New Mexico State University , is a major public, land-grant, research university in Las Cruces, New Mexico, United States. Founded in 1888, it is the oldest public institution of higher education in the state of New Mexico. NMSU is the second largest four-year university in the state, in terms of total enrollment across all campuses as of 2011, with campuses in Alamogordo, Carlsbad, Doña Ana County, and Grants, with extension and research centers across New Mexico.It was founded to teach agriculture in 1888 as the Las Cruces College, and the following year became New Mexico College of Agriculture and Mechanic Arts. It received its present name in 1960. NMSU has 18,497 students enrolled as of Fall 2009, and has a faculty-to-student ratio of about 1 to 19. NMSU offers a wide range of programs and awards associate, bachelor's, master's, and doctoral degrees through its main campus and four community colleges. NMSU is the only research-extensive, land-grant, USA-Mexico border institution classified by the federal government as serving Hispanics. Wikipedia.
Herndon J.W.,New Mexico State University
Coordination Chemistry Reviews | Year: 2014
This is a review of papers published in the year 2012 that focus on the synthesis, reactivity, or properties of compounds containing a carbon-transition metal double or triple bond. © 2014 Elsevier B.V.
Herndon J.W.,New Mexico State University
Coordination Chemistry Reviews | Year: 2012
This is a review of papers published in the year 2010 that focus on the synthesis, reactivity, or properties of compounds containing a carbon-transition metal double or triple bond. © 2012 Elsevier B.V.
Agency: NSF | Branch: Standard Grant | Program: | Phase: MAJOR RESEARCH INSTRUMENTATION | Award Amount: 699.07K | Year: 2016
An award is made to New Mexico State University to acquire an Orbitrap Fusion mass spectrometer, to be housed in the Chemical Analysis and Instrumentation Laboratory. This instrumentation is the state-for-the art for chemical characterization of extremely complex mixtures and will be used in a wide variety of applications that range from alternative fuel research to disease research and fundamental biology. This instrument will be used to describe the chemical composition of complex mixtures to help improve the manner in which biomass is cultivated and processed into fuel and will be a key component to multiple health-related research projects including the imaging of live cells. Given the wide range of applications where the instrument will be used, this research will generate both novel applications and significant advancements in the application of this technology. Finally, the collaborative research projects that use the Orbitrap Fusion will provide a framework for discovery that will improve the ability of our institution to recruit and retain excellent faculty and will provide enormous opportunity for student participation in science and engineering.
The purpose of this acquisition is twofold. First, this acquisition will enable mass spectrometry-based proteomics for a number of collaborative research projects. Modern proteomic capability is currently absent in the state of New Mexico. The Orbitrap Fusion instrument architecture includes three orthogonal fragmentation techniques (electron transfer dissociation, collisional dissociation and higher energy collisional dissociation) and multiplex-in-time ion fragmentation capability, all of which are critical for modern quantitative proteomics and the analysis of protein post-translational modification. Second, with respect to multiple current research efforts in energy, environment and lipid applications, the Orbitrap Fusion offers excellent mass accuracy, ion transmission efficiency across a broad mass range and high resolving power for complex mixture analysis with collisional and higher energy collisional fragmentation methods to aid structural elucidation. Within those project areas, we will utilize the Orbitrap Fusion for the analysis of bio-oils and fuel precursors from biomass thermotreatment and upgrading, algal growth media and feedstocks, lipid mixtures from various biofuel feedstocks and complex environmental samples.
Agency: NSF | Branch: Standard Grant | Program: | Phase: Big Data Science &Engineering | Award Amount: 361.79K | Year: 2016
Successfully tackling many urgent challenges in socio-economically critical domains (such as sustainability, public health, and biology) requires obtaining a deeper understanding of complex relationships and interactions among a diverse spectrum of entities in different contexts. In complex systems, (a) it is critical to discover how one object influences others within specific contexts, rather than seeking an overall measure of impact, and (b) the context-aware understanding of impact has the potential to transform the way people explore, search, and make decisions in complex systems. This project establishes the foundations of big data driven Context-Sensitive Impact Discovery (CSID) in complex systems and fills an important hole in big data driven decision making in many critical application domains, including epidemic preparedness, biological pathway analysis, climate, and resilient water/energy infrastructures. Thus, it enables applications and services with significant economic and health impact. The educational impacts of this project include the mentoring of graduate and undergraduate students, and the enhancement of graduate and undergraduate Computer Science curricula at both Arizona State University (ASU) and New Mexico State University (NMSU) through the incorporation of research challenges and outcomes into existing classes.
