New Jersey Institute Of Technology

NSF Awards · FY 2025 · 2025-08

This collaborative project explores how a tiny droplet, powered by internal and surface activity, can propel itself—serving as a simplified model for how primitive cells, or "protocells", move. In experiments, such systems can be created by building networks of actin proteins inside and along the membrane of giant vesicles. To understand how this motion arises, the research team develops mathematical models that describe how forces inside the droplet and on its surface interact with the surrounding fluid. A key focus is to understand how this active droplet pushes against its environment to generate sustained forward motion—behavior that is fundamental to many forms of movement in soft materials and living cells. The project supports graduate education at Florida State University and New Jersey Institute of Technology, and promotes collaboration and dissemination of scientific knowledge through scientific workshops and seminars. The project aims to elucidate the role of steric alignment interactions in the nematic fluid on drop propulsion. The project combines analytical theory, numerical simulations, and comparisons with experimental data from active vesicle systems. The primary investigator Young leads the development of mathematical models and analytical methods, including theory of partial differential equations (PDE), dynamical systems analysis, differential geometry, and asymptotic techniques. The primary investigator Quaife develops efficient numerical algorithms for solving coupled surface-bulk PDEs on both rigid and deforming geometries. These numerical methods include solvers for surface PDEs on evolving interfaces and bulk-surface coupling across moving boundaries. A central challenge is modeling steric alignment interactions at the continuum level and calibrating their strength using experimental observations. The resulting framework has broad applicability to active matter systems described by coupled surface-bulk dynamics on moving domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

As a widely recognized, reliable, and sustainable energy source, offshore wind (OSW) has been rapidly deployed around the world. However, the OSW development in the U.S. is still in its infancy. The current electric energy infrastructure in the U.S., as well as the enabling technologies, are not well prepared for an efficient transmission and integration of large-scale OSW, which is preventing the U.S. from playing a critical role in leading this global technology transformation. To this end, New Jersey Institute of Technology (NJIT), in collaboration with Rowan University, will conduct planning activities towards establishing a Center for Grid Enhancing Technologies for Offshore Wind Transmission and Integration (GOWIND). GOWIND aims to establish a strategic research and education hub focused on driving technological innovation, creating partnerships, and advancing workforce training to enable the sustainable development of the nation’s OSW industry. With strong support from industry partners of different sectors and government agencies, this collaborative effort will address critical challenges in OSW transmission and integration, its grid enhancement technologies, and the shortage of domestic workforce, seeking intelligent, cost-effective, and industry needed solutions to enable a smooth transition towards an offshore economy and secure energy future. The mission of GOWIND is to drive innovative and scalable technology development, seek synergism in research, execute domestic workforce training, accelerate standards development, boost regional engagement, promote knowledge sharing, and ultimately support the creation of a resilient and sustainable energy future. GOWIND will address a wide spectrum of key unmet industry needs focused on OSW transmission and integration. The Center’s research thrusts encompass high-voltage direct current transmission and converter technologies, grid congestion management and interoperability, energy storage and Power-to-X, grid-forming inverters, coordinated control and operation, condition monitoring and predictive maintenance, and cyberphysical resilience. These areas represent significant economic and technical challenges in OSW integration into the aging infrastructure. The NJIT site offers multidisciplinary expertise in power systems, renewable energy integration, grid optimization, sensor technologies, and advanced computational techniques. With state-of-the-art facilities and strong support from the Sensor Research Lab, Green Technology Research and Training Lab, Intelligent Power Grid Lab, Power System Dynamics Lab, Control and Optimization Lab, Networked Controls and Intelligent Diagnostics Lab, and Renewable Energy Systems Training Lab, NJIT will drive the development of resilient, efficient, and scalable technologies to support the growing offshore wind industry, in collaboration with Rowan and industry partners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-08

Project Summary My lab develops artificial intelligence (AI), machine learning (ML) algorithms, and statistical methods to analyze various genomic data under different experimental designs. With multidisciplinary training in computer science, statistics, and biology, my research program focuses on developing the informatics of tomorrow in the context of pressing biomedical application problems today, in collaboration with my colleagues in the biomedical field. All methods developed in our lab are implemented into user-friendly, publicly available software packages to maximize their impact. In the past five years, we have focused on single-cell genomics, transcriptomics, and epigenomics. Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity and complex biological systems. Despite advances in computational methods, including our lab's contributions, the full potential of these datasets remains untapped due to the lack of powerful tools for integrating and analyzing vast amounts of single-cell omics data. Additionally, emerging biotechnologies like cellular barcoding, when coupled with single-cell sequencing, necessitate the development of new computational methods to fully realize their potential. Therefore, our goals for the next five years include: (1) developing and optimizing large- scale foundation models for single-cell omics data, (2) creating computational methods for barcoding single-cell omics data, and (3) quantifying cell-type annotation uncertainty in scRNA-seq studies. We will develop innovative AI/ML techniques to address computational challenges in single-cell large-scale foundation models (scLFMs), integrate biological domain knowledge, incorporate other modalities and cross-species data, and develop metrics to evaluate scLFM embedding quality. We will also create methods that leverage barcode information and biological knowledge for clustering, cell-cell communications, and cell trajectory inference, as well as statistical methods for detecting clones with longitudinal changes and identifying genes driving these changes. Lastly, we will use conformal prediction to quantify cell-type annotation uncertainty in scRNA-seq studies. We will develop a basic testing procedure to produce statistically valid prediction sets for each cell and a tree-based testing procedure that considers the hierarchical structures of cell types. The proposed research builds upon the PI’s lab's recent progress in developing deep learning methods for single-cell, epigenomic, and genetic data analysis, as well as statistical methods for transcriptomic data analysis. We emphasize the importance of implementing our proposed methods into user-friendly and open-source software tools to benefit the biomedical community. The overall vision of the research program is to advance the development of computational methods for single- cell omics data analysis, ultimately accelerating biological discovery and clinical applications.

