Northeastern University

NSF Awards · FY 2025 · 2025-01

Filters tradeoff accuracy for space and occasionally return false positive matches with a bounded error. Filters are extensively used to compactly represent large datasets in fast memory (RAM) and avoid unnecessary I/Os across databases, storage systems, computational biology, cybersecurity, and networks. Yet modern data-intensive applications are severely bottlenecked by the limitations in filters. A fundamental limitation of traditional filters is that they do not change their representation upon seeing a false positive match. Therefore, the maximum false positive rate is only guaranteed for a single query, not a stream of queries. If users can adapt after seeing false positive matches, they can improve the filter performance for a stream of queries (especially skewed distributions). This project focuses on two goals. First, to design a high-performance, space-efficient, and practical adaptive filter with strong adaptivity guarantees, which means that the performance and false-positive probability guarantees continue to hold even for adversarial workloads. Second, to do a deep dive into various performance trade-offs in applications and integrate the adaptive filter in databases, cybersecurity applications, and computational biology tools. The effort redesigns existing applications and develops new software tools to establish appropriate trade-offs and achieve high performance and space efficiency. This project has the following top-level approach: develop the theory and an accompanying data structure library for strong adaptive filters under various real-world workloads involving deletions and updates, resizing, and merging two adaptive filters. It demonstrates the impact of adaptive filters in the real world by integrating the adaptive filter into applications and achieving massive speed-ups and robust performance for skewed and adversarial workloads. This project will enhance the capability of applications across databases, computational biology, and cybersecurity to achieve higher and stronger performance guarantees. Both accelerated computation (allowing quicker feedback and more experiments) and more extensive computation potentially accelerate the process of scientific discovery. Furthermore, this project places a strong emphasis on combining theory and practice. In addition, the research team will also develop teaching material on adaptive data structures and their usage in modern data-intensive applications and make it freely available online. 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

This project approaches the question of higher performance and better energy efficiency in electronic chip design with two key insights from biological systems: non-Boolean information encoding (analog processing in brain), and co-localized memory and computation (as in brain synapses). The specific objective of this project is to create a design framework for efficient information processing with intrinsic non-binary representations and in-memory memory and computation. If successful, this project can shed light on the fundamental role of information encoding and its physical implementation in determining system energy efficiency, as well as provide practical design automation methodology to infuse computation and learning into the analog/mixed-signal (AMS) domain before the digitalization step. Apart from its technological impacts, the integrated educational plan of this project is to empower students from all backgrounds with interdisciplinary experience and to cultivate a community of lifelong learners with social awareness. The project will enable joint optimization of circuit, architecture, and algorithm in a seamless manner across wide-range of applications including in-memory computing (IMC) and near-sensor processing (NSP), and consists of three major research thrusts: (1) to advance AMS design automation, novel neural network-inspired model abstraction, and hardware substrate will be developed to enable a streamlined design flow that uses AMS circuits as building blocks for information processing; (2) to support flexible and efficient in-memory computing architecture, this project will build intelligent and malleable peripheral interfaces and compilation framework by leveraging the AMS design methodology developed earlier; (3) to address the energy efficiency challenge in resource-constrained sensor systems, it will explore a context-aware analog-to-information frontend design by developing efficient near-sensor processing with multiple signal channels and multiple sensing modalities. These will serve as building blocks towards understanding the holistic interactions and design trade-offs of performance, efficiency, safety, and security in heterogeneous 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 2025 · 2025-01

Computational complexity theory studies the limits of what can be computed and how much time or other resources are required to do so. Randomness plays a key role in this theory because many algorithms that use random choices can solve problems faster and more efficiently than those that do not. In cryptography, which is based on complexity theory, randomness is used to secure data and protect privacy. The study of randomness aims to ultimately improve the performance and security of everyday technology. The objective of this project is to advance the understanding of randomness in computation, with the aim of making progress on long-standing open problems. Specific areas of investigation include pseudorandom generators, which are deterministic procedures that stretch a short random seed into a much longer sequence that "looks random," the complexity of sampling distributions, and the study of mixing of distributions over mathematical structures known as groups. The investigator will foster cross-fertilization between mathematics and computer science. He will also develop publicly-available educational material, such as a book on computational complexity, and lecture notes, surveys, slides, and videos, both at the advanced and introductory levels. In more detail, the investigator will work on extensions of small-bias generators, any of which can solve central open questions about pseudorandom generators. Recent work first used invariant theory to construct generators for low-degree polynomials over large fields, in fact achieving optimal parameters. The investigator will further develop this technique, with the goal of obtaining comparable pseudorandom generators over small fields, which would solve a long-standing problem in circuit complexity. The study of computational lower bounds for sampling has seen substantial progress in the last fifteen years. The investigator will further develop this area and its applications to data structures and error-correcting codes. The investigator aims to use this angle to make progress on the dictionary problem, a fundamental open problem in data structures. The study of mixing in groups has applications in communication complexity and cryptography. A recent theme has been the study of interleaved sequences of group elements. The investigator will further study interleaved mixing. A concrete aim is to resolve whether computing interleaved products requires large communication even for communication protocols involving many parties, which has been conjectured. Another aim is to use interleaved mixing to provide new separations between deterministic and randomized communication complexity. The proposed research can have an impact on a number of different areas in theoretical computer science and mathematics. Also, the investigator will continue to do research working closely with students at all levels. 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

