Sample Grants

Sample of Abstracts of Current NU Grants and Broader Impact Components


Below are some  recently funded projects and the description and broader impact efforts. All detailed abstracts can be seen at NSF’s website which is linked below for each project.


Award Abstract #1749530 

CAREER: Developing a Spatial-Temporal Predictive Framework for the Drinking Water Microbiome.

Ameet Pinto  (Principal Investigator)

Every gallon of drinking water contains millions of microbes that are referred to as the drinking water microbiome. Water utilities perform extensive monitoring in the water distribution system to ensure that the drinking water microbiome remains safe as it travels from the water treatment plant to the household tap. Although these monitoring practices are designed for early detection of microbial contamination, any attempt to fix a problem identified by monitoring will be inherently reactionary. This project seeks to revolutionize the current United States drinking water monitoring system of “detect and mitigate” towards a proactive one of “predict and correct”. This project aims to develop a computer model that will allow water utilities to predict future microbial contamination events across the water distribution system, thus providing an opportunity to prevent contamination events before they occur. Integrated with the research activities will be an education program that includes training of K-12, undergraduate, and graduate students in state-of-the-art microbiome characterization and interpretation to attract them to STEM careers. The goal of this education program will be to prepare future water scientists and engineers with expertise in microbiome research and practice.

The career development plan will advance the safety and health of drinking water by integrating experimental and computational microbial ecology into drinking water microbiome research and by leveraging this research through education and outreach to engage a diverse student body in microbiome research and practice. The research goals of this project are (1) to establish a long-term observatory to monitor high-resolution drinking water microbiome dynamics in Boston’s water distribution system, (2) to develop a novel framework to apply econometric and ecological models to these dynamics, and (3) to systematically integrate the two modeling approaches for comprehensive spatial-temporal forecasting of the abundance and composition of the drinking water microbiome. The research plan combines state-of-the-art integrated analyses and novel modeling approaches to develop a quantitative predictive framework for the drinking water microbiome. This predictive framework will (1) help water utilities preemptively estimate and eliminate future drinking water microbial risks, and (2) serve as foundational knowledge for model-informed quantitative management of the drinking water microbiome. This research is integrated with an education and outreach plan designed to (1) attract and train undergraduate and graduate students in microbiome research and practice, using pedagogical approaches designed to enhance participation of students from underrepresented minority groups in STEM research, and (2) raise public awareness of the drinking-water microbiome through K-12 initiatives. The objectives will be accomplished by developing innovative microbiome-focused curricula in environmental engineering using a Course-based Undergraduate Research Experiences (CUREs) approach in collaboration with Wentworth Institute of Technology and Wellesley College and a K-12 learning activity for Boston area students through the Center for STEM Education at Northeastern University. The project will advance the shared aspirational vision of the drinking water research community and the microbiome-centric drinking water quality management community.

NSF Link: Click Here


Award Abstract #1750539 

CAREER: Leveraging Sparsity in Massively Distributed Optimization

Stratis Ioannidis IOANNIDIS@ECE.NEU.EDU  (Principal Investigator)

This project develops novel parallel optimization techniques based on the Frank-Wolfe algorithm, enablinmailto:IOANNIDIS@ECE.NEU.EDUg the massive parallelization, at an unprecedented scale, of several problems of key significance to computer science, engineering, and operations research. Massively parallelizing such problems can have a significant practical impact on both academia and industry. Using Apache Spark as a development platform, algorithms developed by the project can be implemented, deployed and evaluated over hundreds of machines and thousands of CPUs. The Massachusetts Green High Performance Computing Center (MGHPCC) as well as cloud services, such as Amazon Web Services and the Google Cloud Platform, are leveraged for this deployment, demonstrating both the scalability of developed algorithms as well as their applicability to commercial cluster environments. Educational activities are closely integrated with this research agenda, including a course developed by the principal investigator using MGHPCC as a computing platform, and outreach activities developed jointly with Northeastern University’s Center for STEM Education.

This research advances our knowledge and understanding of the formal conditions under which problems can be massively parallelized via map-reduce implementations of the Frank-Wolfe algorithm. The project leverages sparsity properties that optimization problems exhibit under Frank-Wolfe, thereby enabling their parallelization via map-reduce operations. Beyond tailored, problem-specific implementations, the project identifies formal, structural properties of problems (or, classes of problems) under which such massive parallelization via map-reduce is possible. The use of Frank-Wolfe as a building block for parallelization, both in convex optimization but also in submodular maximization settings, is transformative. In the latter case, it amounts to a non-combinatorial approach for parallelization, attaining the same approximation guarantee as serial algorithms.