NSF Awards · FY 2025 · 2025-07

The rapid growth of online video content has created new opportunities for learning, communication, and civic engagement. However, current accessibility technologies leave many people, including deaf and hard-of-hearing (DHH) individuals and older adults, with incomplete access to this important information resource. While existing automated audio captioning technology is frequently used to transcribe the audio in online video, the focus is on spoken words and ignores environmental sounds, music, and speaking style. These things often carry important information, from the subtle audio cues that signal danger in safety training videos to the environmental sounds that establish setting and mood in educational documentaries. This project will develop adaptive artificial intelligence systems that can determine which of these non-speech sounds are important for understanding video content and present them in ways tailored to individual viewer needs and preferences. The research tackles the complex challenge of translating rich hearing experiences into understandable formats while respecting the different ways that individuals prefer to receive information. By creating tools that make non-speech sounds accessible in digital media, this project ensures that all citizens can participate fully in digital education, entertainment, and civic life. This project consists of a comprehensive agenda that combines human-computer interaction, machine learning and accessibility research. First, through user research with content creators and viewers, the project will investigate: "what non-speech sounds should be captioned?", "why should they be captioned?", and "how should they be captioned?" Results will inform design guidelines for tools to write and display captions. Second, the project will develop captioning datasets, including a large dataset of videos annotated for the needs of viewers. These datasets will further our understanding of the complex relationships that influence what should be captioned and how. Third, the project will develop a steerable and adaptive machine learning framework using multiple types of data (from our datasets) for audio captioning. In this framework, sound events will be: densely captioned with cues for meaning and sound; prioritized and decoded into text and visuals to communicate their meaning; adapted to the needs and preferences of viewers. Viewer needs and preferences will be discovered using a co-design approach with stakeholders. This project will create publicly available tools, guidelines, datasets, and machine learning frameworks to improve learning, communication, and civic engagement for millions of people who are DHH or experience decline in hearing capabilities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Extreme space weather events can disrupt satellite communications, GPS systems, and power grids and even pose risks to astronauts. Understanding and predicting these events is essential for protecting critical infrastructure and ensuring national security. This project aims to develop an advanced cyberinfrastructure that integrates artificial intelligence (AI) with diverse space weather data to improve the forecasting of extreme space weather events. By incorporating generative AI models for data creation, the project enables predictive analyses that are both data-rich and scalable. This significantly enhances the ability to forecast extreme events, including solar flares, coronal mass ejections, and solar energetic particle events, thus helping mitigate their effects on technological systems. Additionally, the project fosters collaboration between computer scientists and heliophysicists while providing open-access tools and datasets to the research community. The project also involves student groups in hands-on research and training, offering mentorship opportunities, and partnering with high schools. This contributes to building a skilled STEM workforce, advancing scientific knowledge through data-driven analysis, advancing core scientific knowledge and contributing to national security. This project develops a cyberinfrastructure for AI-enabled multimodal extreme space weather events forecasting. The cyberinfrastructure enables predictive modeling of solar transient events by integrating solar photospheric magnetogram data spanning three solar cycles or more than 30 calendar years. A key technical advancement is the application of generative AI, with a physics-infused conditional diffusion model, to enhance historical datasets by filling spatial and temporal resolution gaps. Another significant contribution is using multimodal machine learning and explainability techniques to improve the current state-of-the-art space weather prediction methods. The project builds a homogenized dataset of vector magnetograms, open-access computational tools, and machine learning models to process vector magnetograms, time series, and derived metadata parameters. The cyberinfrastructure integrates scalable generative AI techniques and multimodal learning frameworks to optimize forecasts of solar flares, coronal mass ejections, and solar energetic particle events while providing an interoperable learning framework for heliophysics research, benefiting both the scientific community and sectors reliant on accurate space weather predictions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Forest ecosystems provide important resources to humans but face significant challenges from high temperatures and droughts. It is urgent to understand how forests respond to these stresses so we can improve planning and management. This project seeks to understand how trees may slow their growth or die during drought and how important access to deep water is, depending on where trees are growing across the landscape. The researchers will use many tools, including direct forest measurements and satellite observations. Computer models will be used to understand those observations and make forecasts of how forests might behave in the future. The research will be used to improve teaching of students of all ages and seek to expose them to new areas like computer science, ecology and hydrology. Results from this project will be shared widely with the research community and incorporated into classroom activities. The project will promote the participation, recruitment, and retention of students at all educational levels from K-12 to Ph.D. via a series of formal interdisciplinary opportunities for coursework, collaboration, and networking. Elevated forest mortality triggered by extreme drought and heat has been globally observed, with manifold consequences for ecosystem structure, function, and feedback to climate. Predicting tree mortality in response to climate extremes is a long-standing and unresolved scientific problem. The project will evaluate novel hypotheses about forest mortality by assessing the impacts of hydrology on ecosystem function via the expected interaction between groundwater access and plant physiological traits. Key drivers and mechanisms