South Hadley, MA, United States
South Hadley, MA, United States

Mount Holyoke College is a liberal arts college for women in South Hadley, Massachusetts, United States. It was the first member of the Seven Sisters colleges, and served as a model for some of the others. Mount Holyoke is part of the Pioneer Valley's Five College Consortium, along with Amherst College, Smith College, Hampshire College, and the University of Massachusetts Amherst.The school was originally founded in 1837 by Mary Lyon as Mount Holyoke Female Seminary. Prior to founding Mount Holyoke Female Seminary, Mary Lyon founded Wheaton Female Seminary in Norton, Massachusetts in 1834. Mount Holyoke received its collegiate charter in 1888 as Mount Holyoke Seminary and College and became Mount Holyoke College in 1893. Mount Holyoke's chapter of Phi Beta Kappa was established in 1905.Mount Holyoke's buildings were designed between 1896 and 1960. It has a Donald Ross-designed 18-hole golf course, The Orchards, which served as host to the U.S. Women's Open in 2004. U.S. News & World Report lists Mount Holyoke as the 38th best liberal arts college in the United States in its 2013 rankings. Mount Holyoke was also ranked #1 in the nation for Best Classroom Experience in the Princeton Review 2010–2011 rankings. In 2011–2012, Mount Holyoke was one of the nation's top producers of Fulbright Scholars, ranking fourth among bachelor's institutions according to the Chronicle of Higher Education. Wikipedia.

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Rounds C.M.,Mount Holyoke College | Bezanilla M.,University of Massachusetts Amherst
Annual Review of Plant Biology | Year: 2013

Tip growth is employed throughout the plant kingdom. Our understanding of tip growth has benefited from modern tools in molecular genetics, which have enabled the functional characterization of proteins mediating tip growth. Here we first discuss the evolutionary role of tip growth in land plants and then describe the prominent model tip-growth systems, elaborating on some advantages and disadvantages of each. Next we review the organization of tip-growing cells, the role of the cytoskeleton, and recent developments concerning the physiological basis of tip growth. Finally, we review advances in the understanding of the extracellular signals that are known to guide tip-growing cells. © Copyright ©2013 by Annual Reviews. All rights reserved.

Corson C.,Mount Holyoke College
Antipode | Year: 2010

By exploring the shifting and uneven power relations among state, market and civil society organizations in US environmental foreign aid policy-making, this article forges new ground in conversations about conservation and neoliberalism. Since the 1970s, an evolving group of non-governmental organizations (NGOs) has lobbied the US Congress to support environmental foreign assistance. However, the 1980s and 1990s rise of neoliberalism laid the conditions for the formation of a dynamic alliance among representatives of the US Congress, the US Agency for International Development, environmental NGOs and the private sector around biodiversity conservation. In this alliance, idealized visions of NGOs as civil society and a countering force to corporations have underpinned their influence, despite their contemporary corporate partnerships. Furthermore, by focusing on international biodiversity conservation, the group has attracted a broad spectrum of political and corporate support to shape public policy and in the process create new spaces for capital expansion. © 2010 The Author Journal compilation © 2010 Editorial Board of Antipode.

Agency: NSF | Branch: Standard Grant | Program: | Phase: SPECIAL PROJECTS - CCF | Award Amount: 423.00K | Year: 2015

Reproducability is the cornerstone of scientific progress. Historically, scientists make their work reproducible by including a formulaic description of the experimental methodology used in an experiment. In an age of computational science, such descriptions no longer adequately describe scientific methodology. Instead, scientific reproducibility relies on a precise and actionable description of the data and programs used to conduct the research. Provenance is the name given to the description of how a digital artifact came to be in its present state. Provenance includes a precise specification of an experiments input data and the programs or procedures applied to that data. Most computational platforms do not record such data provenance, making it difficult to ensure reproducability. This project addresses this problem through the development of tools that transparently and automatically capture data provenance as part of a scientists normal computational workflow.

An interdisciplinary team of computer scientists and ecologists have come together to develop tools to facilitate the capture, management, and query of data provenance -- the history of how a digital artifact came to be in its present state. Such data provenance improves the transparency, reliability, and reproducibility of scientific results. Most existing provenance systems require users to learn specialized tools and jargon and are unable to integrate provenance from different sources; these are serious obstacles to adoption by domain scientists. This project includes the design, development, deployment, and evaluation of an end-to-end system (eeProv) that encompasses the range of activity from original data analysis by domain scientists to management and analysis of the resulting provenance in a common framework with common tools. This project leverages and integrates development efforts on (1) an emerging system for generating provenance from a computing environment that scientists actually use (the R statistical language) with (2) an emerging system that utilizes a library of language and database adapters to store and manage provenance from virtually any source. Accomplishing the goals of this proposal requires fundamental research in resolving the semantic gap between provenance collected in different environments, capturing detailed provenance at the level of a programming language, defining precisely aspects of provenance required for different use cases, and making provenance accessible to scientists.