NSF Link: Click Here


Award Abstract #1653671 

CAREER: 4D mm-Wave Compressive Sensing and Imaging at One Thousand Volumetric Frames per Second

Jose Martinez-Lorenzo  (Principal Investigator)

Millimeter-wave sensing and imaging systems are used ubiquitously for a wide range of applications, such as atmospheric sounding of the earth to forecast the weather, security monitoring to detect potential threats at airport checkpoints, and biological imaging of superficial tissues for wound diagnosis and healing. These systems typically operate well when the scene dynamics do not change rapidly. Unfortunately this is not the case in emerging societally-important applications like swarms of drones in rescue missions, smart self-driving cars on roadways, or cyber-physical systems searching for suicide bombers when they are on the move. This project will benefit our society with the development of the first four-dimensional (4D) millimeter-wave imaging system operating in fast changing scenarios, in which safety-critical decisions must be made quickly. One of the new applications of this system will be finding security threats, concealed under clothing or inside backpacks, in open areas like shopping malls, sport venues, and office buildings. Specifically, the system will have the capability to scan multiple people moving within a volumetric region of 26 cubic meters, producing 1000 image frames per second in three dimensions, thus outperforming existing millimeter-wave sensing and imaging systems that are currently used at airport checkpoints. In addition to the societal impact, the Principal Investigator (PI) will build a strong educational program through which diverse audiences can understand the principles and limitations of wave-based imaging systems. The integration of research and education will be accomplished through the development of new curricula and research training methods for students, as well as through the elaboration of a roadmap for transitioning students into industry, in collaboration with Northeastern University (NEU) Cooperative Education Program. The outreach plan includes enabling research experiences for K-12, undergraduate, and underrepresented students in collaboration with the Science, Technology, Engineering, and Mathematics (STEM) centers at NEU, as well as education through online materials and public venues.

The research goal of this CAREER program is to understand the theoretical principles and fundamental limitations of adaptable compressive sensing and imaging systems using 4D (temporal and spatial) coding and to develop and experimentally validate these principles through a novel 4D millimeter-wave adaptive compressive imaging radar system. This system will produce 4D volumetric frame rates beyond 1000 frames per second, each frame having over one million pixels. The primary challenges of implementing 4D adaptable imaging systems are the following: 1) the system must be capable of handling variable dynamics, i.e., objects moving with different velocities and located at different focal ranges; 2) the system must sample data with sufficient signal to noise ratio during the limited period of time; and 3) the system must sample data extremely fast to perform fast 4D video reconstruction with high volumetric frame rates. This project will address these challenges as follows: (i) it will develop a new theory that brings together functional analysis, information theory, compressive sensing, and adaptable metamaterials to enhance the information transfer efficiency of sensing systems; (ii) it will develop a new mathematical framework to optimize 4D codes based on the desired information rate and energy efficiency of the compressive imaging system; and (iii) the system will utilize spatial light modulators, vortex-meta-lenses, and compressive reflectors to perform the coding and to dynamically adapt to the state of the imaging region. The result of this research will establish the scientific basis for the proposed new sensing and imaging systems, by enhancing the imaging performance, reliability, and efficiency while reducing the hardware complexity, overall cost, and energy consumption of the system.

NSF Link: Click Here


Award Abstract #1451213 

CAREER: Low-Power Transceiver Design Methods for Wireless Medical Monitoring

Marvin Onabajo  (Principal Investigator)

Wireless communication chips with lower power consumption are needed to enable more widespread wireless connectivity for numerous battery-powered portable and implantable biosignal measurement devices. However, reduced power consumptions lead to degraded performance and reliability, which inhibits the adoption of low-power circuit design approaches. Innovative integrated circuit design techniques are required to alleviate this tradeoff in medical applications, wireless sensor networks and chips with energy harvesting features. The primary research objective of this project is to create design methodologies for performance and reliability enhancements of tunable low-power analog circuits through the incorporation of efficient digital circuits. A key educational goal is to pioneer a unified approach through which students collaboratively learn to combine low-power analog integrated circuit design and digitally assisted performance tuning methods with a primary focus on cutting-edge medical applications. New course materials will establish a long-lasting research and education program aimed at creating reliable wireless capabilities for various miniaturized devices. Undergraduates and high school interns will be directly involved in research tasks. The project team will collaborate with Northeastern University’s Center for STEM Education to organize on-campus activities with K-12 students and teachers as well as outreach visits to connect with underrepresented groups in local schools.