identified from the research will inform the development of Earth system models, paving the way for improved model representation and predictive abilities. This project is jointly funded by the Ecosystem Science Cluster in the Directorate for Biological Sciences and the Water, Landscape, and Critical Zone Processes program in the Directorate for Geosciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This award supports the development of advanced computational methods for tracking and analyzing evolving patterns in large‑scale networks. Patterns of connections among entities, known as subgraphs, underpin insights in domains such as social interactions, biological processes, financial transactions, and communication systems. Real‑time analysis of how these patterns form and dissolve can enable early detection of disease outbreaks, improved understanding of social dynamics, and enhanced network security. By creating scalable and accessible tools for dynamic network analysis, this project will advance the national interest in data‑driven discovery across science, technology, and public welfare. The project will pursue three integrated research thrusts. First, it will develop novel algorithms with provable efficiency guarantees for counting and enumerating subgraphs in the batch‑dynamic model on parallel and distributed systems. Second, it will design and implement high‑level programming frameworks and data structures tailored to dynamic graph workloads, including graphics processing unit (GPU) and distributed implementations, to facilitate practical adoption. Third, it will integrate the new algorithms and frameworks into an open‑source analysis platform and conduct comprehensive evaluations on high‑performance computing clusters and cloud resources. These efforts will yield the first provably‑optimal dynamic subgraph counting algorithms for higher‑order patterns, query‑based enumeration techniques, and user‑friendly software enabling researchers to perform real‑time analysis on evolving networks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This award intends to improve infrastructure operations by supporting research on new economic mechanisms for markets with discrete decisions. A prominent example is unit commitment, which schedules when each fuel-burning generator in a power system is on and off. Startup and shutdown account for a significant portion of the cost of generation, and they are binary decisions because a generator can only be on or off. The discreteness of unit commitment undermines standard economic mechanisms. In particular, resulting prices are generally too low, so that system operators must make out-of-market uplift payments to keep generators profitable. In general, discreteness such as this can create negative incentives in markets, leading to inefficient investment and decision-making. Research to be completed in association with this project intends to offer a new approach to designing pricing mechanisms in discrete markets, with the focus on power systems and electric vehicle charging. It will produce new tools and deepen understanding of tradeoffs in discrete markets. The project team has expertise in power systems and transportation, optimization, and market design, and will train graduate students with the multidisciplinary perspectives needed for this project. This project will investigate the use of copositive programming as a tool for designing economic mechanisms for discrete markets. The approach is based on a fundamental result from Burer (2009), which establishes that a mixed-integer linear program can be equivalently represented as a copositive program. Copositive programming is a conic optimization class that is both convex and NP-hard. As such, it does not provide a better way to solve mixed-integer linear programs. However, its convexity does provide a new notion of duality for discrete problems. Therefore, given the copositive representation of a discrete problem like unit commitment, the dual is immediately available in standard form. The dual variables have economic interpretations as shadow prices and can be used to design market mechanisms such as prices and option contracts. Convexity also brings tools like the Karush-Kuhn-Tucker conditions, which are helpful for establishing properties such as efficiency, revenue adequacy, and fairness. This project will explore how to use these and other features of copositivity to better understand and design discrete markets. As copositive programming is computationally hard and of relatively recent interest, it does not yet have mature solution techniques. This project also aims to make copositive programming more practical via the use of machine learning-based, distributed, and online algorithms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This award will be used to fund the 2025 conference Frontiers in Computational and Applied Mathematics (FACM), to be held at the New Jersey Institute of Technology (NJIT) on June 5-6, 2025. The theme of FACM 2025 is "New trends in data- and physics-based modeling and simulation of fluids". Since its inception in 2004, the FACM conference has established itself internationally as a leading forum for the dissemination of recent results and new ideas in applied and computational mathematics and applied statistics. It attracts a distinguished international audience and provides an important forum to showcase US mathematical talent, and opportunities, especially junior participants, for networking with researchers in academia and industry. This year's conference will explore the advances that have been made in recent years by applying new computational tools and techniques (including machine learning and artificial intelligence, among others) to challenging problems that arise in fluid dynamics. A particular focus will be on application areas, which range from industrial challenges to biomedical applications to large-scale ocean flows. This year's FACM conference theme will address the many exciting emerging new trends in data- and physics-based modeling and simulation of fluids. New research will be presented and discussed in a variety of forums. There will be four plenary presentations of an expository nature, given by recognized US-based leaders in their fields, each of which will set the scene for a focused minisymposium session. The four minisymposium sessions will focus on: fluid-structure interaction, particularly in the realm of biological flows; complex fluid flows arising in applied physics and industrial applications; new trends in the modeling and simulation of wave propagation; and computer-assisted proof in fluid dynamics (particularly for partial differential equations arising in wave-propagation). A highlight of the conference will be the poster session, anticipated to be extensive, dynamic and stimulating. As one might expect from a workshop focusing on cutting-edge research, methodology that draws on data science, topological data analysis, machine-learning and scientific computing will be featured, as well as more traditional areas such as applied analysis and asymptotics - often in intriguing new combinations. The conference website, which will be updated as more speakers are confirmed, can be found at https://sites.google.com/njit.edu/facm2025/home. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This I-Corps project focuses on the development of a smart indoor comfort system that adjusts personal room conditions based on each person’s unique needs. The technology is beneficial for individuals who are sensitive to their environment, such as seniors and those with chronic health conditions. Many buildings rely on fixed temperature settings that overlook personal preferences, often leading to ongoing discomfort and even potential health concerns. This challenge affects people in homes, hospitals, offices, and other indoor spaces. The system integrates wearable sensors, environmental monitors, and a mobile application to collect real-time data and support dynamic indoor condition adjustments. By aligning the environment with how a person actually feels, the system enhances comfort and well-being. Its broader adoption could lower energy costs, support energy efficient building practices, and improve the quality of life for occupants. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a smart, adaptive environmental control device that integrates wearable sensors, environmental modules, and a digital application. Unlike traditional systems, which rely on static settings, this approach dynamically responds to changes in user needs and environmental conditions. The wearable sensor tracks physiological data, such as body temperature and heart rate, while the environmental module measures room conditions, such as temperature and humidity. The digital application collects user feedback on thermal sensation, merging individual and environmental data using online analytics. This solution enables continuous, personalized adjustments based on real-time modeling, offering context-aware adjustments. This system’s flexibility and efficiency could advance smart building systems, energy efficiency, and occupant well-being. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

People who are experiencing cognitive decline, such as people living with dementia and mild cognitive impairment, want to maintain their independence, even as their abilities change over time. To support this goal, this project will design "future proofing" approaches, in which people use smart objects in their homes and routines that can help them adapt to anticipated changing cognitive abilities over time. The research team will create toolkits, training sessions, and guidebooks that will allow people living with cognitive impairments and when appropriate, their caregivers, to invent, make, and use their own future proofing systems. If successful, ideas and outcomes from this project will help people with changing cognitive abilities maintain their independence for longer periods of time, with better quality of their own life and their relationships with friends, family, and community. The main goal of this project is to create a framework for designing technology that can adapt to changing cognitive abilities over time. The research team will first conduct diary studies to find out when people with changing cognitive abilities experience those changes, what impacts they have on their independence, and what ideas they have for reducing those impacts. The researchers will then hold co-design workshops based on those insights, working with people experiencing cognitive impairments and caregivers to create and develop customizable systems that can adapt to changing abilities and support future proofing approaches to maintaining independence. The team will also develop training, examples, and other materials that will help people adopt and adapt future proofing systems to their own needs. To assess these systems and materials, the researchers will do a long-term study of people using the smart objects in their own lives to understand the extent to which people can make use of them and how well these systems support the project's goals of helping people adapt to longer-term changes in cognitive abilities. Through this, the project lays the groundwork for future exploration of technologies that intelligently adapt to the needs of people experiencing progressive changes in ability more generally. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This award provides support to the Ham Radio Science Citizen Investigation (HamSCI) Workshop to be held at the New Jersey Institute of Technology (NJIT) between March 14-15, 2025. The HamSCI Workshop is an annual workshop that aims to bring together members of the amateur radio and professional radio space science communities for mutual benefit. This workshop series has led to cutting-edge work in the fields of space physics, citizen science, and the use of crowd-sourced ionospheric data. The 2025 HamSCI Workshop will feature prominent leaders in radio science, space science, and space weather. The theme of the 2025 workshop is “HamSCI’s Big Year” and refers to the immense level of organization and activity undertaken by HamSCI participants over the past year to support scientific studies of the 14 October 2023 annular and 8 April 2024 total solar eclipses. During this two-day workshop, professionals and amateurs come together to give oral and poster presentations, equipment demonstrations, and have informal discussions and networking related to both amateur radio and applicable scientific fields. Several distinguished speakers have been invited. The 2025 workshop will continue building on topics from past HamSCI meetings, including the use of amateur radio techniques for observing and understanding the physics of space weather, including the ionospheric response to solar flares and geomagnetic storms, and space weather effects on radio wave propagation. The workshop will feature prominent leaders in radio science space weather. It will also serve as a development meeting for the HamSCI Personal Space Weather Station (PSWS), a project funded through the NSF Aeronomy Distributed Array of Small Instruments (DASI) program. This conference would enable (a) development of open technologies and observation networks that can be used in conjunction with existing space science and space weather infrastructure, (b) creation of materials, projects, and products that can be used by groups such as schools, museums, and other educational institutions to teach space and radio science, and (c) enhancement of public relations and outreach through venues and organizations such as the American Radio Relay League (ARRL), CQ Magazine, the HamSCI online presence, the American Geophysical Union, and the NSF Coupling, Energetics, and Dynamics of Atmospheric Regions (CEDAR) program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This I-Corps project focuses on the development of advanced phase change materials designed to improve energy efficiency and temperature control in insulated packaging and environments where thermal regulation is essential. Inefficient