Agency: NSF | Branch: Standard Grant | Program: | Phase: SOCIAL PSYCHOLOGY | Award Amount: 350.64K | Year: 2017

Among adults who co-sleep with a partner, the quality of their sleep is strongly related to the amount of conflict in their relationships. People report worse sleep after conflict and more conflict after poor sleep. However, little is known about why poor sleep and interpersonal conflict are related or what factors might increase or decrease the connection. Both interpersonal conflict and poor sleep are associated with chronic stress, reduced immune function, shorter lifespan, and lower life satisfaction. They also impose staggering social and economic burdens, costing the nation hundreds of billions of dollars every year and negatively impacting the development of children exposed to parental conflict. Understanding what drives links between conflict and sleep is necessary to reduce these tolls. This research will examine behavioral, emotional, cognitive, and psychophysiological pathways through which sleep and conflict are related over time. It will also test whether poor self-regulation (difficulty adjusting behavior, emotions, and thoughts in response to environmental demands) renders some people more vulnerable to negative links between conflict and sleep. The discovery of how specific relationship processes can promote sleep quality and how aspects of sleep quality can reduce conflict severity and frequency could inform intervention strategies for therapists working with distressed couples. The discovery that self-regulation (a resource that can be strengthened through training and practice) can protect people from these negative effects would advance intervention and prevention, ultimately impacting public health, the economy, and child development.

This multi-method longitudinal study of 200 couples will investigate how observed and self-reported features of conflict are associated with fluctuations in objective and subjective measures of sleep quality over time. The study has three objectives. The first is to determine how partners behavioral, emotional, and physiological stress responses to a lab-based conflict are associated with their own and each others typical sleep quality. The second is to determine the direction of links between specific features of conflict and sleep over time, using cross-lagged analysis of dyadic daily diary and sleep assessments collected at home over 14 days. The third objective is to determine whether developmentally organized markers of self-regulation (attachment, heart rate variability, rumination, and post-conflict recovery behavior) moderate links between conflict and sleep over time. This study will contribute to science by advancing a theoretically-derived model of individual differences in interpersonal stress reactivity and regulation that affect behavior in two critically important social contexts. Additionally, this project will provide training for the next generation of STEM scientists by engaging diverse undergraduate women in mentored research and discovery.

Agency: NSF | Branch: Standard Grant | Program: | Phase: GEOGRAPHY AND SPATIAL SCIENCES | Award Amount: 240.00K | Year: 2016

This project uses an innovative methodology called collaborative event ethnography to advance understanding of contemporary global environmental governance. Focusing on a case study of the 2016 World Conservation Congress, the project examines how notions of the green economy, such as those inscribed in the UNEP-led Green Economy Initiative, are shaping global environmental conservation practices. As part of a coordinated effort among researchers from multiple universities, countries, and disciplines, the project strengthens multi-national research, training, and teaching partnerships in producing policy-relevant scientific research. It integrates research and education through cascade mentoring, which includes senior and junior scholars and graduate and undergraduate students, from two liberal arts colleges and a large research university in the development of innovative methodology and theory-building in global environmental governance. Through rigorous theoretical and methodological training for graduate and undergraduate students, the project helps them to develop the skills and knowledge to apply geographic concepts and methods to pressing global environmental issues, while simultaneously exposing them to potential careers in environmental research and policy. By illuminating the complexities of how actors influence global environmental governance, it reveals avenues for redressing seemingly intractable environmental crises and global inequality at a critical historical moment.

The project challenges state-centric understandings of global environmental governance and advances new theories about how contemporary governance occurs. It augments traditional field-based studies of conservation with ethnographic data in order to trace how paradigm shifts occur in global conservation politics. Specifically, the project analyzes how global conservation governance transpires across multiple institutional sites by focusing on international conferences as key venues in which diverse actors, who are normally dispersed in time and space, convene to negotiate. It attends to the types of strategies that diverse actors use and the ways in which conference norms, structures, forms of acceptable knowledge and measurement shape the ways in which these actors interact and influence negotiations, revealing how and why certain actors are better able than others to catalyze paradigm shifts. Ultimately, it documents how paradigm shifts occur not only through official discourse, policy and law, but also through more informal, everyday interactions among public, private and non-profit actors.

