational approach is needed to support the necessary diversity of potential recommendations: new interests for youth to explore; specific programs based on interests, friends activities, or geographic accessibility; or programs needed to level-up (develop deeper skills) and complete skills to enhance youths learning portfolios. Although this information was always available, it was never integrated so it could be used to serve the community of both learners and the providers and to provide measurable student learning and participation outcomes.
The learning ecologies theoretical framework and supporting computational methods are a contribution to the state of the art in studying afterschool learning opportunities. While the concept of learning ecologies is not new, to date, no one has offered such a systematic and theoretically-grounded portfolio of measures for characterizing the health and resilience of STEM learning ecologies at multiple scales. The theoretical frameworks and concepts draw together multiple research and application domains: computer science, sociology of education, complexity science, and urban planning. The L3 Connects infrastructure itself represents an unprecedented opportunities for conducting living lab experiments to improve stakeholder experience of linking providers to a single network and linking youth to more expanded and varied opportunities. The University of Colorado team will employ three methods: mapping, modeling, and linking youth to STEM learning opportunities in school and out of school settings in a large urban city (Chicago). The recommender system will be embedded into youth and parent facing mobile apps, enabling the team to characterize the degree to which content-based, collaborative filtering, or constraint based recommendations influence youth actions. The project will result in two measurable outcomes of importance to key L3 stakeholder groups: a 10% increase in the number of providers (programs that are part of the infrastructure) in target neighborhoods and a 20% increase in the number of youth participating in programs.
Agency: NSF | Branch: Standard Grant | Program: | Phase: EarthCube | Award Amount: 209.15K | Year: 2016
A big obstacle to such sharing is the inordinate amount of time and effort that must be spent in creating, communicating, receiving, and interpreting specifications of data, models, and associated knowledge. This inability to quickly and conveniently share is particularly a problem in computational geoscience, wherein scientists spend significant portions of their time managing the many input and output files that are typically associated with a model. When developing, testing, validating, and comparing models, particularly coupled models, the number of such data elements and the complexity associated with their management soon outgrows human memory capacity. The unfortunate consequence is that researchers often narrow the scope of a model analysis, compromise research quality, or conduct analysis within restricted teams. This pilot project will demonstrate a mechanism to overcome this challenge in the scientific community.
The GeoDataspace pilot will develop a new data-centric approach to describing models and associated data resources for computational geoscience. This new approach will both simplify model use and enhance the shareability, reusability, and reproducibility of models, data, and computations?properties widely sought by computational geoscientists. Specifically, the project will develop methods for defining, sharing, and accessing geounits, collections of descriptive metadata that define a m the entire collection of files needed to run a computational model, including details about the model run. In the case of files, processing and manipulation scripts, manifests, spreadsheets, or one-off databases, the encapsulation may consist simplify of the elements location and specification of each element. The GeoDataspace team includes (a) experts in cyberinfrastructure, data management systems, and SaaS at UChicago; (b) experienced and leading geoscientists in four domains of solid earth, climate, hydrology, and space science, and a leading expert, as well as, geoscientist on model coupling frameworks. Together the team has identified a cross-cutting data management barrier that must be critically addressed in a domain-independent manner so as to extend capabilities to a broader set of geoscientists.All participating geoscientists are initiators, leaders, working-group chairs, and/or representatives of the five modeling communities we represent, including Computational Infrastructure for Geodynamics (CIG), Community Earth System Models (CESM), Consortium of Universities for the Advancement of Hydrological Science, Inc. (CUAHSI), and Community Coordinated Modeling Center (CCMC) at NASA, and finally Earth Systems Bridge (ESB), a community invested in developing model coupling frameworks. In total, the number of geoscientists either directly or indirectly involved in GeoDataspace is in the hundreds, if not thousands.
Agency: NSF | Branch: Standard Grant | Program: | Phase: EarthCube | Award Amount: 784.00K | Year: 2016
Scientific reproducibility -- the ability to independently verify the work of other scientists -- continues to be a critical barrier towards achieving the vision of cross-disciplinary science. Federal agencies and publishers increasingly mandate and incentivize scientists to, at a minimum, establish computational reproducibility of scientific experiments. To comply scientists must connect descriptions of scientific experiments in scholarly publications with the underlying data and code used to produce the published results and findings. However, in practice, computational reproducibility is hard to achieve since it entails isolating necessary and sufficient computational artifacts and then preserving those artifacts in a standard way for later re-execution. Both isolation and preservation present challenges in large part due to the complexity of existing software and systems as well as the implicit dependencies, resource distribution, and shifting compatibility of systems that evolve over time -- all of which conspire to break the reproducibility of an experiment. The goal of the GeoTrust project is to understand the research lifecycle of scientific experiments from conception to publication and establish a framework that will improve their reproducibility.