temperature management leads to substantial energy waste and increased operational costs. The need for better temperature control affects a broad range of industries, including residential, commercial, industrial, transportation, and supply chain logistics sectors. Energy use for heating and cooling accounts for a significant portion of total energy consumption globally. Existing phase change material solutions often suffer from poor thermal properties, limited operation, and lack of adaptability to specific applications. This technology addresses these shortcomings by enabling more precise and efficient temperature stabilization through the efficient storage and release of energy. The materials are designed to effectively absorb excess heat when temperatures rise and release stored heat when temperatures fall, significantly reducing the energy burden on heating, cooling, ventilation, and air conditioning systems. By improving how thermal energy is managed, this technology has the potential to reduce energy consumption and enhance comfort, safety, and resilience. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of nano-engineered phase change materials with enhanced thermal conductivity, heat storage capacity, and structural stability. Functionalized carbon nanostructures were synthesized and incorporated into selected base phase change materials to function as thermally conductive and chemically interactive scaffolds, yielding composite systems with enhanced latent heat storage, accelerated thermal response, and improved structural integrity. Conventional phase change materials are often constrained by low intrinsic thermal conductivity, limited enthalpy of transition, and mechanical degradation under cyclic thermal loading. In contrast, these phase change material composites demonstrate enhanced phonon transport pathways, enhanced heat storage capacity, and morphological stability during repetitive phase transitions. The core innovation lies in the tailored interfacial interactions between the carbon-based nanomaterials and the phase change molecules, which facilitate efficient thermal energy transfer, activate additional heat storage modes in the composite matrix, and mitigate material fatigue over extended operational lifespans. This approach also allows the optimization of the thermal properties of the composites by tuning different chemical interactions. Broad deployment of these advanced composites offers substantial reductions in energy consumption, extended device lifetimes, and increased system-level thermal efficiency, with measurable economic benefits across sectors demanding high-performance thermal regulation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This I-Corps project is based on the translation from lab to market of an advanced artificial intelligence (AI) software tool designed to enhance molecular and drug discovery processes. Drug discovery is a crucial aspect of pharmaceutical development, directly impacting human health and the creation of life-saving treatments. Applying an AI tool that generates interpretable explanations for how and why drug candidates were suggested can accelerate the drug discovery process. The commercialization of this technology has the potential to benefit society by reducing the time and cost associated with pharmaceutical research and development, leading to faster drug approvals and broader accessibility to new treatments. With traditional drug development costing over $2 billion and taking 10-15 years, this solution has the potential to accelerate and de-risk drug discovery, making it attractive to pharmaceutical firms and research institutions. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a Graph Neural Network (GNN)-enhanced Explainable software suite for drug discovery. The software integrates GNN-explainers that provide interpretable explanations for the GNN-based model, with extensive molecular datasets to identify functional compounds more efficiently and with greater interpretability than existing AI-driven approaches. The system includes input preparation of diverse chemical and biological datasets to train black-box GNN models. An explainer framework then analyzes the black-box GNNs, developing explanation sub-graphs and identifying graph information bottlenecks through a GNN-enhanced explainable AI approach. The benefits of this approach include improved efficiency in identifying viable drug candidates, reduced computational costs, and enhanced transparency in AI-driven molecular discovery. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

The impact of this I-Corps project is based on the commercialization of an innovative pretreatment method designed to enhance the performance of Granular Activated Carbon (GAC) filtration in water treatment plants focused on removing per- and polyfluoroalkyl substances (PFAS). PFAS have wide use in both industrial and consumer products. However, PFAS are linked to adverse health effects, even at exceptionally low parts per trillion (ppt) levels. Once PFAS enter the environment, these substances do not easily break down, making their removal a significant challenge. The fundamental issue this solution seeks to address is the inadequate effectiveness of existing GAC filtration in removing PFAS from contaminated water, resulting in low treatment performance and increased operational costs for drinking water treatment facilities. At least 45% of U.S. tap water contains PFAS, and nearly all Americans have PFAS in their blood, underscoring the widespread nature of the problem. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential a new technology for removing PFAS from water. This technology is based on a novel hydrophobic ion-pairing (HIP) pretreatment for Granular Activated Carbon (GAC) filters that enables the filters to capture short-chain PFAS effectively. The HIP technique involves forming ion pairs between charged hydrophilic molecules and hydrophobic counterions, resulting in neutral hydrophobic complexes. In this pretreatment method, the interaction between charged PFAS and the HIP agent creates a hydrophobic complex, enhancing adsorption onto the GAC without altering the filter material itself or requiring complex modifications. The benefits of this approach include increased removal of short-chain PFAS in compliance with regulatory standards, extended lifespan of GAC filter columns used in water treatment facilities, improved adsorption efficiency, and reduced GAC waste. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This I-Corps project focusses on the commercialization of a software solution to optimize the selection of materials used to design and develop solar panels. Software solutions have the potential to significantly enhance the efficiency and longevity of solar panels by preventing overheating, which is a critical challenge in the solar energy sector. Rapid and accurate material selection can also be applied to other industries requiring thermal management solutions, including rechargeable batteries, semiconductors, and electronics. The increasing demand for improved energy efficiency, coupled with the scalability of the software, could offer a more cost-effective solution for improving the functionality, reliability and durability of solar panels, energy storage devices, and other electronics. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a proprietary machine learning (ML) algorithm to identify, design, develop, and synthesize polymer nanocomposites. These materials address critical challenges in thermal management and barrier protection for a range of industries, including solar, rechargeable batteries, semiconductors, and supercapacitors. By utilizing machine learning modeling for material classification, the design, development, and selection of materials with the desired properties is accelerated. These nanocomposites may reduce infrared absorption, provide UV shielding, and perform as a barrier against oxygen, moisture, and other contamination, improving the functionality, reliability, and durability of solar panels. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This I-Corps project focuses on the development of a new on-site treatment technology using high frequency ultrasound to mineralize waste streams containing per- and polyfluoroalkyl substances (PFAS). PFAS are a large and complex class of man-made compounds. Due to their persistence and potential toxicity to human and ecological receptors, PFAS have generated a strong public and regulatory response to their ubiquitous environmental presence. The need for the remediation of PFAS is growing due to societal and regulatory awareness in the wake of rapidly evolving toxicology research on this class of contaminants. This technology has the potential to impact various industries dealing with hazardous waste management, particularly in addressing the ongoing environmental and health threats posed by PFAS contamination. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This technology is based on the development of an innovative approach for decomposing persistent PFAS compounds using a combination of ultrasound and Argon nanobubbles. PFAS are a group of chemicals resistant to conventional degradation methods due to their strong carbon-fluorine bonds. Traditional methods such as incineration have proven inefficient, costly, and harmful to the environment. This novel solution exploits the synergistic effects of continuous Argon nanobubbles and ultrasound waves to disintegrate PFAS into benign byproducts such as fluoride ions and carbon dioxide. Preliminary experiments have shown significant improvements in PFAS degradation efficiency when compared to conventional methods, achieving enhanced destruction while lowering costs, and minimizing harmful emissions. The system is designed to be scalable for industrial use, offering a commercially viable and environmentally sustainable solution for PFAS contaminated water treatment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Solar phenomena such as solar flares, active regions, and solar coronal holes, are very important for understanding causes of the solar activity. Disturbances in the near-Earth space known as Space Weather originate from these solar sources and cause disruptions to communication networks. Radio measurements provide unique diagnostics both before and during solar eruptions. This project will provide routine radio observations of the solar activity using two modern radio solar arrays located at the Owens Valley Radio Observatory as an integrated facility to serve the solar physics and Space Weather communities. The two arrays, the Expanded Owens Valley Solar Array and the Long Wavelength Array, are both world-class solar radio telescopes. They are also the only instruments in the U.S. that provide continuous radio imaging observations of the Sun. This project will support their operations as well as investigations of the wide range of solar phenomena which is important for protecting national assets such as satellite communication networks from harmful Space Weather effects. The project will advance our understanding of solar activity in four key areas: (1) the magnetic energy release as well as the associated particle acceleration and transport processes, (2) the Coronal Mass Ejections, shocks, and solar energetic particles as drivers of Space Weather, (3) the plasma, magnetic structures, and dynamics of solar active regions, and (4) the quiescent solar atmosphere and small-scale transients. The project will provide open-access, science-ready data to the community along with a user-friendly interface as well as near-real-time data products of use in operational forecasting and nowcasting of solar and Space Weather events. The project will also support a science workshop and data camp each year to provide training for analyzing and interpreting the solar array data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Society has been witnessing an increasing integration of advancements of Artificial Intelligence (AI) into Internet-of-Things (IoT), namely AI of Things (AIoT), covering applications ranging from smart health, power grid and robotics, to multimedia processing in cyber-physical systems. The widespread applications of AIoT generate complex and massive amounts of data that evolve across both space and time (spatio-temporal data). Efficiently analyzing these spatial-temporal datasets is critical for improving the performance and the cost-benefit of AIoT applications. This project devises novel techniques to analyze spatio-temporal data in an interpretable way, thus advancing the next-generation IoT and AI technologies. Moreover, this project shares its research outcomes, including newly developed algorithms and interpretable AIoT solutions with the public, and offers educational opportunities for undergraduate and graduate students. The goal of this project is to investigate spatial-temporal data analysis in AIoT systems with the advancement of graph signal processing and graph learning techniques, structured via four research thrusts: (i) development of novel topology sampling and graph neural network pruning of single-layer graph models for data analysis efficiency; (ii) investigation of multilayer graph models to improve spatial-temporal data processing; (iii) analysis of propagation behavior and dynamic graph evolution in AIoT systems; and (iv) establishment of parameter-efficient transfer learning for spatial-temporal signal processing. This project advances both theoretical foundations of graph learning and practical solutions to AIoT applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