Agency: NSF | Branch: Continuing grant | Program: | Phase: INFO INTEGRATION & INFORMATICS | Award Amount: 197.18K | Year: 2016

Many problems in science today require the analysis of massive datasets.
This project investigates the fundamental problem of extracting latent hidden regularities from high-dimensional scientific data sets, specifically from two different types of spectroscopic measurements -- three-dimensional hyperspectral imaging used in remote sensing of the Earth and other planets, and one-dimensional spectral signals arising from chemical analyses from laser-induced breakdown spectroscopy (LIBS), such as used currently by the Curiosity rover on Mars. The project is applying recent advances in deep learning, optimization, and machine learning to practical real-world scientific applications involving the analysis of materials from Earth and outer space, such as Mars, as well as the mapping of Martian and terrestrial surfaces through hyperspectral imagery.

Deep learning uses multi-layer neural networks to construct a hierarchy of latent representations of high-dimensional datasets. This project designs novel architectures and algorithms for deep learning, and applies them to spectroscopic domains, such as LIBS and hyperspectral imaging. Three challenges from spectroscopic domains guide the research. First, in many applications such as the Curiosity rover on Mars, the number of available LIBS spectra are limited as it requires an active sensing operation followed by transmission of data by a robot situated millions of miles from Earth. A further challenge is that data from Mars is inherently unlabeled, and instrumental variations and terrain variations between Earth and Mars require solving a key transfer learning problem. For hyperspectral imaging, the project is extending work on deep learning applied to two-dimensional images to data that involves two spatial dimensions as well as the third spectral dimension, where images are recorded at multiple wavelengths. This project explores a variety of ways of designing new convolutional neural networks and other approaches that can effectively exploit the third spectral dimension.

Agency: NSF | Branch: Standard Grant | Program: | Phase: POLYMERS | Award Amount: 192.57K | Year: 2014


Poly(vinyl alcohol) (PVOH), a nontoxic and water-soluble polymer, spontaneously attaches itself to silicone substrates from water solution and forms fractal (tree-branch like) structures from nanoscopic to macroscopic scales. The objectives of the proposed work are to explore the formation mechanism of PVOH fractals on silicone substrates and to probe the material and experimental factors controlling the structural features of PVOH fractals. The polymer fractal formation dynamics will be imaged during their actual generation using optical microscopy; the fractal structures from the nanometer to micrometer scales will be characterized by various microscopy techniques. Wettability and other surface characteristics of the silicone substrates, before and after PVOH adsorption, will also be analyzed to assess the effect of the fractal polymers on substrate properties. The funds requested will support the proposed scientific research involving a number of undergraduate students at Mount Holyoke College and local female high school students over the next three years. With the proper training, mentoring, and encouragement, the students will gain competency and confidence in scientific research and will be better prepared for the next phase of their educational endeavors.


Poly(vinyl alcohol) (PVOH) spontaneously adsorbs from aqueous solution to silicone substrates and forms unusual fractal structures at multiple length scales. The crystalline nature of PVOH and the unique properties of silicone thin films (molecular flexibility, low surface tension but high water compatibility) will be probed in exploring the conditions for fractal formation. The dependence of fractal features (size, density, and dimensionality) on experimental variants (silicone thickness and composition, PVOH molecular weight and degree of hydrolysis, and adsorption parameters) will also be the focus of the proposed work. Atomic force and optical microscopy will be used to image dynamically PVOH fractal formation on various silicone substrates in-situ and to analyze the fractal features from nanoscopic to macroscopic scales. Contact angle goniometry and ellipsometry, in addition to various other surface techniques, will be utilized to characterize the silicone substrates, before and after PVOH adsorption, to assess the effect of the fractal adsorbed polymers on substrate surface chemistry and properties. The proposed research will not only establish a new system to prepare fractally branched polymers with controlled structural features, but will also provide insights on material and experimental requirements for fractal polymer formation. The funds requested will support research involving a number of undergraduate students at Mount Holyoke College and local female high school students over the next three years.

Agency: NSF | Branch: Standard Grant | Program: | Phase: CAREER: FACULTY EARLY CAR DEV | Award Amount: 411.53K | Year: 2013

This proposal connects foundational research for multi-robot formations with the development of empowering experiences for women undergraduates in the classroom and beyond. The theoretical nature of the research is complemented by a firm grounding in hardware and computer vision fundamentals, integrated throughout a comprehensive education plan. The PI will develop an understanding of geometric formations of multi-robot systems, such as swarms in both 2- and 3-dimensions. Sensor and communication costs will be integral to modeling and algorithmic considerations, as minimizing power consumption is increasingly important for the design of lightweight and agile robot platforms. The PI will establish mathematical foundations and develop algorithms in three fundamental directions: (1) understanding a formations structural properties, (2) producing optimal control architectures, and (3) predicting a formation?s internal motions. Developing a unifying theory from both theoretical and applied perspectives will produce a wealth of new directions, such as actuating a formation as if it were a single traditional robot.