In recent years, significant efforts have been made to exploit erbium dopants in non-magnetic crystals for microwave-to-optical photon transduction. While microwave-to-optical conversion has been experimentally demonstrated with a bandwidth as large as 1 MHz, the conversion efficiency falls short of theoretical predictions. The primary limitation lies in the weak coupling of the erbium dopant to the microwave. However, a recent theoretical work suggests that the erbium-microwave coupling can be significantly improved by doping erbium ions into yttrium iron garnet; the magnons in this doped garnet can effectively mediate the coupling, thereby increasing the transduction efficiency by three orders of magnitude. This project aims to develop thin films of erbium-doped yttrium iron garnets and experimentally demonstrate the theoretically predicted efficient transduction. If successful, this project will lead to a new material for efficient quantum transduction. Such transduction materials can be used to entangle distant superconducting qubits and link superconducting quantum systems to other quantum computing platforms. This advancement will significantly advance the fields of quantum computing, quantum communication, and quantum networks. Two undergraduate students and two graduate students per year will participate in film growth, device fabrication, magnetic, optical, and microwave measurements, microwave-to-optical conversion experiments, and data analysis. Outreach to K-12 students will include laboratory tours and mentoring high school students for summer research. The project consists of three main thrusts. The first thrust is dedicated to using magnetron sputtering to grow erbium-doped yttrium iron garnet thin films. To optimize the erbium doping level efficiently and timely, a composition-spread technique will be employed to ensure high throughput fabrication of the films. The second thrust focuses on the comprehensive characterization of the structural, magnetic, and optical properties of the thin films fabricated in the first thrust. This characterization will particularly include the use of extensive ferromagnetic resonance measurements to determine magnetic damping constants and magnon decay rates, as well as the use of optical measurements to determine optical transition linewidths over a wide temperature range from 5 K to 300 K. These parameters are critical for transduction experiments in the third thrust. Under the third thrust, integrated devices will be fabricated that consist of erbium-doped yttrium iron garnet thin film strips, high-quality superconducting resonators, and connections to optical fibers. These devices will be used to study microwave-magnon coupling and microwave-to-optical photon conversion at a temperature of 2 K 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

According to the Occupational Outlook Handbook, employment opportunities in computing are projected to grow much faster than all other disciplines. The goal of this project, a collaboration between the Center for Inclusive Computing and 10 large public universities located across eight different states, is to implement a series of Interdisciplinary Computing Majors (ICMs) in such a way that students who are new to computing, and did not enter university as computing majors, are attracted to discover and persist in their chosen ICM. The expected outcome of this project is an increase in the number of universities offering ICMs, and a more diverse pool of career-ready graduates who can satisfy the growing need for computing occupations in the United States. The project team will examine the results and impact of the implementation of the ICMs and document the outcomes in a set of case studies that explain the implementation plan and data for each school. Given recent advances in AI, an essential outcome of this project will be to determine how to best integrate AI and other disciplines. The project team will examine how universities can create ICMs in which the computing side of the curriculum includes an emphasis on learning and ethically applying AI to the other discipline. The ICM case studies, complete with data measuring the change in the demographics of declared majors, will provide a generalized road map of best practices for other universities interested in implementing interdisciplinary computing majors. This work will be guided by an advisory council that will meet with the project team once a quarter to review learnings, data collection and generalizations. 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

Commonly known as the “spotted wing Drosophila,” Drosophila suzukii has become one of the most captivating cases of rapid worldwide invasion. Originally from southeast Asia, this fruit fly was first recorded in California in 2008 and has since spread to 48 of 50 states in the US, with parallel expansions occurring in Europe. In addition to its intriguing invasion biology, D. suzukii is a significant pest that causes up to $500 million in annual losses to US agricultural efforts. The proposed work will create long-term collaborations with local farmers and naturalists across Vermont and Kentucky to understand the genetic, physiological, and ecological underpinnings of D. suzukii’s success as an invasive species in the context of a rapidly changing world. Of particular interest to our work is D. suzukii’s capability to develop into summer-specialized and winter-specialized “morphs,” a physiological feature that plays a key role in their success and hardiness as invaders. We will combine physiological experiments, genomics, and computer simulations to predict how these traits will evolve under various climate change projections. We will focus on the capacity of the fly to expand its habitat into northern latitudes as colder winters, an ecological delimiter for D. suzukii, continue to weaken due to climate change. We will also develop a summer science module for K-12 students focused on horticulture, invasive species, and climate change, and lesson plans from these modules will be published in peer-reviewed science education journals. The project will train multiple undergraduate interns, two graduate students, and a postdoc. Global climate change has introduced novel stressors to many habitats, and it is unclear which species may emerge as winners or losers in a changing world. To date, most efforts in climate change biology have focused on traits important for coping with extreme heat events. Yet, winter temperatures are warming twice as fast as summer temperatures in North America, and the evolutionary consequences of more heterogeneous winters remain understudied. This is a critical knowledge gap given that species distributions are often limited by minimum winter temperatures. Quantifying factors that shape winter biology is critical for predicting organismal responses to changing climates. This proposal investigates the relative contributions of plasticity, local adaptation, and seasonal adaptive tracking in fine-tuning key overwintering traits in D. suzukii. We will use flies from Vermont and Kentucky to quantify genetic variation in cold tolerance, overwintering survival, and post-winter reproduction, as well as the reaction norms of these traits in summer/winter seasonal morphs. We will use whole genome resequencing to determine whether D. suzukii has persistent overwintering populations and the extent to which genetic structure is shaped by adaptive tracking. Lastly, we will create a novel set of simulations to explore how plasticity, local adaptation, and adaptive tracking evolve in a metapopulation that experiences fluctuating stressors. We will also incorporate projections into our simulations to predict how reaction norms for overwintering traits evolve in response to climate change. This project is jointly funded by Integrative Ecological Physiology (IOS/IEP) and the Established Program to Stimulate Competitive Research (EPSCoR). 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 · 2024-11

