SUNY at Stony Brook

Stony Brook, NY, United States

SUNY at Stony Brook

Stony Brook, NY, United States

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Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Macromolec/Supramolec/Nano | Award Amount: 565.14K | Year: 2016

This research project enables the synthesis of next generation functional polymers (plastics) with precise sequences of building blocks. The precise sequence of the synthesis allows preparation of the polymers in bulk quantities and these quantities enable the researchers to determine the effects of the synthetic sequences on the physical properties of the new polymers. These synthetic methods are readily adoptable by the polymer community because the materials are prepared in one-step from simple starting materials that are commercially available. This project trains graduate students to pursue interfacial research projects that solve problems at the chemistry-materials-chemical engineering interface. The research impact is transferred to the broader community through formal training in general public communication under the auspices of the Alda Center for Communicating Science at Stony Brook University. Using techniques to improve audience engagement and for distilling their message, researchers engage local high school students and college students in understanding the activities of this research.

This project is centered on developing alternating ring-opening metathesis polymerization syntheses that provide materials with monomer-level control of sequence. With the successes in ruthenium catalyst development provide both functional group tolerant and rapidly propagating catalysts, the next goal is to develop monomers that generate materials with previously inaccessible types of nanoscale morphologies. The Sampson laboratorys recent discovery of the bicyclo[4.2.0]oct-1(8)-ene-8-carboxamide-/cyclohexene system allows the preparation of very long, alternating polymers with high monomer economy. An aggressive determination of the scope of the alternating polymer reaction is planned. This project identifies polymer microstructures that can and cannot be achieved. The chemistry research provides a unique opportunity to explore and discover novel nanoscale structures formed as a consequence of precise macromolecular sequence control.


Grant
Agency: NSF | Branch: Continuing grant | Program: | Phase: ALLIANCES-MINORITY PARTICIPAT. | Award Amount: 1.58M | Year: 2016

The Louis Stokes Alliances for Minority Participation (LSAMP) program assists universities and colleges in diversifying the science, technology, engineering and mathematics (STEM) workforce through their efforts at significantly increasing the numbers of students from historically underrepresented minority populations to successfully complete high quality degree programs in STEM.
The State University of New York Louis Stokes Alliance for Minority Participation (SUNY LSAMP) has been funded for 20 years and has built a synergistic collaboration of 14 public higher education institutions in the state of New York that provide a diverse mix of academic strengths and capabilities. Stony Brook (lead institution), Albany, Binghamton, Buffalo, College at Buffalo, Farmingdale, New Paltz and Old Westbury (4-year institutions) and community colleges: Dutchess, Nassau, Orange, Schenectady, Suffolk and Westchester colleges comprise the partnership. Over the next five years, the goals of the project are to:

--meet the grand challenge of preparing underrepresented minority students for successful transition into science, technology, engineering and mathematics (STEM) majors;
--focus on providing experiential activities that lead to socialization into science;
--promote significant systemic change by increasing research on broadening participation.

The primary focus of SUNY LSAMP is to ensure student success in completing undergraduate and graduate degrees in STEM disciplines. Interventions are primed for a more intentional focus on LSAMP students success at the community college transfer level and successful entry into graduate school in the completion of STEM degrees.
The SUNY LSAMP program is comprehensive and designed to enhance academic and research outcomes including well-prepared underrepresented minority students in STEM disciplines equipped for success in graduate school, engaging mentoring and research student retention models, and a sound and rigorous evaluation plan. The program will leverage its institutional research infrastructure and its partnerships with other state organizations and STEM projects to facilitate successful transitions of minority students in STEM fields.

The goal of the study, which focuses on community college participants, is to identify an evidenced-based formula for understanding what components of STEM enrichment programs contribute to STEM success and why among underrepresented minority students. This is a multi-institutional research project that utilizes a theoretically grounded model - Fostering (STEM) Identity through Transitions (FIT) Model -to explore issues of STEM success. The study has the potential to fine-tune the FIT theoretical model resulting in the continuing development of enrichment programming in STEM throughout the SUNY system and for use at other institutions.

Alliance program evaluation findings assessing the effectiveness of the SUNY LSAMP strategies will be shared with the education community to build the knowledge base and foster implementation of best practices. In addition, the project includes a study to identify an evidenced-based formula for understanding what components of STEM enrichment programs contribute to STEM success among underrepresented minority students.