The research contributions have the potential to significantly impact cutting-edge technology for the control and coordination of multi-robot systems. The PI is junior faculty at Mount Holyoke College, a liberal arts college for women, where a recent growth in enrollments has led to an average of 15 computer science majors a year (surpassing the peak of 2002). She will engage this vibrant community of budding computer scientists through her proposed education plan. Two courses will be developed, designed to simultaneously educate undergraduates through core computer science principles and expose them to exciting research problems challenging the field. Students will have additional opportunities for experiences outside the classroom through highly visible robotics and computer vision projects on campus, producing role models for generations to come. By working closely with the student-run CS Club, the PI will establish a supportive environment that fosters growing interest in technology from traditionally under-represented groups. She will also actively involve students in research by supervising two undergraduates each summer through a compelling research experience.

Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 141.13K | Year: 2013

With support from the Chemical Measurements and Imaging program, Professors Melinda Dyar of Mt. Holyoke College and Sridhar Mahadevan of University of Massachusetts at Amherst and their students will use laser-induced breakdown spectroscopy (LIBS) measurements, including laboratory investigations of standard materials at varying experimental conditions, to develop numerical methods that will address limitations to the broad application of LIBS imposed by matrix effects and plasma variability. State-of-the-art dimensionality reduction and transfer learning methods from machine learning and statistics will be used to build innovative LIBS-based predictive models. These investigations will extend classical methods in statistics for dealing with multiple paired data sets, such as canonical correlational analysis, to deal with unlabeled data, and extract nonlinear low-dimensional regularities in the data. The project includes the design of a suite of model-building tools that can deal with a range of problems and optimization objectives, including different types of correspondence information available across datasets, diversity of global objectives ranging from preserving local to global geometry, and producing linear or nonlinear mappings to lower-dimensional factors.

Laser-induced breakdown spectroscopy (LIBS) is a chemical analysis tool that uses the light emitted by a sample when a focused laser pulse generates a plasma at the sample surface. LIBS has a number of features that make it particularly useful for field use, including rapid analysis, minimal sample preparation and suitability for stand-off, that is remote, detection. Moreover, LIBS can detect and quantify light elements that are not always measured using other methods. Consequently, LIBS is well-suited to many applications including, defense interests (e.g., military explosive detection, illegal drug detection, airport security), in-situ analysis of archeological sites, field work at hazardous waste sites, and geological resource exploration. However, utilization of LIBS measurements is limited by signal variability with measurement and sample conditions. This project launches an integrated research program to couple state of the art LIBS instrumentation at Mount Holyoke College to equally state of the art numerical methodology in artificial intelligence and machine learning at the nearby University of Massachusetts to increase the utility of LIBS measurements. This project will provide an interdisciplinary training environment that includes undergraduate, graduate and post-doctoral researchers.

Agency: NSF | Branch: Standard Grant | Program: | Phase: ITEST | Award Amount: 71.17K | Year: 2016

This National Robotics Initiative project will develop a robotic learning environment for middle school geometry students where students who are novices in geometry will learn new concepts by tutoring a humanoid robot to manipulate its gestures and spoken prompts in response to student utterances and problem-solving actions. The project is based on the principle that the act of tutoring can lead to motivational benefits such as student engagement, positive attitudes toward the subject being studied, and increased confidence. In this application, the robot is simultaneously a tool that students can program and a social actor that intelligently responds. This research project will engage a broadly diverse population and is aimed at increasing the participation and retention of underrepresented groups in fields of science, technology, engineering, and mathematics (STEM). There are three components to the broader impacts of this project: 1) Scientific understanding of how robotic learning companions affect STEM attitudes and confidence, 2) Learning among students from traditionally underrepresented groups in the research process, and 3) Creation of a human-robot interaction platform for education and experimentation. The principles discovered through this project are expected to promote increased participation of women and other underrepresented populations in STEM educational activities and STEM-related careers.

The goals of this research are to link robot behaviors to mediating motivational factors and STEM outcomes with the ultimate goal of understanding how to manipulate robot behaviors to improve a learning interaction. The proposed research will make contributions to understanding of how robotic learning environments can be designed to have a transformative impact on STEM learning. The research is guided by two questions: 1) How do robot behaviors influence students mediating motivational factors and affect STEM skills and attitudes? And 2) What is the impact of adapting robot behaviors to student behaviors on mediating variables and STEM skills and outcomes? To find answers to these questions, the project will undertake three initiatives: 1) Further development of NaoTAG (Nao Tangible Activities for Geometry) to serve as a platform for experimenting with how different aspects of robot behaviors influence student-robot interactions within the teachable agent context; 2) Understand how student and robot behaviors influence the mediating factors that have identified, and STEM skills and outcomes; and 3) Modeling student-robot interactions.

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