ABSTRACT Infections by Balamuthia mandrillaris cause granulomatous amoebic encephalitis (GAE) with a >92% fatality rate. Current treatments lack strong scientific support, leading to limited options and poor resolution of infections. Classified as a category B NIAID emerging infectious pathogen, B. mandrillaris faces obstacles in therapeutic development due to poor understanding of the pathophysiology, absence of validated targets, and low industrial interest. The project team combines expertise in amoeba biology from Dr. Christopher A. Rice at Purdue University, medicinal chemistry from Dr. Lori Ferrins, and molecular modeling from Dr. Mary Jo Ondrechen, both at Northeastern University. Previous research by this project team identified two compounds, Omipalisib and PKIS40, known human PI3K inhibitors, with activity better than currently recommended therapeutics against B. mandrillaris by the CDC. The project team has extensive experience re-optimizing human kinase inhibitors for parasitic diseases, and possess the complementary skills to position them to drive this program to success. Aim 1 is focused first on understanding the SAR and SPR. Using commercially available compound libraries we will enumerate compounds that can be made, and will prioritize compounds for synthesis using molecular docking (the Ondrechen lab has already built and evaluated a homology model). Compounds that demonstrate they can adopt a viable binding mode will be synthesized, and then evaluated for in vitro activity in Aim 2. This will occur simultaneously for both Omipalisib and PKIS40 chemotypes which were chosen because they are: 1) low micromolar inhibitors of B. mandrillaris which is better than the current chemotherapeutics; 2) there is a good selectivity index versus mammalian cells; and 3) there is a wealth of information in the literature that can be used to help inform optimization. Aim 2 will focus on the evaluation of all compounds synthesized in Aim 1 for their on-target activity against BmPI3K, and phenotypic evaluation against the trophozoites. At the conclusion of the project, the compounds that meet the Target Candidate Criteria, laid out in the proposal, will be prioritized for progression through secondary cysticidal and cytopathogenicity screening assays, pharmacokinetic, and a small in vivo proof-of- concept efficacy study with the top 2 lead candidates (although no funding is requested for this work). The development of new drug(s) would have a significant impact on the treatment of the orphan diseases caused by B. mandrillaris and this is the first-in-class project that seeks to optimize PI3K inhibitors for GAE using both phenotypic and target-based approaches. By the end of this project, we will have identified several compounds that are suitable to enter future secondary biological assays, and in vivo proof-of-concept studies against B. mandrillaris, and we will have chemically validated a novel target for this fatal disease.

NSF Awards · FY 2024 · 2024-11

The ability to reflect is a lifelong learning skill necessary for success within and beyond the classroom. This project seeks to advance people's skills around reflection through using AI-supported prompts to deepen their critical thinking, using problem/puzzle solving as the concrete learning task. Furthermore, the team aims to advance knowledge of how recent advances in AI can be used to foster reflective learning "just-in-time" and "on-the-job" in different interaction contexts: while working behind a computer, in virtual spaces, or physical spaces. The methods are intended to be adaptable to a number of contexts, including education (digital learning environments), the workplace (digital training), gamified simulations (such as escape rooms or virtual scenarios for training), and self-understanding in everyday life (e.g., personal health or fitness apps). Through this work, the project will make significant advances around supporting reflection, with the potential to develop self-guided educational experiences that may be especially useful for populations underserved by existing educational opportunities. The project team will create a study environment in the form of an escape room that poses problem-solving tasks, using three different interaction contexts: a 3D digital screen-based version, a virtual reality simulation, and an augmented reality physical format. The team will build and validate an AI-empowered reflective learning tool that seeks to deepen and accelerate learning with progressively more complex and expansive capabilities, including using large language models to give feedback and prompts for reflection, data-driven detection of learners' behaviors and emotions, and data-driven detection of the learner's environment. The researchers will run in-person and online user studies to examine when and how reflection should take place across the three virtuality contexts, with the goals of optimizing the timing and style of reflection to deepen learners' understanding of problems, accelerate their ability to find solutions to problems, and increase their engagement in the learning environment while reflecting. The result of this project is the creation of a flexible AI-empowered reflective learning tool that can be implemented in many forms of modern technologically enhanced learning and training environments. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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 · 2024-10