State-of-the-art low-power receivers are prone to interference due to their limited dynamic ranges, which is particularly severe when multiple wireless medical monitoring devices coexist in close proximity to each other. To overcome this challenge, an adaptive design methodology will be devised to enhance interference suppression through extra filtering in the receiver path. This research effort will address the performance deficiencies of low-power integrated circuits such that a broader range of devices can be equipped with short-range wireless connectivity. It will provide new knowledge to design transceivers with better immunity to interference through the introduction of adaptive filtering in RF front-ends, digitally assisted linearity improvements for low-power analog circuits, and digital spectrum analysis for self-calibrations. Novel circuit-level linearization methods will be demonstrated to enable the design of analog circuits that include transistors operating in the subthreshold region with substantially improved dynamic ranges. These methods will be leveraged to achieve leading-edge performance with less than one-sixth of the power compared to current transceivers. The research will produce techniques to evaluate gain and linearity characteristics of analog circuits using an efficient fast Fourier transform engine that calculates the frequency spectrum of signals with significantly less chip area than existing methods. This will be a foundation for new built-in test and calibration strategies that counteract rising process variations of advanced chip manufacturing technologies.



Award Abstract #1350114

CAREER: Nano Electro Mechanical Resonant Sensing Platform for Chip Scale, High Resolution and Ultra-Fast Terahertz Spectroscopy and Imaging

Matteo Rinaldi  (Principal Investigator)

Broader Impact: This long-range integrated research and educational program will lead to a transformative NEMS THz detector technology that will enable the use of THz technologies in a wide variety of applications that could significantly impact the quality of life in different aspects, such as health, security and sustainability. Simultaneously, the proposed program will educate a cadre of experts needed for a full exploitation of these new THz technologies. The following goals will be targeted through outreach, education and technology transfer: (1) Bringing awareness to K-12 and undergraduate students, in collaboration with The Center for STEM Education at Northeastern University, with the integration of “NanoTech Units” into existing summer programs in which students will interact closely with the Principle Investigator (PI) and his graduate research assistants. (2) Hands-on demonstrations at large public venues. (3) Results dissemination through strategic use of online resources. (4) Education and training of graduate and undergraduate students in collaboration with industrial partners on the “Northeastern University Cooperative Education” basis in order to help the students developing the knowledge, awareness, perspective, and confidence necessary to bridge academic research and market needs. (5) Graduate and undergraduate courses development, including topics relevant to the proposed research.

NSF Link: Click Here


Award Abstract #1454414 

CAREER: Building Chemical Synthesis Networks for Life Cycle Hazard Modeling

Matthew Eckelman (Principal Investigator)

The proposed research will create a science-based, spatial, and dynamic modeling platform to enable next-generation sustainability assessment of chemicals. High-quality inventory data for hundreds of chemicals and validated estimation tools for thousands more will constitute an open toolkit for the global modeling community. Mechanistic process models will offer unprecedented accuracy in modeling chemical unit processes for LCA while still maintaining a conserved, network structure of energy and material flows upstream to resource extraction. New algorithms and metrics will integrate the inherent hazard approach of green chemistry with the systems approach of LCA. Research activities will leverage existing computational and modeling facilities at Northeastern and Sandia National Laboratory. The research tasks are anticipated to advance modeling and assessment capabilities in evaluating chemical technologies for public and private decision-making. Data, models, and results will be disseminated widely and structured to enable interoperability with existing modeling platforms. The integrated research and education plan will directly engage local students, teachers, and the public, potentially affecting thousands of students and citizens. The project will broaden participation in science and engineering by recruiting and mentoring student researchers from under-represented groups for high school (Young Scholars, Step-Up Programs), college (REU), and science teachers (RET). Design, delivery, and assessment of education and outreach activities will leverage existing capabilities and expertise from Northeastern’s highly successful Center for STEM Education, the Graduate School of Engineering, and the Center for Teaching and Learning through Research and will build on the PI’s experience in K-12 science instruction, teacher training, and online education.

NSF Link: Click Here

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