Over the past 20 years, the SUNY LSAMP program has yielded amazing outcomes including a quadrupling of STEM B. S. degrees to students in historically underrepresented populations. The alliance has achieved an eleven-fold increase in STEM enrollment for this same population of students during this same period. Since 2011, community college students transferring to 4-year STEM undergraduate programs has nearly doubled.

The best practices implemented in this project will have transferable values for STEM activities in other institutions in the nation. It will also create an environment in which the outcomes achieved will be sustainable after the project comes to an end. SUNY LSAMP will not only increase the number of successful students completing degrees in STEM disciplines, but is also committed to creating a more diverse and competitive STEM workforce in the nation.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: NSF Research Traineeship (NRT) | Award Amount: 2.99M | Year: 2016

This National Science Foundation Research Traineeship (NRT) award to SUNY at Stony Brook will provide science and engineering graduate students with unique interdisciplinary skills to assist and eventually lead in the translation of complex data-enabled research into informed decisions and sound policies. Within all sectors of industry and government, effective decision making depends on the ability of scientists to interpret data and communicate results in a way that supports the decision-making process. This new training program responds to this challenge with an interdisciplinary set of new courses and a suite of activities united by the theme of Scientific Training and Research to Inform DEcisions (STRIDE). It specifically will include the rarely explicitly taught skills of decision support, such as understanding the perspectives of stakeholders, science communication, and translating scientific uncertainty. The project anticipates training 90 PhD students, including at least 20 funded doctoral trainees, and a similar number of non-trainee MS and PhD students that will participate in program components from the departments of atmospheric and marine sciences, ecology and evolution, computer science, biomedical informatics, applied mathematics and statistics, journalism, and advanced computational science.

STRIDE will initially target environmental sustainability (including climate change, marine ecology and natural resource management, and illegal deforestation) and energy sustainability, and will add population health in the third year. Research in advanced visual data analytics to support decisions will be pervasive in all areas. The cross fertilization between disciplines focused on decision making will prepare students to make discoveries in the domain sciences and will lead to innovations in visual data analytics. To develop research skills in new contexts and to diversify career perspectives, trainees will have summer externships at non-academic partners such as IBM Research, Brookhaven National Laboratory, and the National Marine Fisheries Service, with new partners being added each year. The program comprises three major components: 1) a set of specially designed courses on decision support, spatial data analysis, visualization, and communication required for all students; 2) training in a STEM domain discipline; and 3) a set of non-course-based program elements in which all students will participate, including recruitment, skill development, professional development, and personal development. In addition to degrees in their domain-science disciplines, students will receive a graduate certificate from STRIDE after completing the three proposed courses and program activities. Specific innovations to be tested by rigorous evaluation include the seminar course in scientific decision support that will feature many government/industry scientists, decision makers, and journalists remotely leading discussions on the science, societal, and other challenges associated with decision support in their respective fields. Another new course focusing on science communication for decision makers will be provided by the Alan Alda Center for Communicating Science in the School of Journalism.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: ALLIANCES-MINORITY PARTICIPAT. | Award Amount: 1.07M | Year: 2016

The Louis Stokes Alliances for Minority Participation (LSAMP) program assists universities and colleges in diversifying the STEM workforce through their efforts at significantly increasing the numbers of students from historically underrepresented minority populations to successfully complete high quality degree programs in science, technology, engineering and mathematics (STEM) disciplines. The LSAMP Bridge to the Doctorate (LSAMP-BD) activity provides two-year support at the postbaccalaurate level for students from historically underrepresented minority populations to matriculate in STEM graduate programs with the ultimate goal of earning a doctoral degree in a STEM discipline. Participants are selected from LSAMP institutions nationwide. The State University of New York System Louis Stokes Alliance for Minority Participation (SUNY LSAMP) at Stony Brook University is the host site for the 2016-2018 BD program in which a cohort of twelve LSAMP certified students selected from the national pool will engage in STEM research, academics and professional development activities leading to STEM Masters and Doctoral degree completion. The BD program at Stony Brook University provides a comprehensive set of support services that monitors student progress, builds a strong BD community, and increases BD students academic and professional skills.