Networked embedded and Internet of things (IoT) systems are essential to everyday life and predicted to reach one trillion systems by 2035. These systems power a variety of embedded and IoT devices, such as sensors, medical devices, wearables, smart family gadgets, industrial computing units, autonomous vehicles, and infotainment systems. While the benefits of these systems are unparalleled, they are susceptible to cyberattacks, which are occurring at unprecedented levels and often have severe consequences ranging from loss of life to homeland security breaches. To ensure our IoT infrastructure and ecosystem are built on a trustworthy and secure foundation, this project's novelties are to expand knowledge in pursuit of trustworthy and deployable solutions encompassing the hardware and software layers of computer systems. The project's broader significance and importance, beyond securing the IoT infrastructure, are to train the next generation of cybersecurity researchers, educators, and practitioners with deep theoretical understandings and practical skills in this field. Trusted Execution Environments (TEE), an enabling technology for the confidential computing paradigm, are offered in Central Processing Units (CPUs) as a foundational primitive for security to keep code and data loaded inside computer systems protected. The hardware and software layers of existing TEEs nevertheless have been criticized for lack of transparency and presence of vulnerabilities. This project studies a systematic research approach to increase the trustworthiness and deployability of TEEs and TEE-based security solutions for embedded and IoT devices. Specifically, the project advances the frontiers of knowledge in (1) designing trustworthy TEE hardware paradigms with a minimal Trusting Computing Base (TCB); (2) discovering and fixing confused deputy vulnerabilities of TEE software; and (3) developing new security solutions that utilize TEEs and other hardware units for better protection and superior performance. The education thrust advances the state of knowledge in IoT software and system security education pedagogy and platform. 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 · 2024-10

ENG-QUANT: Metamaterial-enabled superconducting nanowire detectors for high temperature operation Single-photon detectors are at the core of modern strategic quantum technologies. Superconducting nanowire single-photon detectors offer outstanding, unbeaten counting performances at near-infrared; however, their sub-Kelvin operation requires bulky and expensive cryostat, hindering their widespread field deployment and representing one of the obstacles to the large-scale diffusion and democratization of photonic quantum technologies. To solve this bottleneck, this proposal aims to increase the operational temperature of these detectors by exploring superconducting, optical, and thermal metamaterials combined in novel device architectures while using traditional superconducting compounds available for large-scale fabrication. The proposed research, if successful, will not only result in nanowire single-photon detectors operating a ~10 Kelvin for near-to-mid-infrared wavelengths but extend superconducting technology's applicability, making it accessible to more researchers and broadening the scope of quantum research and applications beyond the elite confines of current technology. This research project will train one graduate student in the wide area of quantum hardware, provide opportunities for the early involvement of undergraduate researchers, specifically from underrepresented groups, and will have a significant educational impact, with the development of teaching modules on several topics. The approach of this project is to develop high-temperature, highly efficient near-to-mid-infrared nanowire detectors through the integration of metamaterials. The theory of single-photon detection in nanowires suggests that efficient detection can be achieved at high temperatures if nanowires can reach the true superconducting depairing state. The proposed research specifically targets this objective and supports high-temperature detection by developing and integrating three nanostructured metamaterials. (1) A superconducting metamaterial capable of reaching the superconducting depairing limit at high temperature, based on nanowires fabricated from optimized high critical temperature type-II thin-film, featuring topography tuning for homogenous switching currents, vortex engineering for controlled pinning and reduced entry, and fabrication process optimization for reduced roughness. (2) An optical metamaterial to support high external efficiency based on nanowire-integrated plasmonic nanostructures metamaterials. (3) A thermal metamaterial to foster high-temperature detection capabilities by enhancing high-energy down-converted phonons injection and recovery. Finally, the three metamaterials will be integrated into a single architecture to demonstrate high-efficiency single-photon detectors operating at high temperatures. Beyond quantum technologies, the successful completion of the project will impact various other applications, from astronomy to biomedical imaging. 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 · 2024-10

Cyber attacks have become increasingly more sophisticated, coordinated, and widespread over time. Automated machine learning (ML) techniques to proactively detect and prevent malicious activities are becoming popular, but these defenses are themselves susceptible to stronger attacks. Adversaries can compromise ML-based systems at both training time (poisoning attacks) and deployment time (evasion attacks). This project’s novelties are creating novel methods and tools that can be used to investigate real-world poisoning attacks and designing feasible mitigation techniques against these attacks. The project’s broader significance is to put forward recommendations and create software tools to help practitioners on the use of ML for preventing malicious activities on cyber networks. The project team has expertise in machine learning and cybersecurity, and plans a set of education tasks and outreach activities which include public release of course materials on ML security, mentoring undergraduate and graduate students in research projects, and collaboration with industry partners to transfer the developed technologies to practice. This project considers three interconnected thrusts addressing different aspects of understanding ML poisoning attacks and building defenses against malicious activities in cyber networks. The first two thrusts seek understanding on poisoning attacks against supervised learning, semi-supervised learning and unsupervised learning. The team will adopt techniques such as explanation-based ML methods and generative models for identifying stealthy poisoning attacks. The third thrust introduces novel poisoning-resilient machine learning defenses based on data sanitization and training ensemble models in order to achieve certified robustness of ML systems against poisoning attacks. These research thrusts constitute the foundation for creating and transitioning resilient AI/ML models to industry/DoD partners to enable protection of real-world cyber networks against advanced attacks. The team will work with its industry partners to help them to adopt the created techniques from the project for DoD 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 2024 · 2024-10

