Hofstra University

NSF Awards · FY 2026 · 2026-07

This award supports the participation of American researchers in Workshop in Computability Theory which will be held August 3-7, 2026, at the Technische Universität Wien in Vienna, Austria. The focus of this workshop is the foundations of computation, specifically computability theory and complexity theory. Researchers in these fields address questions about the difficulty of solving a computational problem, whether in terms of the time and space required for a computer to do so or in terms of the relative difficulty of problems that a computer cannot solve. The primary objective of this conference series is to expand the pool of researchers in computability and complexity theory by supporting and encouraging researchers from these areas in their investigations of research topics that are new to them, and this grant makes it possible for American researchers to develop their international professional networks and strengthen the United States’ reputation in these areas. This conference is structured to enable researchers in computability or complexity theory to immerse themselves in a research project in an area they have not worked in before under the supervision of leading researchers in this area. These projects will involve topics such as algorithmic information theory, propositional proof complexity, and computational complexity from the perspective of computable structure theory, and they have been selected not only to cover a broad array of topics but also to allow a group of approximately five or six people to make tangible progress on them within a week with room to continue the collaboration after the workshop. This will extend the participants’ research networks beyond their current subfields. The conference website can be found at https://www.dmg.tuwien.ac.at/cocogems/. 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 · 2025-10

Due to the ubiquity of wireless systems, any advancement in their fundamental capabilities significantly impacts society. Such advancements can enhance the bandwidth of wireless networks, increase the precision of military and weather radar systems, and boost the performance of scientific instruments. Radio-frequency (RF) antennas play a crucial role in these wireless systems by converting electrical signals into electromagnetic fields that radiate in specific directions. By precisely controlling the direction of these electromagnetic waves (known as the radiation pattern), wireless systems can concentrate their signal power efficiently, enhancing their overall effectiveness. The advent of phased arrays, which are collections of antennas that can electronically alter their radiation patterns, has revolutionized the capabilities of wireless systems. However, traditional phased array performance is limited by the properties and placements of their individual antennas. These challenges can be addressed by integrating metamaterials (passive metallic structures that manipulate electromagnetic fields in beneficial ways) into the phased array. Recent advances in metamaterials equipped with programmable switches have shown promise in dynamically adjusting radiation patterns, paving the way for phased arrays with improved control over electromagnetic waves. This project aims to tackle the computational challenges associated with optimizing the settings of these programmable metamaterials. By leveraging recent advances in artificial intelligence (AI), a deep neural network is trained on both simulated and experimental data to uncover the relationships between switch settings and radiation pattern characteristics. The network is then analyzed to create a human-interpretable representation of these relationships. The insights gained then inform enhancements to the deep neural network, the optimization approach, and the design of improved metamaterials. Ultimately, the project seeks to advance the field of programmable metamaterial design and enhance the performance of antenna and phased array technologies. On the education side, this project establishes a new integrated research and education program in Hofstra University, a primarily undergraduate institution, to motivate and train students toward professional careers in RF engineering, contributing to the workforce development in the local civilian and military telecommunications industry. This project characterizes the optimization problem presented by programmable metamaterials embedded in the near-field of a phased array, focusing on three main research objectives: (1) develop an effective deep learning approach that solves example problems and efficiently utilizes computational resources, (2) visualize and characterize the optimization problem's feature space while analyzing the electromagnetic properties of these features, and (3) identify methods to simplify the optimization problem and enhance metamaterial design by leveraging the feature space and its electromagnetic properties. To achieve objective (1), a convolutional neural network is trained using supervised learning with simulated training data. Transfer learning techniques fine-tune the neural network on an existing array with embedded programmable metamaterials; the array is also utilized to test the network's performance. For objective (2), feature visualization and attribution techniques extract and interpret the features identified by the neural network. These features undergo study through finite element analysis to identify the electromagnetic mechanisms driving performance improvements. Additionally, the features are examined to identify patterns that can be leveraged to reduce the complexity of the optimization problem. Finally, for objective (3), insights into the optimization problem's feature space inform strategies to simplify the optimization problem and enhance metamaterial designs, further improving the performance of both the neural network and the phased array. 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 · 2025-10