The technical goal of this project is to establish the theoretical, algorithmic, and computational foundations of big data driven context-sensitive impact discovery in complex systems. This project develops probabilistic and tensor-based models to capture context-sensitive impact from complex systems, often modeled as graphs, and designs efficient learning algorithms that can capture both the contexts and the impact scores among entities within these different contexts. The modeling of the context sensitive impact considers dynamic nature of relevant contexts and the diverse applications. This requires addressing several major challenges, including latent contexts of impact, heterogeneous networks of entities, dynamicity of impact in varying contexts, and high computational and I/O costs of context-sensitive impact discovery. Therefore, this project designs novel scalable probabilistic and tensor-based algorithms to capture and represent context-sensitive impact. These algorithms and the novel data platforms they are deployed in are efficient and scalable in terms of off-line and on-line running times and their space requirements. To achieve necessary scalabilities, the developed platforms employ novel multi-resolution data partitioning and resource allocation strategies and the research enables massive parallelism and efficient data access through new non-volatile memory based data management techniques.
Herndon J.W.,New Mexico State University
Coordination Chemistry Reviews | Year: 2013
This is a review of papers published in the year 2011 that focus on the synthesis, reactivity, or properties of compounds containing a carbon-transition metal double or triple bond. © 2013 Elsevier B.V.
Agency: NSF | Branch: Standard Grant | Program: | Phase: Cyber-Human Systems (CHS) | Award Amount: 495.63K | Year: 2016
Unmanned robotic systems are set to revolutionize a number of vital human activities, including disaster response, public safety, citizen science, and agriculture, yet such systems are complex and require multiple pilots. As algorithms take over, and controls are simplified, workers benefit from directing, rather than controlling, these systems. Such simplifications could enable workers to use their hands and focus their perception in the physical world, relying on wearable interfaces (e.g., chording keyboards, gesture inputs) to manage teams of unmanned vehicles. Adaptive autonomy, in which unmanned systems alter their need for human attention in response to complexities in the environment, offers a solution in which workers can use minimal input to enact change. The present research combines wearable interfaces with adaptive autonomy to direct teams of software agents, which simulate unmanned robotic systems. The outcomes will support next-generation unmanned robotic system interfaces.
The objective of this project is to develop wearable interfaces for the direction of a team of software agents that make use of adaptive autonomy and ascertain the effectiveness of interface designs to direct agents. This research develops a testbed for wearable cyber-human system designs that uses software agents as unmanned robotic system simulations and uses adaptive-autonomy algorithms to drive the agents. The research develops a framework connecting wearable interface modalities to the activities they best support. Developed systems will be validated through mixed reality environments in which participants will direct software agents while acting in the physical world. The principal hypothesis is that a set of interconnected interfaces can be developed that, through appropriate control algorithms, maximizes an operators control span over a team of agents and optimizes the operators physical workload, mental workload, and situation awareness.
Agency: NSF | Branch: Continuing grant | Program: | Phase: Cyber-Human Systems (CHS) | Award Amount: 132.49K | Year: 2017
This projects goal is to support disaster response teams -- particularly urban search and rescue -- by designing wearable technologies that meet their information needs. Wearables for disaster response are promising because responders hands are often busy and their awareness must be on their surroundings rather than a screen, making it hard to use common computing devices like smartphones, tablets, or computers. A number of prototype wearable technologies exist that might help: head-up displays can show data alongside the physical world; audio headsets can enable communication with both teammates and computers; armbands can provide touch-based feedback and allow gestures for communication and control; sensors can detect location, motion, and aspects of peoples physical and mental state. However, usable wearable systems and design guidance for building them is scarce in general and even scarcer in the context of disaster response. The key insight behind the proposed work is that interfaces for training simulations and computer games make heavy use of head-up displays and specialized controllers; further, these often share elements of real disaster response scenarios. This project will study the features and effectiveness of these interfaces to generate design guidelines for wearable computing systems. Working closely with the Texas Task Force One response team, the project team will create custom-built wearable systems to support their mission as well as purpose-built mixed reality training simulations that combine virtual simulation with physical-world settings. The team will validate those simulations with the disaster response partners and use them to test and improve both the wearable technologies and the design guidelines the team creates. Both the work itself and the lessons learned will be used to improve classes at the lead investigators institution around designing mixed reality technologies and human-computer interaction (HCI); the lead, and his institution, are also committed to broadening participation in computing education and computing research.