Non-technical Description: Advances in photonic materials that generate, process, or detect light can transform diverse areas of science and engineering, including lasers, optical fiber communications, augmented and virtual reality displays, solar energy harvesting, as well as quantum computing and sensing. By rationally engineering the composition and/or structure of materials at various length scales, it is possible to dramatically enhance their optical responses and performance. However, the traditional approach for the discovery and development of new photonic materials relies on trial-and-error and case-by-case explorations, which are often time consuming and ineffective. This project will use advanced artificial intelligence techniques to develop new artificial photonic materials that can be engineered to have prescribed properties and surpass naturally occurring materials. The research seamlessly integrates materials science, photonics, engineering, physics and artificial intelligence. In tandem with research, the team will develop a multi-channel education program to enhance the learning experience of a broad spectrum of the society, and prepare the next-generation workforce and technology leaders. Technical Description: The project aims to accelerate the pace of the discovery, design, and implementation of new engineered photonic materials, particularly photonic metamaterials, with user-defined spatial, spectral, linear, non-linear and quantum properties through a data-driven approach. This approach will consolidate properties of constituent material compositions, their geometric structures spanning atomic length scales to micrometers, and their underlying symmetries and topology. The project consists of three research thrusts, including (1) establishing deep learning frameworks to construct photonic metamaterials with high efficiency and accuracy; (2) integrating information on the tailorable optical properties of the constituent material platform into deep learning models, to benefit the design and development of reconfigurable metamaterials; and (3) investigating hybrid material systems that couple topological photonic structures designed by deep learning with quantum emitters and optical nonlinearities. The team will accomplish the interdisciplinary research by fusing theory, computation, deep learning, materials engineering, fabrication and experimentation in a closed-loop manner. Through the project, new fundamental knowledge and insights about the interdependent relationships among structure, properties, performance, and processing across different scales will be gained. In alignment with the Materials Genome Initiative (MGI), the project will create a comprehensive library of different artificial meta-atoms and meta-molecules and their optical responses, and eventually drive transformative applications of photonic metamaterials for classical and quantum information processing. 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 · 2024-10

Industrial knitting machines exist on factory floors worldwide and manufacture various textiles for a range of clothing and home goods. However, beyond common textile products, modern machine-knitting manufacturing equipment has the potential to produce complex interactive smart textiles with a variety of useful, innovative applications. For example, fabrics embedded with conductive yarns can be used to make clothes with health sensors. Imagine a sweater that can measure the heart rate of a patient with heart disease and be washed and handled like any other sweater. Such sensors are not produced because the software used to design these smart textiles and control these knitting machines is limited and too complex for many garment designers. This issue makes the production of smart textiles too expensive or complex to put into practice. This project will investigate the user-experience needs of garment designers and hand-knitters for machine-knitting software to help them create these complex smart textiles. This project will promote science by advancing knowledge of the machine knitting design process and how it can be integrated with existing machine knitting tools to produce novel smart textile materials. This advancement will support national health and well-being by providing a new way of manufacturing smart textiles for various applications such as healthcare, sustainable and local, within the United States, manufacturing of clothing. This project will enable further development of machine-knit materials and the design of smart textile garments. Success will be measured by three outcomes: (1) a comprehensive examination of the design space of machine knitting, (2) the development of two software systems, KnitScript for programmers and KnitCAD for designers, to support the design of machine-knitted smart textiles, and (3) the creation of interactive, health-sensing garments. To engage with stakeholders such as programmers, machine knitting experts, hand-knitters, and textile designers, the research will employ various human-centered methods, including interviews, artifact collection, participatory design, and design probes. System development will draw on concepts from software engineering, programming language design, and interaction design for design tools. 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 · 2024-10

Activities such as puzzles and competitive sports bring immense joy and meaningful experiences that can offer numerous cognitive, physical, and mental well-being benefits. However, a significant portion of 50+ year-old players face barriers that exclude them from engaging in these activities in the digital domain, whether due to technological inaccessibility, representational issues, or content mismatches. People over 50 are often unable to access the benefits of such activities designed for the digital domain. This oversight represents a significant missed opportunity, economically, culturally, and socially. This project will focus on the responsible design, development, and deployment of artificial intelligence (AI) technologies to address inequitable access to such services and resources for social connection and personal growth for older adults. A tool will be designed to empower 50+ year-old players to engage in digital activities that might not be designed with them in mind or that are created by developers lacking the expertise or resources to support older adults through robust age-friendly features and optimizations. The approach will leverage user-centered and participatory research, which emphasizes the collaboration with 50+ year-old users and industry stakeholders. Research will address accessibility features, activity mechanics, interface design, and social interaction functionalities catered to an aging user base and will aim to automate and augment such elements using cutting-edge AI implementation. The Phase 1 project will build collaborations among key partners and establish design guidelines for responsible, ethical development and deployment of the tool. Objectives include a literature review of 50+ year-old users and AI tools for accessibility, a national open-ended exploratory survey followed by a confirmatory survey, workshop sessions for idea generation and co-design with users, and integration of findings in a comprehensive design document and established community of key stakeholders. Together, these efforts form the foundation of a player-centered AI tool that will make digital activities more accessible for older adults. 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 · 2024-10