Ten cohorts of students have matriculated in SUNY LSAMP graduate LSAMP-BD programs at Stony Brook, Buffalo, Binghamton and Albany since 2006. The programs promote systemic change in graduate education practices and policy in ways that increase the success of individual students on the doctoral pathway and the effectiveness of STEM graduate programs, continuing the goal of diversifying Americas STEM workforce. The program at Stony Brook University will be externally evaluated and students will be tracked throughout the program and into STEM careers following completion of STEM doctoral degree programs. Collaborations with other STEM networks and resources internal and external to the state as well as other graduate programs, such as NSFs Graduate Research Fellowship Program (GRFP), Alliances for Graduate Education and the Professoriate (AGEP-T) and institutional resources will further ensure that students complete the STEM terminal degree.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Big Data Science &Engineering | Award Amount: 731.26K | Year: 2016

The sparsity of large networks makes it difficult to efficiently extract features for machine learning algorithms. Recent work on network embeddings (DeepWalk) has revealed how neural language modeling can be applied to a very general class of graph analysis problems in data mining and information retrieval. This project will improve training algorithms and data representation for large-scale networks, creating better, more powerful graph embeddings for weighted and attributed networks. It will also enable meaningful comparison of the relative performance of network connectivity features vs. more text-oriented features. It is possible that there might be more usable information in links than in the readable content itself.

This project will develop these methods in several new directions, including extensions to new graph classes and speed/scale enhancements. The original DeepWalk induced latent representations only from unweighted, undirected, and connected graphs. But there is considerable interest in applying it to more general graphs arising in data analysis. Doing the right thing on such natural networks as bipartite and disconnected graphs presents surprisingly subtle issues of theoretical and practical significance. This project will also explore several ideas to increase training performance of network embeddings, including more efficient gradient updates and improved graph sampling methods and particularly the power of self-avoiding random walks to oversample otherwise rare nodes. This project seeks to extend the effective range of DeepWalk by several orders of magnitude, from the 10 million vertex graphs we routinely handle today to web-scale networks on billions of nodes. The broader impacts of this work are far reaching across data mining and information retrieval, including user profiling/demographic inference, online advertising, and fraud detection. The software and data resources developed under this research project will be released as open source. They will be directly applicable to the biomedical and social sciences, and serve as both an educational and scholarly resource. For further information, see the project website at http://www.cs.stonybrook.edu/~skiena/deepwalking.


Grant
Agency: NSF | Branch: Continuing grant | Program: | Phase: Elem. Particle Physics/Theory | Award Amount: 650.00K | Year: 2016

This award funds the research activities of Professors Maria C. Gonzalez-Garcia, Christopher P. Herzog, Patrick Meade, Leonardo Rastelli, Martin Rocek, Robert Shrock, Warren Siegel and George Sterman at Stony Brook University.

Physics is constantly evolving as new techniques and capabilities are developed to understand the natural world. This award supports research in a broad but deeply related set of topics in Theoretical Physics, and engagement at each of these frontiers of knowledge serves the national interest by advancing fundamental scientific progress in the United States. Investigations will include the analysis of high-energy collisions of elementary particles, which probe the fundamental laws upon which the material universe is based, and proposals for new experiments involving elementary particles and atomic nuclei. These subjects are closely related to the deep mysteries of gravity, space and time, and the possibility that all of the known forces in nature are related, possibly unifying gravity itself with the forces that are seen in electrical phenomena, in radioactivity, and in nuclear physics. The theoretical methods developed in particle and nuclear physics have also been found to have exciting applications to chemical compounds and metals with unusual properties, like superconductivity. New theoretical methods, sometimes relying on modern computational capabilities, are making possible reliable predictions for the properties of solids and liquids that were previously thought intractable. This research, by advancing our knowledge of the laws of nature and by contributing to a better understanding of the physical universe, including practical materials, has significant broader impacts and implications for our world view. Indeed, many of the topics under study by the senior participants are frequently discussed in the media, including the Higgs boson, black holes, the role of neutrinos in the Universe, string theory, the quark-gluon plasma, and quantum entanglement. The research carried out under this award will also serve in the training of graduate students and mentoring of postdoctoral fellows at the highest levels. The faculty on this proposal conduct their research with graduate students, and share their experience and expertise with undergraduates and community members, in and beyond the classroom. Individual members will also continue their own outreach activities, which have included successful traditions in high-school research mentoring and in the organization of science playwriting competitions.