GeoTrust will develop sandboxing-based systems and tools that help scientists effectively isolate computational artifacts associated with an experiment, use languages and semantics to preserve artifacts, and re-execute /reproduce experiments by deploying the artifacts, changing datasets, algorithms, models, environments, etc. This reproducible framework will be adopted by and integrated within community infrastructures of three geoscience sub-disciplines viz. Hydrology, Solid Earth, and Space Science. Using cross-disciplinary science uses cases from these sub-disciplines, and engaging independent evaluators, we will assess the effectiveness of the framework in achieving reproducibility of computational experiments. Finally, verified results will be associated with ?stamps of reproducibility?, establishing community recognition of computational experiments. The framework will be developed as an EarthCube capability, with software developed and released as per EarthCube requirements. Early adopters across other geoscience sub-disciplines will be continually sought.
Agency: NSF | Branch: Standard Grant | Program: | Phase: Cyberlearn & Future Learn Tech | Award Amount: 299.65K | Year: 2016
The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. This project will begin to build a testbed for research partnerships to design and test cyberlearning systems for smart and connected communities for learning. Doing this type of research requires partnerships between educators, researchers, technologists, and youth, and enduring relationships between organizations within a city. The Digital Youth Network at DePaul designs learning systems aiming to ensure that all youth, especially the underserved, cultivate the critical skills, literacies and agency necessary to have the opportunity to create lives that are engaged, empowered and successful. Building on prior successes of testing its own learning technology initiatives across the city of Chicago, this project will help DYN serve as a testbed for other researchers working towards models of learning with technologies at the scale of a large city. This will allow many more researchers to more effectively build and study innovative ways to deploy technology to empower diverse learners at that scale.
The project will explore the creation of frameworks and protocols to reduce the friction in creating research partnerships to support the creation of ecosystems that support the fluid integration of the technologies, learning frameworks, activity structures, curriculum, and human capital, necessary to support learning across space, place, and time. To achieve this goal, the project will conduct four activities: (1) Playtest and pilot three existing NSF-funded projects with DYN formal and/or informal learning partners, and refine models for testbed partnerships based on these experiences; (2) Develop case studies that shed light on the problems of practice (from diverse stakeholder perspectives) around the creation and implementation of a city-scale testbed within a community learning hub; (3) Develop protocols for differential sharing of student learning data with hub partners and aggregate learning data with the larger cyberlearning community; and, (4) Host a two-part workshop that brings together a diverse group of stakeholders, including hub partners, to develop a testable model for the development and implementation of research-instrumented innovation hubs designed to support playtesting of still-in-development NSF interventions with under-resourced communities.
Agency: NSF | Branch: Standard Grant | Program: | Phase: SOFTWARE & HARDWARE FOUNDATION | Award Amount: 499.58K | Year: 2016
Replication is a key ingredient in achieving scalability and high availability of computer systems. This is evident for globally distributed databases, but it is also increasingly true for individual computers, smartphones and even watches, with multiple layers of cache coordinating communication between multiple processor cores. Where there is replication, consistency is necessarily at conflict with performance and availability. Strong notions of consistency allow simpler application development and debugging, at the cost of performance. This project explores weaker notions of consistency, investigating the extent to which performance gains can be achieved while maintaining a relatively simple programming model.
This project investigates abstraction, and under what circumstances can a client programmer safely reason about a data structure using its sequential specification. The project develops a common foundational framework that unites the areas of distributed data structures, relaxed memory models and concurrent data structures. The project explores foundations for languages and tools for shared-memory multi-core programming. By improving the programming model, the project encourages better use of the resources available in current and future hardware.
Agency: NSF | Branch: Standard Grant | Program: | Phase: Secure &Trustworthy Cyberspace | Award Amount: 264.84K | Year: 2016
Database Management Systems (DBMS) have been used to store and process data in organizations for decades. Larger organizations use a variety of databases (commercial, open-source or custom-built) for different departments. However, neither users nor Database Administrators (DBAs) know exactly where the data is stored on the system or how it is processed. Most relational databases store internal data using universal principles that can be inferred and captured. This project will build tools that draw on these principles to offer x-ray vision into storage of many DBMS, illustrating exactly what is happening inside. This research benefits users from a variety of backgrounds: students, teachers, database users, DBAs and forensic analysts. Tools developed by the research team enable DBAs to inspect storage and observe any leaking data, thereby helping forensic analysts discover what happened in a database during an attack. Users are given the power to restore data that was deleted in the face of a critical corruption event and recover it. The same tools help students understand concepts of database operations by their use in introductory courses during which students observe security vulnerabilities.