NON-TECHNICAL SUMMARY A deep understanding of how electrons behave in materials has powered some of the most transformative technologies of modern life, from personal computing and mobile devices to weather prediction and artificial intelligence. These advances were made possible through breakthroughs in semiconductor technology, for example the invention of the transistor. Today, researchers are exploring a new class of semiconductor interfaces, known as two-dimensional "moiré materials", that may drive the next generation of innovations. These materials have the potential to support new technologies such as ultra-efficient spintronic devices and may one day become the building blocks of fault-tolerant quantum computers. However, scientific understanding of these materials is still in its early stages. In particular, existing theoretical models often rely on simplified, qualitative approaches that cannot fully capture the complex behaviors observed in experiments. This project aims to develop more accurate, predictive tools to model these systems by combining machine learning (ML) techniques with advanced quantum simulation methods. The goal is to discover new electronic phases of matter, characterize their properties, and generate reliable benchmark data to support future research. In addition to advancing materials science, the project will broaden access to computational research. The principal investigator (PI) will mentor undergraduate students in areas such as programming, scientific computing, and data analysis. The project will also make use of Hofstra University’s new high-performance computing cluster and include outreach events designed to introduce students from across disciplines to essential computational research skills. TECHNICAL SUMMARY This project investigates strongly correlated electronic phases in two-dimensional moiré materials, with the goal of advancing our theoretical understanding of these materials from qualitative to quantitative. Moiré materials host long-range periodic potentials that enhance electron-electron interactions, leading to unconventional electronic and magnetic states such as generalized Wigner crystals, superconductors, and topological insulators. These phenomena are not well described by standard mean-field theory. The intellectual merit of the project lies in its use of many-body wavefunction techniques, which can uncover novel correlated electronic phases that are inaccessible to the standard approach. The research that will be carried out will employ a multi-messenger computational framework that integrates density functional theory, diffusion Monte Carlo, and neural quantum states simulations. The PI will use machine learning techniques to expand the flexibility of variational ansätze for strongly correlated systems and interpret the emergent solutions through the lens of many-body physics. High-accuracy diffusion Monte Carlo simulations can help distinguish physically meaningful phases from artifacts of variational overfitting. The resulting benchmark datasets will serve as reference points for evaluating and improving approximate methods. The broader impacts of the funded activities will include: (1) establishing a computational research community at Hofstra University and neighboring institutions, including Adelphi University and Nassau Community College. The PI will host tutorials to teach the basics of ML and computational research to interested students, introducing skills that are transferable across disciplines; (2) training undergraduate researchers in high-performance computing, AI-assisted simulation, and scientific software development; and (3) using the project’s outputs to enhance interdisciplinary education in computational science. By leveraging Hofstra’s new high-performance computing infrastructure and its connections with the local community, this project will contribute to the development of a more inclusive and data-literate scientific workforce prepared for the 21st-century. STATEMENT OF MERIT REVIEW 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 · 2025-09

Microelectronics are the backbone of modern devices and systems. Introducing students to this field can spark interest in careers which are of vital importance in the modern economy. This high school strand CSforAll Research-Practice Partnership Project between Hofstra University, Birdbrain Technologies, North Carolina State University, and 13 high school technology and engineering teachers aims to ensure that high school students learn about microelectronics through the use of hands on, real-world problem-solving opportunities. The project will enhance the microelectronics dimension of the Advanced Placement Computer Science Principles (AP CSP) course curriculum and materials to enliven students’ interest in the big ideas of computing. The project will develop students' interest and motivation in microelectronics and stimulate creativity, with the potential to improve educational practices that would yield broad economic, scientific, and workforce benefits to individuals and society as a whole. MicroCS is a High School Strand research-practice partnership. project. RPP partners include members of an implementation and management team, a Research Team, and a Practitioner team comprised of 13 technology and engineering teachers. The project will be carried out in Carroll County, MD; Wilmington, NC; and Raleigh, NC. The problem of practice the project will address is remedying the lack of emphasis current CS curricula place on microelectronics by enhancing NSF-developed computer science (CS) curriculum to include microelectronics topics and theoretical underpinnings. The curriculum will demonstrate how the analog world we live in is encoded to allow digital processing to be used, and how analog and digital systems coexist all around us to create the very powerful embedded systems used in society today. In addition, the limited range of activities that can be supported by BirdBrain Technologies’ Hummingbird kit (used by many schools to teach coding through robotics in various coding languages), will be addressed by adding a wide range of additional analog and digital sensors, output devices, and actuators. The increased range of sensors and output devices will facilitate the creation of a much wider range of computer control and robotic assignments that can be embedded in the curriculum to stimulate student interest in CS. Overall, the project will help students and the CS education community to develop the interest and motivation to engage in microelectronics and highlight the role of microprocessors and microelectronics in society, and undertake a deep dive into how microprocessors are used in both embedded and traditional computing. 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 · 2025-04