When solutions or solutions that contain particles are frozen on Earth, particle settling and liquid motion due to gravity affect the structure of the resulting solid. By freezing these solutions in microgravity condition, the subtle forces involved in growing the solvent crystals and forming particle-assemblies between those crystals will be revealed. The experimental setup on board the ISS will provide an ideal environment for imaging crystal growth and particle motion under strictly controlled conditions. Comparing the structural features obtained on the ISS with those on the Earth will reveal the mechanisms of structure formation that are obscured by gravity. Combined with computational models, the proposed research will help designing new materials as well as improving both the structure and properties of existing ones for applications in biomedicine, catalysis, water purification, and energy generation and storage. This award is ideally suited for the integration of research and teaching in STEM educational programs that incorporate space themes to increase interest and diversity, and to improve skills in K-12 STEM education. The fundamental knowledge gained by performing structure formation studies on the ISS and Earth will be of interest to both the freeze casting and the directional solidification communities. To date, only few short duration (25 seconds on a parabolic flight) microgravity freeze casting experiments have been performed. This study is the first to analyze and quantify complex dynamics and interactions of directional crystal growth and particle self-assembly, in the presence and the absence of gravitational forces, and with an externally applied magnetic field. The complementary set of experimental and simulation results will enable a more systematic exploration of currently unpopulated spaces in material structure and property. Two complementary approaches will be pursued: i) ex situ and in situ observations and quantification of the freeze casting process, and analysis of the morphology of ice-templated materials manufactured in microgravity and in terrestrial under well-defined and controlled conditions; and ii) the development of simulation techniques using the experimentally determined input data to enhance the predictive capability of freeze-casting models for fabrication of critical materials in Space and on Earth. The new experimentally validated, model-based tools will enable the science-based design and manufacture of new materials. 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 · 2024-10

To provide essential services, anchor institutions (which include public libraries, schools, and hospitals) increasingly rely on high quality broadband Internet connections that enable important services such as educational software, videoconferencing, and telehealth. However, little is currently known about whether the quality of such anchor institutional networks (AINs) meets their needs. This project will fill this gap by conducting rigorous measurements to identify locations, quality, and opportunities for improving AINs. Such information can help governmental and private efforts to improve the ability of anchor institutions to serve the technological needs of the general public. The intellectual merits of this project fall in three related categories on computer network availability, reliability, and performance. First, on network availability, the work will create an annotated map of AINs, including the providers that they use to connect to the Internet. Second, on network reliability, the project will collect evidence to assess how reliable AINs are, for example, how often they experience outages, using a mix of existing and novel methods in Internet measurement. Third, on network performance, this research will determine whether AINs meet the technology needs of users who rely on them, for instance, whether the connection speed at the library is sufficiently high to support videoconferencing for all patrons who need it. This category requires significant advances in network performance characterization, particularly on determining (and measuring) the adequate bandwidth needs for a varied mix of networked applications. This collaborative project, which brings together researchers from Northeastern University, University of California-Davis, and University of California-Santa Barbara, has the potential for substantial broader impacts beyond its scientific advances. AINs are often the last line of availability for many users from historically-marginalized communities, including school-age children in rural or tribal areas, who do not have reliable or adequate Internet service at home. Thus, adverse events affecting AINs (outages) or persistently inadequate connections (low performance) can lead to disproportionately negative impacts on these at-risk communities, including low-income neighborhoods in urban cores. By producing a comprehensive study that evaluates connectivity at anchor institutions, this project will facilitate broadband equity and access efforts from consumer advocates, Internet providers, and local, state, and federal governments. All code and non-sensitive datasets will be publicly released on this repository: https://github.com/anchor-institutions/anchor-institutional-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 2024 · 2024-10

Privacy is often perceived as an abstract concept by both internet users and software developers. When users are engaged in online activities, it is difficult for them to make informed decisions about their personal data due to the challenges they face in understanding and experiencing the privacy implications of their behaviors in advance. Similarly, many software developers lack the ability to comprehend how the data practices of their applications may impact user privacy and to implement proper data practices that conform to users’ privacy expectations. This project is tackling this problem by developing a new, empathy-based framework to enhance privacy education and design. The project team is using generative AI to create synthetic personas with AI-generated personal data. Using the personas, the team is designing, creating, and studying new interactive sandboxes and developer tools that allow individuals to empathize with these personas, leading to a more concrete and situated understanding of privacy. This understanding, in turn, fosters positive privacy-oriented behaviors among internet users and privacy-responsible software development practices among software developers. To enhance users’ privacy knowledge and developers’ privacy-responsible software development practices, the project is systematically studying the mechanisms and applications of empathy invocation in the context of privacy. The goal is to develop metrics, guidelines, and conceptual frameworks for empathy-based approaches that foster privacy and security in cyberspace. Using these findings, the project team is employing user-centered design methods to develop: 1) systems that invoke empathy to improve users’ privacy literacy and decision-making; and 2) empathy-based developer tools that support developers to proactively identify and address diverse privacy needs of users at the early stages of the development life cycle. These systems are deployed in outreach events to promote privacy literacy in under-resourced user and developer communities. Additionally, they are incorporated into college-level privacy literacy educational modules to support hands-on experiential learning. 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 · 2024-10