At the technical level, this award will support research over a wide range of theoretical physics, largely based on quantum field theory and string theory. Recent years have seen a strengthening engagement of theory and experiment in particle physics, with new and exciting data from the Large Hadron Collider, from neutrino observatories and cosmic ray satellites. These new sources of information concerning our universe enable theorists to test long-standing ideas against evolving data, and to develop new theoretical methods to guide experiment. At the same time, novel applications of quantum field and string theory, such as applications of duality and the conformal bootstrap, have been developed in and beyond particle physics, opening unexpected avenues of research into nuclear physics, condensed matter physics, and quantum information, sometimes with applications in pure mathematics. As active participants in these historic developments, the senior personnel of this award will continue and develop their work in high energy collider phenomenology, quantum chromodynamics, in neutrino and astroparticle physics, in applications of gauge-gravity duality, conformal field theory, extensions of the Standard Model, electroweak symmetry breaking, superstrings, and the geometry of supersymmetric gauge theories. This research will also help train a new generation of theorists at the postdoctoral and graduate levels.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Chemistry of Life Processes | Award Amount: 550.01K | Year: 2016

With this award, the Chemistry of Life Processes Program in the Chemistry Division is funding Dr. Elizabeth Boon of the State University of New York Stony Brook for the investigation of a new protein in bacteria called NosP that may be involved in forming bacterial biofilms. A biofilm is generally a slimy, protective layer on a surface. Biofilms may form on living or non-living surfaces and can be found in natural, industrial, and hospital settings. Bacterial biofilms include dental plaques, some forms of pneumonia, and films that cover medical devices and harbor infectious diseases. These structures are sometimes resistant to antibiotics and harsh chemical treatments. Very little is known how biofilms form or how to destroy them. Dr. Boon is studying how biofilms are disrupted by nitric oxide. This research program provides broad training at the interface of chemistry with biology for undergraduate, graduate, and high school students. Dr. Boon has designed a course to help transition undergraduate students into research-based careers. She also collaborates with a local high school teacher so that underrepresented local students gain laboratory experience and the confidence to pursue careers in research.


Bacterial biofilms are a considerable threat to food and water safety because they cause persistent biofouling of food and food contact surfaces, and are resistant to antibiotics. Biofilm regulation by nitric oxide (NO) has been observed broadly in bacteria, thus interventions based on NO signaling could have an impact on food and water safety. Bacterial NO signaling is poorly understood. In this research project, the Boon group clones expresses, and purifies NosP and point mutants, then quantifies the specificity and affinity of diatomic ligands for NosP and its variants to determine if NosP binds NO at concentrations known to modulate biofilms (nanomolar). To gain insight into the heme environment and structure of NosP, Dr. Boon and her coworkers generate high-resolution crystal structures of NosP, as well as vibrational structures of the heme cofactor using electronic and resonance Raman spectroscopy. NosP is expected to be established as a novel NO sensor in P. aeruginosa. This research elucidates the molecular basis for NO signaling in P. aeruginosa and may open opportunities for controlling biofilms caused by this pathogen. Successful delineation of the role of NosP in P. aeruginosa may provide a basis for understanding NosP in other bacteria.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: EARS | Award Amount: 800.00K | Year: 2016

With the explosion of mobile data, there is a growing realization that the radio frequency spectrum must be treated as an important resource that is in limited supply. Policy makers and researchers alike are promoting various forms of spectrum sharing models to improve spectrum utilization. Just like any other resource with mismatched demand and supply, all steps towards better utilization of radio spectrum have also increased the need for large scale spectrum monitoring. This serves two key purposes: (i) it helps identify available spectrum opportunities, making spectrum sharing systems more effective, (ii) it can help us develop deeper understanding of spectrum usage and demand over time and space. Large-scale spectrum monitoring can feed into multitudes of spectrum-aware applications forming an entire ecosystem of spectrum data, analytics and apps. The proposed project develops an end-to-end enabling platform called SpecSense to support this vision. SpecSense (i) crowdsources spectrum monitoring using low-cost, low-power custom-designed hardware, and (ii) provides necessary library and interface support for spectrum-aware apps via a central spectrum server/database platform. This project is expected to foster interest in spectrum data marketplaces facilitated by crowdsourced spectrum sensing. This can engender commercial interests in various aspects of the spectrum data ecosystem. In many fields, e.g., healthcare, education, Internet-of-Things, there is a tremendous need for mobile bandwidth and innovation is stunted due to a lack of bandwidth. Success in this project will drive such innovations. The project will also contribute to various educational activities for students with a range of academic preparations.

