Grinnell College is a private liberal arts college in Grinnell, Iowa, U.S. known for its rigorous academics and tradition of social responsibility. It was founded in 1846, when a group of New England Congregationalists established the Trustees of Iowa College.In its 2014 edition of "America's Best Colleges", U.S. News & World Report ranked Grinnell 17th among all liberal arts colleges in the United States, and third highest for economic diversity as measured by low-income students receiving federal Pell Grants. Grinnell had an acceptance rate of 27 percent in 2014. Wikipedia.
Agency: NSF | Branch: Standard Grant | Program: | Phase: SOFTWARE & HARDWARE FOUNDATION | Award Amount: 159.99K | Year: 2016
Type-directed programming is a powerful programming paradigm found in strongly-typed functional languages where the types of a program are used to guide its development. Users of such languages frequently comment that their programs write themselves once they declare the appropriate types. In reality, the actual development process is far from automatic; developers still must apply manual reasoning principles to derive their program even though many of their choices are forced by the languages type system. This project aims to mechanize the type-directed programming process by leveraging techniques from program synthesis and type theory. The intellectual merits of this project are twofold: (1) the expansion of the theoretical foundations of program synthesis with types and (2) the application of these foundations towards program assistance tools that aid in type-directed programming. Beyond merely providing a tool that enhances the productivity of current functional programmers, the projects broader significance and importance is the crystallization of the benefits of type-directed programming in a form that allow non-functional programmers to understand, appreciate, and directly benefit from this programming paradigm.
The project extends prior work in the foundations of program synthesis with types, addressing issues of expressiveness and scalability encountered when adopting these foundations into synthesis tools. Notably, the project unifies type-based and verification-based approaches to program synthesis, allowing rich support for both algebraic and primitive data types as well as providing a common framework for understanding both styles of synthesis. In addition, the project investigates semi-automated, rather than fully-automated, program synthesis where the user interacts with the synthesis tool throughout the synthesis process. The basis of this approach lies in adopting the refinement tree, a data structure that captures the potential shapes of programs that a synthesizer can produce, into a useful data structure for visualizing and interacting with this tool. By pursuing semi-automated synthesis, these tools scale up to real-world programming environments by using the developer as an oracle whenever the tool would otherwise take too long or get stuck searching for a solution.
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 220.40K | Year: 2011
With this award from the Major Research Instrumentation (MRI) Program, Professor Martin Minelli from Grinnell College and colleagues Charles Cunningham, T. Andrew Mobley and Stephen Sieck will acquire a benchtop X-ray diffractometer. The proposal is aimed at enhancing research training and education at all levels, especially structure studies in areas such as (a) molybdenum coordination complexes, (b) bimetallic organometallic compounds, (c) organic nitrogen (nitrone) compounds, and (d) intermetallic compounds.
An X-ray diffractometer allows accurate and precise measurements of the full three dimensional structure of a molecule, including bond distances and angles, and provides accurate information about the spatial arrangement of a molecule relative to neighboring molecules. The studies described here will impact a number of areas, including organic and inorganic chemistry, materials chemistry and biochemistry. This instrument will be an integral part of teaching as well as research in the chemistry and physics departments.
Agency: NSF | Branch: Standard Grant | Program: | Phase: S-STEM:SCHLR SCI TECH ENG&MATH | Award Amount: 99.17K | Year: 2012
The collaborative project between Knox College and Grinnell College is developing adaptable laboratory and curricular materials for an advanced chemistry course using a context-rich pedagogical approach. The project is (1) introducing modular spectroscopic instrumentation and computational techniques into the physical chemistry curriculum, (2) guiding the students in developing skills that will let them apply their knowledge of physical chemistry concepts to interdisciplinary applications, (3) assessing the impact of contextualized laboratory experiments to student comprehension and attitudes, and (4) disseminating the materials and assessment findings to the community.
The project is building on a prior NSF funded project, the Physical Chemistry with a Purpose module series (DUE 0340873) by developing complementary laboratory exercise that introduce students to selected spectroscopic techniques and computational methods used in modern research. Experiments are being developed featuring some combination of Raman, UV-Vis, fluorescence, and NMR spectroscopy, along with computational chemistry and mathematical modeling. The project is utilizing recent developments in lecture and laboratory pedagogies of active, contextualized learning to assist students in developing a deeper appreciation for, and understanding of, physical chemistry and its relationship to other scientific disciplines. Modular spectrophotometry equipment is adding capabilities in Raman, surfaced-enhanced Raman, time-resolved fluorescence, and chemiluminescence to the Physical Chemistry Laboratories. The Gaussian suite of programs is being used for molecular modeling and Mathematica is being used for the mathematical modeling.