The goal of this research is to create artificial intelligence (AI)-enhanced digital tools to amplify freelance workers ability to work in online labor markets. This research is important because online freelancing -- in which professionals work on a collection of individual tasks, outside of traditional workplaces -- is rapidly becoming a significant component of modern labor markets. Online freelancers need to market themselves, find well-paying and skill-enhancing jobs, and be able to perform and get credit for quality work. However, online labor markets make it hard to do this at times because of their design: their policies are often more friendly to employers than to freelancers, while their algorithms for matching people with jobs and prices are often opaque to workers. The key idea of this project is that AI-enhanced digital tools may be able to help workers better-manage their profiles, workload, and task performance. To that end, the project team will work with online freelancers to develop and evaluate a number of prototypes that counter these problems. If successful, the research will both improve the specific problems of online freelancing work as well as provide an example of how AI-enabled tools, designed wisely, can complement rather than replace people in jobs. To achieve this goal, the research effort leverages human-centered design principles. Working with a carefully selected and steadily updated sample of online freelancers, data will be gathered through interviews and focus groups to identify and advance the functionality and needs of AI-enabled tools to support these workers. In doing this, the research effort leverages the investigators' ongoing work in building similar tools for crowd workers and insights from an ongoing panel study of online freelance workers. Over three years and through multiple design, deployment, and feedback cycles, the research team will collaborate with organizations dedicated to supporting online workers. Together, they will enhance the AI-enabled tools' functionality and design to address the needs of these workers. 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 · 2024-10

While the research on securing IoT software and systems has made significant progress in recent years, educational offerings in this area have not kept pace. This lag can be attributed to several factors, which include a lack of wide access to IoT software and the essential infrastructure needed to develop cybersecurity curricula and teaching materials, and a lack of active learning platforms for students such as IoT specific Capture-the-Flag (CTF) systems. Moreover, existing CTF platforms have many pedagogical, functional, and inclusiveness limitations. To mitigate current educational shortcomings, this project will design and host a next generation CTF platform. It will have profound broader impacts, including: (1) enhancing the education and training of the next generation of cybersecurity researchers in topics related to IoT software and systems security; (2) preparing a diverse group of educators and practitioners who will have deep theoretical understanding and practical skills in IoT software and systems security; and (3) involving Minority Serving Institutions in the project that will utilize the project's outcomes to enhance their cybersecurity curricula. This project will advance the state of knowledge in IoT software and systems security education pedagogy and platforms. The key intellectual merits include the following. (1) Student-centered pedagogy in software and systems security education that will involve students in designing CTF and defensive challenges, while tracking and supporting students' progress by automating the feedback process. (2) Inclusive pedagogy in software and systems security education. (3) Development of the PwnIoT.Academy, a next generation CTF platform that will support student-centered and inclusive pedagogy. (4) Development of IoT CTF and defensive challenges for different architectures and software platforms. (5) Collection of extensive data on student learning, which will enable a better understanding of the capabilities of the platform as well as identification of persisting challenges to cybersecurity education and the diversification of the workforce. This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. 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 · 2024-10

The proposed project aims to understand how aging affects people's ability to use emerging technologies for human-computer interaction, such as virtual reality (VR). As people age, their visual, spatial, and motor control abilities decline; this decline in visuospatial-motor (VSM) functions likely affects how well they can use VR and related technologies that involve immersive 3D environments. This decline could in turn reduce older adults' ability to reap the educational, social, health, and general well-being benefits that VR and related technologies can provide. The goal of this project is to link neuroscientific measures of brain activity with the use of VR-based 3D environments, building models that relate VSM abilities to the successful use of features of VR designs. These models will advance scientific understanding of brain function in virtual spaces and are intended to guide the design of future VR interfaces so that they are better able to adapt to variations in VSM ability associated with aging. The project will also support education and diversity by involving a multidisciplinary team from neuroscience, engineering, and computer science. The insights gained could inform the design of more accessible and inclusive HCI systems, benefiting a broader range of users across various demographics. The project proposes an Immersive Multimodal HCI (Immersive mHCI) framework to explore the underlying neural dynamics that connect age-related changes in visuospatial-motor (VSM) functions to the digital competence required for adapting to immersive 3D HCI environments. The research is structured around three key thrusts: Thrust 1 involves the design of a novel dual visuospatial-motor virtual reality-based interface (VSM-VRI) as an immersive 3D task environment. This interface will facilitate the multimodal characterization of the complex nonlinear dynamics underlying visuospatial and motor interactions, providing a realistic and challenging context for studying VSM functions. Thrust 2 focuses on developing novel nonlinear pattern recognition techniques and a graph-based learning framework. These tools will characterize and fuse the nonlinear dynamics of VSM neural interrelations as reflected in electrical and vascular-hemodynamic neural activities captured when experimental participants use the proposed 3D task environment. The goal is to create a comprehensive model that captures the intricate spatiotemporal neural patterns associated with VSM functions. Thrust 3 aims to develop and test statistical methods to evaluate the proposed VSM-VRI and graph-based computational frameworks. These methods will predict age-related VSM functionality changes and their effect on adaptation to emerging 3D HCI environments, compared to traditional 2D screen-based interactions. The project's outcomes will enhance understanding of VSM functions and inform the design of adaptive, inclusive HCI systems that cater to diverse user needs. 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 · 2024-10