PROJECT SUMMARY / ABSTRACT Our NIH R25 Research Education project’s main goal is to develop a continuous team-based medical device design process incorporating clinical, business, and state-of-the-art features via the enhancement of Hofstra University’s Bioengineering (BE) Program educational activities, culminating in an improved Bioengineering Capstone / Senior Design course experience. Through this approach, BE undergraduates will gain increased knowledge, competency, practice, and modern skills that are vital to meet workforce demands in biomedical and healthcare technology industries. This innovative three-phase design process: 1) Device conceptualization, 2) Performance evaluation using finite element analysis (FEA), and 3) Further development in the Senior Design course, extends the traditional one-semester capstone into a multi-year experience that allows students to engage in prototyping, translational, regulatory, and business aspects of more mature device concepts. The project’s Specific Aims are: 1) Offer real-world perspectives to design ideas, and 2) Strengthen the design process education with FEA support. These aims will be accomplished through a collaborative summer clinical immersion program for identification of critical patient needs and generation of device concepts early on; structured mentorship from professionals in clinical practice, regulation, and business; integration of Senior Design work into multiple BE team-based design activities; extracurricular FEA workshops to evaluate and iterate device ideas through computer simulation; and a website for storing and sharing these educational materials. Student participants will be selected through a merit-based process consistent with program requirements. Program effectiveness will be evaluated over the project’s five-year period using consistent assessment instruments and objective rubrics to support rigorous and reproducible conclusions.

NSF Awards · FY 2025 · 2025-01

Cornell Tech, in collaboration with Hofstra University and the University of Illinois, seeks to expand its proven Break Through Tech AI Program designed to equip all undergraduate students with the skills needed to thrive in the fast-evolving fields of artificial intelligence (AI) and machine learning (ML). By expanding participation in high-quality AI education through a network of Instructional Hubs, this project aims to double the number of students served annually. This new generation of AI leaders will help ensure advances in responsible AI and promote US competitiveness in this exploding technical field. This project focuses on scaling up the ML Foundations component of the Break Through Tech AI program. This nine-week, skills-based training course is delivered by faculty and graduate students from newly established Instructional Hubs at various institutions. The expansion will involve recruiting five new Instructional Hubs, training instructors through a “Train the Trainer” program, and delivering synchronous lab sessions to ensure students gain practical, industry-relevant skills. By the end of the three-year grant period, the program aims to serve 1,500 students annually, significantly enhancing the readiness of the STEM workforce. This project will contribute to the field by providing a scalable model for AI/ML education and generating valuable data on the effectiveness of distributed instructional hubs in expanding participation in cutting-edge AI education. 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 · 2024-12