Easily adaptable materials, relying on relatively low cost instrumentation, are being developed and made available to the community at large through both common dissemination pathways and via multiple digital repositories. The materials are being tested utilizing the diverse student populations at the collaborating schools and in both semester and trimester settings. Guided by the intended student learning outcomes for each module, the assessment and evaluation activity is using both empirical and qualitative approaches and is being consistently administered at both intervention sites. These data are being compared to similar data collected prior to the implementation of the intervention in order to establish how well the project is meeting its goals for improving student learning outcomes.
Agency: NSF | Branch: Standard Grant | Program: | Phase: LONG-TERM RSCH IN ENVIR BIO | Award Amount: 37.44K | Year: 2013
This project continues a 7-year study of environmental and demographic variation across the geographic range of a native California plant in order to understand the ecological and evolutionary limits on an organisms distribution. Prior work showed that environmental variables related to climate, soils, and interactions with insect pollinators and seed-eaters all change across the plants range. Population growth rates, a measure of population viability, also decline toward the range edge. Over the next five years, research will examine the role that dormant seeds, which remain buried in the soil for 2-5 years, play in allowing populations to weather adverse environments and persist long-term. The project will also determine range-wide differences in the longevity of dormant seeds.
With impending climate change, parts of the current geographic range of many organisms are likely to become unsuitable for continued persistence. Understanding what limits species from colonizing unfavorable environments now can help forecast species responses to deteriorating conditions within their current range. In addition to its scientific impacts, the project will serve an important educational mission by engaging high school students, undergraduate students from research and primarily undergraduate institutions, and graduate students in research. Project leaders will work with high school students from a rural California high school and from a charter school in St Paul Minnesota through in-class activities and field projects.
Agency: NSF | Branch: Standard Grant | Program: | Phase: MAJOR RESEARCH INSTRUMENTATION | Award Amount: 61.81K | Year: 2014
Non Technical Abstract:
The LI-COR Odyssey Infrared Imaging System acquired through this award will enable highly quantitative investigations in diverse areas of research such as mitotic spindle function, muscle function, obesity, receptor function, and plant responses to heat stress. This instrument uses infrared (IR) technology for the highly sensitive and accurate measurement of biological molecules over several orders of magnitude, thus facilitating the detection of very low but biologically meaningful signals in a diverse group of applications, including protein and nucleic acid gels, protein arrays, In-Cell Westerns and tissue sections. Further, this instrumentation will be incorporated into undergraduate science education and research training programs in biology, biochemistry, neuroscience, and psychology.
This highly sensitive imaging system will be used in a broad range of ongoing research projects, making previously qualitative assays quantitative. In particular, the Odyssey imaging system will be used to: a) examine the role of the protein myosin-10 in mitotic spindle function by facilitating dominant-negative analysis; b) investigate the involvement of high-fat-diet-induced changes in levels of the adipose hormone leptin in the hippocampus; c) analyze the contribution of the 3 untranslated region of the Rubisco activase gene to the stability of the Rubisco activase protein and thus to the heat stress response in plants; d) explore the role of perisynaptic glial cells at the neuromuscular junction by determining whether they express COX-2 in response to stimulation of their muscarinic acetylcholine receptors; and e) monitor the isolation and purification of neuronal nicotinic acetylcholine receptor subunits for use in studies to analyze drug-receptor interactions.
Agency: NSF | Branch: Standard Grant | Program: | Phase: S-STEM:SCHLR SCI TECH ENG&MATH | Award Amount: 199.97K | Year: 2011
The investigators on this project are developing, implementing, and evaluating interactive Web-based games and corresponding investigative laboratory modules (labs) to effectively teach statistical thinking and the process of scientific inquiry to undergraduate students. Each game-based lab presents a research question in the context of a case study and encourages students to follow a complete process of statistical analysis. These materials consist of one- or two-day activities designed for introductory college courses as well as more complex projects geared toward upper level undergraduate courses. The game-based labs provide early opportunities for students to experience the role of a research scientist and to understand how the field of statistics helps advance scientific knowledge. The intellectual merit of this project lies in its contribution to the scholarship of statistics education by combining cutting-edge game-based technology with realistic research problems to foster statistical thinking at multiple stages of a students academic career. The approach embraces recommendations from the Mathematical Association of America and the American Statistical Association that encourage students to collect data, determine an appropriate technique for analysis, use technology, perform the analysis, make inferences, interpret and then present the results. The broader impacts of the project are felt through its creation of a new educational model that holds strong potential to influence the direction of statistics education. In particular the game-based labs enable the exploration of individualized research questions and just-in-time feedback that students recognize as directly related to the goals within their game. Providing students with intriguing real-world problems that demonstrate the intellectual content and broad applicability of statistics as a discipline encourages students to consider a career in statistics or to incorporate statistical thinking into any career.