Some DBMSs provide profiling and recovery tools, but the functionality is always database-specific and varies wildly across different platforms. This research project standardizes basic profiling and data recovery capabilities and delivers a universal solution for most major relational DBMS. This solution includes recovery against corruption events that can cause data loss or incapacitate any modern DBMS; reconstruction of unrecoverable (i.e., discarded or deleted) data; and visualizing artifacts that offer insight to forensic analysts. The tools built in this project focus on providing easy-to-use and intuitive visualization of all deconstructed DBMS content from disk and RAM and recommend strategies for minimizing data leaks. Development and evaluation is done in collaboration with Information Technology (IT) professionals and academic DBAs as well as industry partners. This project also produces a suite of standard benchmarks that can quantify data leakage and recovery rates for different databases. Finally, the visualization tools and benchmarks are combined into training tutorials and student lessons both for database and security curriculums.
Agency: NSF | Branch: Standard Grant | Program: | Phase: STEM + Computing (STEM+C) Part | Award Amount: 999.44K | Year: 2015
DePaul University, in partnership with Loyola University Chicago, the University of Illinois Chicago and the Chicago Public School District (CPS), proposes a project -- Accelerate ECS4ALL -- that will create a robust peer-coaching model to support the growing number of novice Exploring Computer Science (ECS) teachers in Chicago and across the country. Over the last few years, under the Taste of Computing project and CPS CS4All initiative, over 100 teachers have participated in the ECS Professional Development, over 60% of CPS high schools are now offering the ECS curriculum, and approximately 7,000 students are taking the course each year. Of these students, 43% are female, 41% African American, and 43% Hispanic, significantly increasing exposure to CS among traditionally underrepresented populations. Peer coaching is needed now to sustain these successes, ensuring that novice teachers are able to fully embrace the guided-inquiry, equitable pedagogical approach of ECS and live it in their classrooms.
A Coach in Development Program will be developed and 15-20 experienced ECS teachers will participate in the program before being deployed as peer coaches in CPS high schools. They will, for example, observe classrooms through visits and video recordings, and work with teachers on reflecting, planning, goal-framing, and analysis of student work. The project will also support recruiting efforts targeted at teachers and administrators to facilitate 100% enrollment of CPS schools in CS4ALL.
Agency: NSF | Branch: Standard Grant | Program: | Phase: CROSS-DIRECTORATE ACTIV PROGR | Award Amount: 150.00K | Year: 2017
Although crime has decreased across most major US cities, violent crime is still a prominent issue in many urban environments. Neighborhoods that are plagued by such excessive violence endure negative effects on social and economic development. Though there are several approaches to addressing gun violence, one successful public health approach to reducing violence focuses on violence interruption, a neighborhood-level model whereby trained intervention workers identify potential violent events and provide immediate alternatives to disrupt violent incidents in their communities. This project investigates the potential of a mobile phone application to help intervention workers predict and interrupt violence during crimes and violent incidents. Violence interruption workers also provide long-term outreach in an attempt to permanently change violent behavior, including providing access to resources such as mental health services, social services, educational opportunities, and work training. This award will also support the design and development of a mobile application that uses predictive analytic techniques to provide violence interruption workers with the most effective intervention strategies based on data from over a decade of prior interventions.
Working with a leading violence interruption organization, this project examines how a mobile application can improve violence interruption outcomes by providing recommendations for the most effective strategies for resolving violent conflicts. The PI and her students will evaluate the mobile application in a 3-month deployment in Chicago during the summer, typically the most violent period of the year. In addition to supporting the reduction of violence, results from this project will lead to a deeper understanding of how to design technology to scale effective violence interruption techniques informed by public health principles. Furthermore, using computational analysis techniques to provide the most effective interruption techniques to violence interruption workers will not only advance our knowledge of the role of technology in violence interruption, but also extend prior literature pertaining to the social and behavioral impact of technology use in crime prevention and violence for those most affected by violent crime. This work will support improvements in the science of broadening participation, in that the focus will be on residents in low income, high crime neighborhoods and local, nonprofit organizations attempting to reduce and mitigate violence in their communities.