PROJECT SUMMARY One of the most detrimental afflictions of the human condition is the inability to create or recall memories. As our population ages and the prevalence of neurodegenerative diseases increases, the burden of memory impairment is expected to surge. Hence, it is critical to develop novel interventions that improve memory performance. This project seeks to determine the mechanisms of human memory brain network activity and develop biomarker- guided, closed-loop electrical brain stimulation (EBS) that can be utilized to correct pathologic brain states and restore successful memory function. Specific patterns of neural activity across the brain, especially fluctuations in the theta (4-8Hz) and ripple (80-140Hz) frequencies, are correlated with successful human memory encoding and retrieval. We propose to further elucidate these brain dynamics and implement EBS guided by neurophysiology to improve memory function. The key innovations of this proposal are the application of direct, closed-loop EBS in awake humans that is precisely timed to endogenous memory network activity, and a focus on investigating and harnessing local electrophysiological rhythms in the theta and ripple frequencies for optimizing EBS. We will take advantage of our high-volume epilepsy surgical center specializing in stereoencephalography electrode implantation in neurosurgical patients. This represents a unique opportunity to record high-resolution, in-vivo electrophysiological recordings of human memory brain networks and apply EBS with high spatial and temporal precision. To succeed in this proposal, we will also draw upon our experience in utilizing open- and closed-loop EBS for the modulation of brain networks and our expertise in advanced analytic techniques. In Aim 1, we will elucidate the spatiotemporal properties of theta and ripple activity in memory encoding and recall that underlie successful episodic memory performance. In Aim 2, we will assess whether EBS for augmenting human memory function can be optimized by targeting endogenous theta memory network activity during memory encoding. This project is thoroughly integrated with a training plan that will provide the PI with a strong foundation for a successful physician-scientist career with a long-term goal of developing biomarker-based paradigms for neuromodulation modalities to optimize and expand treatment for neurological and neuropsychiatric applications.

NSF Awards · FY 2024 · 2024-07

Periodic coastal hypoxia (low dissolved oxygen in the water and sediments) has major ecological consequences, including some negative effects on marine fauna and ecosystem services. Worldwide, coastal hypoxia is caused by both natural and human human-induced environmental change. Higher summer temperatures and increased inputs of nitrogen pollutants exacerbate microbial processes that consume dissolved oxygen, increasing the frequency and severity of hypoxia events. Biotic and abiotic interactions within these microbial communities are rarely studied simultaneously, which limits our understanding of the feedbacks between microbial processes and hypoxia. This project will investigate the relationships among a broad spectrum of planktonic microorganisms (bacteria, archaea, protists and microscopic animals) and abiotic variables (e.g., temperature, oxygen, N compounds) in the waters of an estuarine embayment in Long Island Sound, NY, that suffers hypoxia every summer. The project will quantify communities, metabolism, and predator-prey interaction across the microbial community. Results will inform scientists, environmental partners, and the public about the root causes of hypoxia, at a time when coastal hypoxia is increasing. The project has a strong educational component, including research experiences for high-school and undergraduate students. The students will be trained in microscopy, DNA sequencing, and bioinformatics. Training activities will increase participation of underrepresented minorities and will focus on marketable skills that empower and help retain individuals in higher education and STEM jobs. The overarching goal of this project is to launch a multi-annual research program investigating interactions between microbial plankton and deoxygenation in human-impacted coastal waters. The project will fill a key knowledge gap by simultaneously examining communities of bacteria, archaea, and microbial eukaryotes. Microbial processes lead to, and are influenced by, coastal hypoxia. This project will use the summer progression of deoxygenation in a eutrophic estuarine embayment as a “natural laboratory” to quantify changes in microbial community structure and functioning in hypoxia versus normoxia. Methods will combine field work, cell counts (microscopy, flow cytometry), DNA sequencing (metabarcoding, shotgun metagenomics) and experimental quantifications of bacterioplankton and phytoplankton consumption by microeukaryotes. Environmental data, microbial abundances, taxonomic and functional genetic profiles, and prey consumption rates will be used to test hypotheses on changes in feeding interactions and metabolic potential as summer progresses. Results will improve the understanding of microbial effects on (and responses to) hypoxia, and will help predicting trophic and biogeochemical shifts under current and future dissolved oxygen levels in coastal waters. 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 2023 · 2023-09