Agency: NSF | Branch: Standard Grant | Program: | Phase: EVOLUTIONARY ECOLOGY | Award Amount: 214.06K | Year: 2015
Colors of male and female animals can be strikingly different, a condition usually explained by the advantages provided in attracting mates. However, the sexes may vary over a wide spectrum, from very different in color to identical, and in some species one sex can vary across typically male or female colors. This study tests whether colorful pigments function not as a visual mating signal, but rather as antioxidant protection against damaging effects of UV radiation. The researchers extend their past research documenting the spectacular variation in color in endemic Hawaiian damselflies, including the discovery that sexual and population variation in color is correlated with each sexs solar exposure, which varies substantially across different island habitats. Undergraduate students at both RUI institutions will assist in research and in developing curricular materials and an exhibition that explores color in nature, both scientifically and artistically. This project contributes to our understanding of genotype to phenotype mapping and tests a novel hypothesis about pigmentation and antioxidant function.
The researchers will (1) identify how body color is correlated with UV exposure, which varies with sex and species-specific microhabitats; (2) test whether sexual selection can explain the habitat and color correlations; (3) connect color variation with survivorship in different habitats, and (4) identify pigment chemical identity and confirm its antioxidant function across many species of the radiation. A direct test of the pigments antioxidant function provides a new perspective on the causes of variation between species and sexes, and sets the stage for future studies on color variation, including its genetic basis, its role in crypsis, and the role of selection on color vs. gene flow in species diversification.
Agency: NSF | Branch: Continuing grant | Program: | Phase: CAREER: FACULTY EARLY CAR DEV | Award Amount: 371.69K | Year: 2017
Observations of planets around other stars will be one of the main targets of the future James Webb Space Telescope and the 30-meter diameter telescopes under construction here on the Earth. The investigator will create computer programs to model the observations of the atmospheric structure and composition of those planets near to Earth in size, or super-Earths. These programs will be used to study the characteristics of atmospheres, focusing on their temperatures and pressures, of many different types of super-Earths. The investigator will make these programs broadly available to other scientists to use for their research. This project serves the national interest as it helps our scientific understanding of the properties of planets around other stars that are similar to the Earth, and which could harbor life. The investigator will also teach special courses for students with a low level of STEM preparation, and create a peer mentoring program for STEM students coming from backgrounds that traditionally are not represented in these fields, to fight lower success rates in STEM classes at her home institution.
The investigator will build a set of computational tools that will be broadly applicable to modeling the radiative transfer and atmospheric chemistry for low-mass exoplanets in the Milky Way galaxy. The research will first complete the development of Exo-RT, a robust radiative transfer (RT) suite of codes that will calculate atmospheric structure (temperature-pressure profiles), emission spectra, and transmission spectra for transiting exoplanets. The key improvement of this RT suite over existing codes is its inherent ability to model atmospheres of arbitrary atmospheric composition, making it ideally suited toward modeling a diverse set of super-Earths. The fully developed code will then be applied to a number of studies of super-Earth atmospheres, determining the extent to which the attributes of a super-Earth can be inferred from observations of its atmosphere and measurements of its bulk density. Atmospheric studies of super Earths are a key science motivation for future exoplanet investigations with the James Webb Space Telescope and ground-based 30-meter class telescopes. At her home institution, Grinnell College, the investigator will also implement a spatial reasoning course for students with a low level of STEM preparation, and a peer mentoring program for STEM students from traditionally underrepresented groups, to combat lower success rates in STEM classes.