This project addresses several of the core intellectual challenges in developing SpecSense, viz., (1) Exploration of FPGA-based sensors where sensing algorithms are built into the FPGA, with accompanying tools to automatically implement and optimize these algorithms so that they provide the desired trade-off between power and performance; (2) Novel interpolation techniques to estimate spectrum occupancy in both spatial and temporal domains; (3) Algorithms to support optimized selection of sensors to minimize overall sensing cost; (4) Development of an end-to-end testbed and evaluation over a range of spectrum-aware applications. The project team has a range of expertise in topics relevant to the proposal, such as automated hardware design, digital signal processing, detection and estimation, wireless networking, networking algorithms, and networked systems design.


Grant
Agency: NSF | Branch: Continuing grant | Program: | Phase: SOFTWARE & HARDWARE FOUNDATION | Award Amount: 95.89K | Year: 2017

Internet-of-Things (IoT) embedded with sensing, actuation, computing, communication and storage resources are expected to transform our homes, offices, neighborhoods and communities into smart environment, providing novel functions, services and creating potential economic impacts in trillions. Despite recent booming IoT business, there is a lack of foundational software/hardware architecture suitable for fine grained access, performance assurances and management critically demanded in smart environment. This research takes a principled approach to design and develop such a software/hardware architecture serving as a foundation for future IoT technology and applications, thus alleviating some of the critical technical obstacles impeding the trillions of economic potentials in diverse IoT applications. The PI will collaborate with the police and industry partners for technology pilots to assist officers for agile emergency response in their daily duties, and seniors to age at home with improved life quality. Students from Ph.D., M.S., to undergraduate and high school levels will be involved and trained during the research. Outreach to K-12 and local communities will leverage the research prototypes for science projects and makers clubs to foster a culture of technology and innovation.

This research promotes smart object command operations as first class citizens. It will develop software/hardware architecture and systems firmly rooted in flexible, fine-grained access, command execution assurances and scalable management demanded in smart environment. States and information related to access constraints are bundled into self-sufficient and self-protected command data units. The research will explore and develop mechanisms to ensure formal IoT command execution properties; essential hardware traffic forwarding and security functions to ensure low latency and protection of IoT operations; and configuration, monitoring, maintenance essential in smart environment management.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: ENG DIVERSITY ACTIVITIES | Award Amount: 598.95K | Year: 2017

The National Science Foundation has a strong commitment to broadening participation in STEM. This commitment is embedded in its Strategic Plan and investment priorities related to preparing a diverse, globally engaged science, technology, engineering, and mathematics (STEM) workforce and integrating research with education. To maintain the countrys competitive edge across the world, NSF funds projects that will help the nation with pinpointing effective strategies to attract, retain, and support underrepresented students in engineering. This project will measure the impact of strategies designed to encourage underrepresented, pre-collegiate students to pursue engineering careers, practices for developing and monitoring inclusive engineering education, and support systems that encourage underrepresented students to persist in engineering. The project is closely aligned with the NSF broadening participation priorities, and it has great potential to build upon the literature base on integrating engineering experiences for pre-collegiate students (e.g., high school) and informing current engineering practices and efforts at various junctures of the STEM pipeline, especially engineering. It is quite likely that it will contribute to the recruitment, retention, and graduation engineering literature for underrepresented students, thereby increasing their participation in engineering at every juncture of the educational pipeline.

Using a mixed-method research design, the investigators outlined a project that involved collecting both quantitative and qualitative data on how to best attract, retain, and support traditionally, underrepresented pre-collegiate and collegiate students in engineering. The project will apply a research paradigm based on an expectancy-value model and the theory of planned behavior through a two-pronged approach. First, an attraction program will concentrate on high school students and teachers/counselors to assess strategies for encouraging students to pursue engineering careers. These include afterschool activities in engineering disciplines for high needs, ethnically and gender diverse students (120 secondary students annually) and professional development to educate science teachers/counselors in engineering preparation, as well as in the diversity of engineering career pathways (40 teachers/counselors annually). Second, a retention program will focus on female undergraduate students and on faculty members, graduate students, and post-doctoral associates, with the aim of developing successful engineering educational practices that encourage women undergraduates to persist in the field. This component will consist of academic interventions for female students (25+ annually) as well as training for instructors (40+ annually) on inclusive practices. Overall, the projects research insights on pedagogical and counseling professional development, pre-college programs, and college retention programs, will elevate the engineering profession and national welfare by adding to the diversity of perspectives that will shape the future of technological advancement.

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