The work will start with a deep study of training practices and needs for disaster response teams. The team will closely collaborate with its existing task force partners, using ethnographic observation of their existing training practices and interviews with task force leaders to develop models of task force training requirements and design considerations for wearable systems to support them. These will be disseminated both to disaster response teams and the HCI community. In parallel, the team will extract design best practices from existing training simulations and computer games, focusing on those that align with disaster response scenarios and needs. Team members will analyze the interaction techniques and mechanics these systems use -- how they provide situation awareness through ancillary displays, interface controls for managing large numbers of units and functions, and communication facilities for multiple participants -- along with peoples preferences for and ability to use them. This will lead to a design catalog of existing interfaces and best practices for designing wearable and mixed reality interfaces that, along with the requirements identified through studying the task force, will inform the design of both wearable interfaces and testbed training simulations for disaster response contexts. These design activities will involve regular communication with task force partners to get feedback on using the interfaces in real situations, helping to align both the technology and simulation designs with real disaster response needs. Finally, the team will evaluate both the interfaces and the developed testbeds through a number of small-scale deployments and experiments, looking at how they support situation awareness, efficiency, and communication, as well as their effect on stress and workload. Much of the work will be used to support both courses and outreach activities; these include developing cases and projects based on the work for an HCI class as well as leveraging the designed technologies to improve a course on mixed reality. These will also be used to support a mixed reality interface development outreach activity with the local community college to expose students from underrepresented groups to both research and to work with advanced computing technology.
Agency: NSF | Branch: Standard Grant | Program: | Phase: ROBUST INTELLIGENCE | Award Amount: 500.00K | Year: 2016
There is a growing need for optimization methods to support decentralized decision-making in complex multi-agent systems including target tracking in sensor networks, mission planning of unmanned autonomous vehicles, coordination of rescue robots in disaster scenarios, and scheduling of intelligent devices in smart homes within smart grids. This class of problems is particularly challenging to solve due to a combination of the following requirements: There is a high degree of uncertainty that must be taken into account during planning; the planning process must be done in a decentralized fashion; and the resulting plan must be executed in a decentralized way as well. The objective of this project is to respond to the crucial challenge of developing an integrated approach that captures all these requirements within a single framework in order to improve the scope and applicability of multi-agent techniques in real-world applications. The long-term broader impacts of this project include the potential for the research findings to improve decentralized decision-making in real-world problems. In the short term, high-school students will benefit from the education modules developed by the PI, which will be disseminated through collaborations with local outreach programs as well as local teachers and summer camp organizers. The students will develop better computational thinking skills and be exposed to computational concepts applied to relevant applications of interest. The significance of these efforts is made more crucial by the fact that a majority of the student body at local high-schools as well as at NMSU is Hispanic.
This project will make the necessary foundational contributions to the field of multi-agent systems to improve the scope and applicability of such systems, especially those that utilize automated planning and constraint optimization techniques, in the real world. More specifically, this project will result in (i) novel ways to more accurately model a large class of multi-agent planning problems using decentralized constraint-based models; (ii) new scalable algorithms with theoretical guarantees suitable for solving large-scale decentralized planning problems; and (iii) effective ways of improving computational thinking in high-school students via the use of constraint-based representations.