STARS Computing Corps aims to address the challenge of increasing the number of students who graduate with computing degrees and who remain in the field of computing after graduation by implementing evidence-based programs designed to promote persistence in computing degree programs and entry to the tech workforce. STARS especially seeks to expand the national community of computing students by improving pathways for computing and Artificial Intelligence (AI) education and to the future computing and AI workforce. Through this project, STARS will continue its national community of practice—open to all college students, faculty, and administrators in computing-related fields—and associated resource center to build capacity in college computing departments for developing more comprehensive computing and AI educational experiences. Ultimately, the work of STARS has the potential to expand computing persistence and participation at all education levels and across disciplinary boundaries leading to computing career opportunities including AI, quantum, and biotechnology. STARS creates significant knowledge, institutional, and human resources that can increase the reach of computing education research to a larger audience of researchers, educators, and K-20 students. STARS conferences, programs, and networks disseminate evidence-based approaches and advance peer-reviewed scholarship for computing students. STARS activities include: 1) developing undergraduate student leadership abilities and provides resources that support their academic and professional development; 2) a year-long program (STARS AI Scholars) for undergraduate students, graduate students, and K-12 teachers to learn about foundational AI concepts, gain exposure to AI research topics, and engage in AI-focused outreach activities for K-12 students; 3) providing undergraduate students with early opportunities to engage in computing and AI education research experiences; 4) an in-person event providing sessions on academic and professional development to promote computing degree persistence, career awareness, and workforce entry; and 5) fostering a network that connects STARS undergraduate students with computing professionals to provide mentorship and guidance as students’ navigate a wide array of computing career opportunities including AI, quantum, and biotech. STARS will also conduct research aimed at identifying practices and programs that improve pathways to the future computing and AI workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Per- and polyfluoroalkyl substances (PFAS) are a class of chemicals that can contaminate the environment and affect public health. Because of the health concerns, there is a critical need to understand how humans are exposed to PFAS compounds transported through the environment. On August 19, 2024, there was a spill of PFAS-containing firefighting foam at the Brunswick Executive Airport in Brunswick, Maine. Investigators will measure PFAS concentrations in soil and water samples obtained from multiple sites around the spill over the course of one year. These measurements will be used to assess how these PFAS “forever chemicals” move through soil and water over time. Benefits to society from this project include data sharing with scientists and educators to advance knowledge, and results disseminated in the form of peer reviewed studies that regulators, policymakers, and other stakeholders can use to implement better strategies for emergency response to such spills. Research has demonstrated that PFAS contamination of the environment has significant effects on human and ecological health. Researchers typically have access only to studies of sites that have been contaminated in the past such as landfills, burn pits, or former manufacturing facilities. This results in a significant knowledge gap in our understanding of PFAS fate and transport from recent releases. A spill of firefighting foam containing high concentrations of PFAS at the Brunswick Executive Airport presents a once-in-a-lifetime opportunity to reveal mechanistic insights about PFAS contamination after a spill in a well-defined watershed. The goal of this project is to understand the distribution of PFAS within the soil horizon and the impacted watershed as a function of time and proximity to the spill site. The specific research objectives are to collect, archive, and analyze PFAS in soil and water samples at various locations over time to reveal mechanistic insight on PFAS fate and transport. Results from this study can help develop mitigation strategies for emergency response groups to prioritize containment and clean up. In addition, the work can help impacted municipalities implement effective water advisories for their residents, as well as benefiting a broader group of researchers, policymakers and other stakeholders who study and manage PFAS contamination. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The Ham Radio Science Citizen Investigation (HamSCI) network is a community-powered observatory that studies how space weather affects Earth’s upper atmosphere and the technologies society relies on, including communication and navigation systems. Operating as a Distributed Array of Small Instruments (DASI), it uses Personal Space Weather Stations (PSWS) to collect data across a growing network of stations in the U.S., Canada, Alaska, and Europe, strengthening understanding of space weather impacts. Built through close collaboration between professional scientists and the amateur radio community, the PSWS network currently includes more than 35 operational stations, many hosted by licensed radio operators who contribute instrumentation, data collection, and technical expertise. Each station measures key features of the geospace environment, including high-frequency radio signal variations, very low frequency transmissions, natural radio emissions, and changes in Earth’s magnetic field. This project will expand and standardize the network by deploying thirty additional stations and ten GPS-disciplined transmitters that provide stable beacon signals across large regions, enabling detailed studies of solar flares, geomagnetic storms, and traveling ionospheric disturbances. Together, this community-driven infrastructure supports hands-on education, workforce development, and improved resilience of critical communication and navigation systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

This project will study how the matrix (the mixture of proteins and other molecules that surrounds cells and provides structural and biochemical support to tissues) and the surrounding interstitial (between the spaces) fluid pressure can impact cancer cells within a biomimetic (biology imitating) model. The chemical and physical signals lead to cancer cells invading nearby tissue. A well-known physical signal is interstitial fluid pressure, and biomimetic pressure modeling can improve the current cancer models. The project aims to create interstitial pressure around breast cancer cells and measure the cell response over time. Light-assisted bioprinting technology and microfluidics will be used to construct the tunable solid tumor model. Microfluidic design and hydrogel parameters will be applied to explore the role of pressure gradients in regulating cell migration and metastasis. The results of this project can be used to design tunable cancer models for drug screening applications. In addition, the proposed research will positively impact Science-Technology-Engineering-Mathematics (STEM) education by training undergraduate