PROJECT SUMMARY Magnetic resonance imaging (MRI) is a versatile imaging modality that suffers from slow acquisition times, which is a challenge, for both time sensitive applications and for patient throughput. Accelerating MRI would benefit patients both by reducing the time they need to be in the scanner and in reducing the cost of healthcare. This project is part of a larger scientific effort to accelerate MRI while maintaining the diagnostic quality. Acceleration, even by a factor of two, would result in a major advance for public health. Two of the current approaches to accelerate MRI rely on collecting less data (under-sampling) and deep learning reconstruction. These approaches can lead to images with diagnostic quality using significant under-sampling but may suffer from artifacts which are hard to characterize and may sometimes resemble anatomy. Specifically, this project will optimize the performance of accelerated MRI, including undersampling patterns and deep learning reconstructions, on detecting and localizing subtle lesions. To carry out this optimization, we will first develop the methods required for detection of lesions by machine and human observer models. The human observer models will be validated by psychophysical studies where humans perform the detection task. In the first aim of this project, we will apply and develop detection tasks and model observers. We will consider under-sampled acquisition strategies in MRI including one and two-dimensional subsampling methods using deep learning reconstructions which enforce data consistency. We will develop detection tasks for signals in anatomical backgrounds were the signal location is known and when the observer needs to search for the signal. The human and machine performance in these tasks will be modeled. In the second aim, we will optimize data acquisition and neural network reconstruction using signal detection with observer models and psychophysical experiments. We will also introduce a detectability-based loss function to neural network reconstructions. There is recent interest in exploring the benefits of low/mid field MRI which has a trade-off with higher noise. In the third aim, we will evaluate the effect of field strength on signal detection. We will use data from high field acquisitions from a publicly available database to model images from lower magnetic fields. Using the detection of subtle lesions, we will evaluate detection performance with varying field strength. This research project will help to strengthen the research environment and broaden participation at Manhattan College by involving students in biomedical research incorporating applied mathematics, statistics and data science.

NIH Research Projects · FY 2025 · 2022-08

Abstract Coronary heart disease patients do not always have a saphenous vein suitable for an arterial bypass graft. Despite advances in vascular replacement and repair, fabricating small-diameter vascular grafts with low immunogenicity that are capable of host tissue remodeling remains a challenge. These important clinical needs will be met by investigating heat treatment parameters for decellularized plant-based scaffolds. Recent advancements in plant decellularization have produced cost-efficient cellulose scaffolds providing promising alternatives for skeletal, cardiac and bone tissue engineering. We were the first to develop robust, endothelialized vascular grafts from decellularized leatherleaf viburnum that matched mechanical properties of native blood vessels. Our long-term goal is to tissue engineer and test a patent, non-thrombogenic vessel for engraftment that mimics mechanical and structural properties of native vessels. By evaluating a range of heat treatment techniques, we will improve biodegradation and reduce immunogenicity of our plant-based vascular grafts and offer new methodologies to expand its applications within this quickly growing plant decellularization field. We hypothesize that heat-treated plant-derived scaffolds will recapitulate the robust multi-layer structure of native vessels and enable host tissue remodeling, with greater patency and resistance to thrombosis in vivo. We have developed a highly innovative heat treatment disrupting lignin and hemicellulose in the cell wall, as well as intermolecular ester bonds. Our Specific Aims are: 1) optimize plant-based vascular grafts using heat treatment to degrade the scaffolds and inactivate harmful lectin proteins and 2) validate grafts in vivo to compare their patency, tissue integration and biodegradation to a field standard (PTFE). An immediate goal is to remove foreign DNA from the decellularized plant scaffold but maintain cellulose’s mechanical properties. After graft recellularization of the endothelial and smooth muscle cells, cyclic application of fluid shear stress will be applied to pre-condition the construct for implantation and validation in an animal model. Our preliminary tests providing heat treatment of plant leaves in alkaline solution, combined with decellularization, improved decellularization efficiency, degradation, and elastic modulus. Thus, we predict providing a key method for decellularizing plants and pre-conditioning cells in a natural scaffold capable of successful engraftment. Importantly, our proposed project will also enhance the research environment at Hofstra University by allowing undergraduate students to plan, execute and perform analysis of authentic hands-on research that serves a critical need in public health. The PI and his research team will provide a diverse group of undergraduate students with a closely mentored biomedical research experience and projects that they can work on independently. Students will prepare articles for journals and present at appropriate scientific conferences. This will allow students to acquire a broad range of skills in biomedical engineering that they would otherwise not have access to and is expected to have a significant impact on their future studies and career choices.