Agency: NSF | Branch: Standard Grant | Program: | Phase: S-STEM:SCHLR SCI TECH ENG&MATH | Award Amount: 1.00M | Year: 2016
New Mexico State Universitys (NMSU) S-STEM project entitled Increasing the Success of Low-Income, Academically Talented Students in Engineering is a synergistic effort between the NMSU College of Engineering and NMSU student support programs and services. The project will support a cohort of twenty (20) academically talented engineering students who demonstrate financial need, with scholarship funding and Cohort Academic and Research Experience (CARE), which includes individualized self-assessment and monitoring, academic success workshops, undergraduate research experiences and internships, one-on-one relationships with faculty mentors, and training to increase self-efficacy, metacognitive self-awareness and self-study skills. The scholars will form a natural cohort sharing common challenges associated with low-income status, as well as common experiences in engineering. Historical NMSU data suggests that many will also share experiences as first-generation college students and historically underrepresented minorities, thereby broadening participation for students from those groups. Through industry partnerships, the project will enhance professional development and engineering workforce opportunities for NMSU?s engineering students.
The objectives of the project are to: (1) provide financial assistance to academically talented students demonstrating financial need, (2) provide students with academic support, professional development, and research experience opportunities, (3) strengthen relationships and synergistic efforts with existing NMSU programs and services, and (4) increase retention of S-STEM scholars to degree completion and graduation. While implementing activities to accomplish these objectives, the project team will investigate the hypothesis that students increased awareness of metacognition-based strategies motivates them to alter their study practices and the impact of students study practices on learning and retention while iteratively developing valid and reliable instruments for use in this and future studies about study skills and metacognition-based practices as related to student success in STEM. Findings on the relationships among student study habits, learning performance, retention, metacognition, and self-efficacy for engineering will be of value to the STEM education field in general by providing faculty increased understanding of the value of metacognitive awareness among their students.
Agency: NSF | Branch: Continuing grant | Program: | Phase: | Award Amount: 492.03K | Year: 2016
As our closest star, the Sun provides an important laboratory to study the processes that govern stars in our Galaxy. The Sun is also the primary driver for the space weather that impacts human life on Earth. Since 1976, the National Solar Observatory (NSO) has operated the Sacramento Peak Observatory located in Sunspot, New Mexico for the NSF as a premier solar research facility available to scientists in the U.S. and abroad. The primary research facility still in operation at the observatory is the Richard B. Dunn Solar Telescope (DST). The DST can study the Sun at high resolution in wavelengths ranging from the optical to the infrared. The NSF and the NSO are currently building the most powerful solar telescope in the world, the Daniel K. Inouye Solar Telescope (DKIST) atop Haleakala, Maui, Hawaii. While DKIST will render the DST redundant as a national user facility, the DST retains substantial value both as a scientific tool and as a training ground for the next generation of solar researchers. This award supports science and operations of the DST as it transitions from the NSO to a potential university-based consortium led by New Mexico State University (NMSU). During this transition, the proposers will begin an innovative science program of long-term, synoptic observations of solar magnetic fields while preparing the facility for consortium operations.
This award supports science and operations of the DST for a period of 24 months bridging the gap between the departure of the NSO at the end of 2017 and the development of the NMSU-led Sunspot Solar Observatory Consortium (SSOC) as the primary operator beyond 2018. The development of the SSOC will depend on maintaining the telescope and its instrument suite, transferring critical knowledge, and reducing areas of identified risks. As the SSOC develops, its members will be provided with dedicated DST time in accordance with the member?s level of participation. During the bridge period covered by this proposal, the PIs will conduct a program of synoptic science concentrating on four key areas of interest: (1) solar filament magnetic field structure, (2) solar flare energy budgets, (3) quiet Sun magnetic canopy, and (4) solar prominence instabilities. In order to deliver the proposed synoptic science, specific knowledge of the facility will be transferred during the transition period overlapping the final year of NSO at Sunspot and the first year of SSOC operations. The proposers highlight three important objectives to be accomplished during the transition period: (1) Complete a critical synoptic science plan for the DST; (2) Hire consortium personnel for telescope and site knowledge transfer; and (3) Update and upgrade the telescope. In accomplishing these objectives through this award, the PIs aim to achieve the long-term goal of reinventing and reinvigorating the DST and the Sunspot facility.