students at the New Jersey Institute of Technology (NJIT). A summer training program will be developed to broaden the participation of underrepresented groups and community college students in academia. The investigators hypothesize that the metastatic and migratory behavior of solid tumor cells in three-dimensional (3D) microtissues can be modulated by matrix pore size, fluid-induced pressure, and matrix stiffness gradients. The 3D microtissue is an aggregate of cells and essential microenvironment cues with a pre-defined assembly. Microtissue advancement toward patient- and stage-dependent models requires understanding how the microstructure and biophysical cues of the tumor environment can regulate cells' invasiveness and metastatic behavior. Three research tasks are proposed to study the role of fluid-induced pressure in directing tumor cell invasion. First, a tumor spheroid-laden microtissue model with controlled microstructure will be 3D bioprinted, characterized, and optimized. The parameters of a photocrosslinkable gelatin-based bioink will be adjusted based on the desired fluid-induced pressure. Second, the correlations between cells’ invasive behavior and microstructure will be quantified via real-time cell tracking and measurements of gene expressions over time. Numerical simulation will also be used to identify the localized pore pressure and hypoxia gradients in the model. Third, the cross-correlation of tumor vasculature and interstitial pressure gradient in controlling tumor cell migration will be quantified and assessed to identify their impacts. Statistical analysis will be performed to determine the governing structural parameters in the solid tumor microtissue model. The accomplishment of this project will enhance our understanding of the mechanisms of drug resistance in invasive cancers such as triple-negative breast cancer. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

This three-year renewal REU Site: Optics and Photonics: Technologies, Systems, and Devices is hosted by the New Jersey Institute of Technology (NJIT). The project focuses on engaging undergraduate students from diverse backgrounds and from institutions with limited research opportunities in cutting edge research. Ten students each year will be recruited and selected for this program, engage in research projects, professional development, and explore STEM careers and graduate school preparation. Emerging research topics feature optics and photonics technologies, systems and devices (i.e., optical imaging, optical nano-fabrication, optical communications). The Site will address the growing national need to provide career opportunities for students in the areas of Optical, Photonic and Semiconductor Devices. Optical and photonic technologies, devices and systems enable modern communication systems, sensors, computing, healthcare systems, and environmental systems. By focusing on next generation optical and photonics technologies and systems, this program seeks to increase the number of students entering the workforce with skills in these areas. Optics and photonics play a significant role in the semiconductor devices such as light emitting diodes (LEDs) and photodetectors that are used as communication devices. CMOS imagers in digital cameras and laser diodes with critical applications are part of chip-shortage problems. Undergraduate students will engage in optics and photonics research, with faculty mentors and their research teams, during an intensive and unique 10-week summer program in three focus areas: 1) optical, photonic and semiconductor devices, (2) optical systems, and (3) optical communications and networking. Participants will focus on comprehensive readings and become familiar with specific equipment needed for their projects. The REU students' laboratory experiences will be enhanced by technical seminars and workshops on non-technical topics, such as presentation skills and ethics, and visits to sites such as the $20 Million Microfabrication Innovation Center for semiconductors and the NJIT Makerspace housing industrial grade tool and technologies where students can test ideas, visualize, create prototypes, and apply complex technologies, machines and materials used in manufacturing. Each student will prepare a final report and give a presentation on his or her project at the end of the program at NJIT's International Undergraduate Summer Research Symposium. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

This project will exploit high resolution plasma line observations made by the Arecibo Radar to significantly increase the number of photoelectron observations available to the community. The Earth’s ionosphere is created by the Sun’s radiation, which breaks up particles in the upper atmosphere to create plasma. One product of this ionization process is a high energy electron called a photoelectron. Photoelectrons are constantly created during the day and are important for sustaining and heating the ionosphere. Measurements of photoelectrons are rare and difficult to make. Archived experiments from the Arecibo Observatory are one of few datasets that can be used to create meaningful photoelectron measurements. This work will further our understanding of the interaction between photoelectrons and plasma in the ionosphere. The primary output of this research is altitude resolved measurements of the photoelectron distribution which will be made available to the community. The team is diverse in gender and career stages, including early career researchers. This project will support a graduate student and undergraduate students at an MSI (NJIT). Furthermore, reports and presentations of this research will broaden the community and public’s understanding of Arecibo’s legacy as a unique, world-class instrument. Measurements of photoelectrons in the ionosphere are rare and difficult to make, particularly in situ. This project will significantly increase the number of photoelectron observations available to the community by exploiting high resolution plasma line observations made by the Arecibo radar. The team will use a combination of experiment, kinetic plasma theory, and data science to answer the following questions: 1. Does a given photoelectron distribution provide a unique set of plasma line observations? 2. Do asymmetries in the photoelectron distribution create asymmetries in the upshifted versus downshifted plasma lines? and 3. What is the effect of photoelectrons on the frequency of the plasma line? The work will improve our understanding of the photoionization process, and the pathway photoelectrons take to heat the ionosphere and resolve the anisotropy of the photoelectron distribution and assess the relative importance of local production and vertical transport of high energy electrons. This work will also create a catalogue and archive of past plasma line experiments at Arecibo, making this unique dataset more accessible to the community through the Madrigal database. The Geospace Facilities (GF) Program cofunds this project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.