Agency: NSF | Branch: Standard Grant | Program: | Phase: COMPUTATIONAL MATHEMATICS | Award Amount: 116.31K | Year: 2016
Traditionally, a signal is measured by acquiring every component in the signal and then compressing the signal with an appropriate computational algorithm. For example, digital cameras capture an image with a huge number of pixels and then a compression scheme such as JPEG is used to reduce the size of the digital image for storage or dissemination. In many applications, the costs and challenges associated with acquiring measurements are considerable. In compressed sensing and matrix completion, the measurement process is altered in order to drastically reduce the number of measurements, but the signal reconstruction process is necessarily more difficult. Compressed sensing and matrix completion transfer the workload from the measurement process to computational resources dedicated to the signal reconstruction. Typical applications include compressive radar, geophysical data analysis, medical imaging, and computer vision. This project will take a holistic approach to data acquisition and algorithm development for compressed sensing and matrix completion where theoretical guarantees often rely on computationally expensive subroutines and apply to computationally burdensome measurement processes. Increased efficiency can be achieved through sparse measurement operators, relaxed subroutine requirements in iterative greedy algorithms, and the implementation of these algorithms on computation accelerating hardware.
Compressed sensing combines the acts of signal acquisition and compression into a single operation. Computationally efficient algorithms then produce accurate approximations to sparse signals by exploiting the underlying simplicity that the signal has relatively few important components. Matrix completion similarly exploits the simplicity of the target matrix having only a few independent columns; in other words, one recovers a low rank matrix from a limited number of measurements. While leading greedy algorithms for compressed sensing and matrix completion have theoretical guarantees defining the number of measurements required for accurately recovering the underlying low dimensional signal, these guarantees require many more measurements than practical for applications. Furthermore, many of the algorithms employ theoretically useful but computationally expensive subroutines. Observed performance of more computationally efficient measurement operators encourages the adoption of techniques in practice that lack worst case, uniform guarantees for acquisition and reconstruction. This project seeks to balance the competing desires for theoretical guarantees and fast, efficient algorithms. The project will pursue theoretically viable algorithms which are also practically useful and provide solutions to linear inverse problems in reasonable amounts of computational effort including power, time, and affordable hardware. At the same time, establishing empirical performance characteristics for computationally efficient measurement operators and recovery algorithms which lack precise guarantees will help guide practitioners and theorists in future research. To provide near real time solutions to these computationally intensive algorithms, the project will also further accelerate computation by designing and disseminating algorithm implementations which exploit the massively parallel computations available on high performance computing graphics processing units.
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 202.14K | Year: 2015
Libraries and archives are digitizing historical maps for widespread online access. Without technology for searching them, large map collections relevant to a given problem or question may remain obscure even in online archives. If all of the text in a map can be read automatically by computer, a wealth of information becomes quickly available -- location names, geographic features, and often statistics. This project will increase capacity for search and analysis of historical maps by automatically recognizing place names and other text in these digitized artifacts while simultaneously aligning them with modern geography. The improvements this project will make to current text recognition methods will facilitate more powerful uses of humanitys trove of old maps -- for example, by allowing scientists and policymakers to establish changes in land usage, waterways, or borders over time. By creating free, open-source tools for studying historical maps, this project will increase public engagement with science and technology and empower any Internet user to explore the intersection of technology and history. This research will train a diverse group of graduate and undergraduate students in constructing, learning, and making predictions with adaptive models featuring heterogeneous yet highly interdependent entities.
Although many institutions are digitizing hundreds of thousands of historical maps, most digitized map images are poorly annotated, limiting their usefulness. Manual annotation and metadata association is highly laborious. This projects primary objectives are (1) to fully automate text and shape-based georeferencing (aligning map images to the known global geography) while (2) indexing words and place names (for search) by enhancing text detection and recognition methods in these complex artifacts. These innovations will address the shortcomings of manual georeferencing and current automated text recognition algorithms. The researchers will employ an iterative interpretation process for solving problems including text/graphics separation, text recognition, and georeferencing. For example, the fact that all members of a given class of text entities on a map (e.g., county names) are typically rendered in the same text style can be used to inform predictions about difficult members of the category with information derived from more easily-recognized members. The researchers will use a dataset of annotated maps containing over 12,000 words in 9,000 place names as benchmark data for testing the algorithms developed in the project. Software, data, and benchmarks will be broadly distributed on the project website (http://www.cs.grinnell.edu/~weinman/research/maps.shtml). Findings will be shared with the research community through journals and conferences in the computer vision, artificial intelligence, and GIS communities.