The Internet of Things (IoT), encompassing devices such as medical equipment, autonomous vehicles, and industrial control units, is becoming integral to modern life and is expected to reach one trillion devices by 2035. Unfortunately, malware attacks on IoT systems are increasing rapidly, exemplified by incidents like the Mirai botnet and the Colonial pipeline attack. While significant research has explored malware detection for PCs and mobile devices, these methods are not suitable for IoT systems due to their diverse operating systems and low power. Current models also struggle against sophisticated attacks that aim to evade detection. To address these challenges, the project team is developing DANGER-IoT, an approach to IoT malware detection that works across heterogeneous platforms, is efficient for low-power devices, and robust against advanced attacks. The researchers are collaborating with industry experts to ensure the project's ideas work well in real-world settings and are creating open-source tools and datasets. Spread across four universities and three countries, this project is also impacting a diverse group of students through new courses, security competitions, and international exchanges. The DANGER-IoT project focuses on developing advanced machine-learning models for IoT malware detection. The first goal is to create a generic model that can detect malware across heterogeneous IoT platforms by constructing a common embedding space for similar functions across different operating systems and architectures. The project's second aim is to ensure efficiency for low-power devices by applying model compression techniques adapted from explainable AI and model pruning. To enhance robustness, the project will explore large-language models for code-style transfer, making malware appear benign to existing classifiers, and using the results to design a novel moving-target defense. By integrating multi-task learning, behavior classification, and a comprehensive IoT malware dataset, DANGER-IoT aims to provide a scalable detection approach, robust defenses, and significant contributions to the community through shared data, benchmarks, and tools. 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 2025 · 2024-09

Abstract An estimated 31% of physicians and 54% of nurses experience professional burnout, at a cost of ~$4.6 billion annually, in part due to significant time burden, poor system resiliency, and process inefficiencies. Declared a national emergency by the U.S. Surgeon General’s Office – a staggering 76% of healthcare professionals re- ported exhaustion, burnout, and excessive work burden during the COVID-19 pandemic – burnout significantly affects care quality, medical error, occupational injury, workforce attrition, depression, and suicide contempla- tion. Work burden, personnel burnout, staff shortages, and strained resources are complex issues, deeply in- tertwined, and especially prevalent in practices serving disadvantaged populations, yet remain both signifi- cantly understudied, as a system, and increasingly common. The encouraging broad adoption of asynchronous care (patient portals, EHR email, text messaging) fortunately can significantly improve care and patient experi- ence, but also contribute significantly to time stresses, work-after-work, and burnout, further exacerbated by (and contributing to) staff shortages, inefficient processes, and non-resilient systems. It is not surprising that a National Academy of Medicine report (Taking Action Against Provider Burnout: A Sys- tems Approach) thus recommended greater use of systems engineering to optimize technology use workflows, reduce endemic inefficiencies, develop resilient processes, and apply complex adaptive systems ideas. This project directly responds to 4 of the 8 NAM report recommendations, uniquely incorporating systems science and systems engineering approaches to better understand and address the relative impacts of these issues by: (Aim 1) Understanding the nature, extent, issues, and exemplars of system resiliency, efficiency, and optimized workflows in asynchronous care processes; (Aim 2) Investigating relationships between work burden, system resiliency, inefficiency, and burnout and their impact on care quality and safety; and (Aim 3) Estimating the rel- ative impacts and generalizability to other settings of various types of interventions effective interventions. Notably, (1) analytic process simulation and system dynamics models will be integrated with other methods to help understand these issues, identify insights, estimate effect and relationship sizes, and evaluate potential interventions, and (2) system resiliency analysis and design methods will be applied to develop more robust adaptive processes – both hallmarks of systems engineering. This research will be conducted by a multidisci- plinary team of engineering, qualitative, and health service researchers working with 4 varied community health centers (in complexity, rurality, ethnicity) and validated through statewide CHC and primary care organizations for generalizability, since burnout affects some demographics more than others. Results will be evaluated through a combination of process, resiliency, burnout, and safety measures, with expected impacts including more efficient and resilient processes, reduced burden and burnout, and better care that, if scaled to 20% of primary care across the US, could benefit 209,000 practices serving 133 million patients.

NIH Research Projects · FY 2025 · 2024-09

Project Summary A robust enzyme design strategy will profoundly impact human health, such as by achieving asymmetric synthesis of pharmaceuticals not easy to be achieved by small-molecule catalysts. However, photoexcitation has rarely been considered in enzyme design. In recent years, it has been found that certain enzymes can be repurposed by photoexcitation for non-natural chemical reactions that cannot be easily achieved by small- molecule catalysts or traditionally engineered enzymes. These enzymes, termed photoenzymes, are believed to utilize photoactivatable cofactors, combined with a natural or mutated enzyme scaffold, to reach new reaction spaces. Photoenzymes have emerged as a promising new class of catalysts for non-natural reactions important in pharmaceutical synthesis, such as asymmetric radical reactions important in late-stage functionalization of drug-like molecules. However, the mechanisms of photoenzymes have not been studied well and there has not been a clear rational discovery and design strategy for photoenzymes, not only because these are emergent systems, but also because existing computational methods are not adequate. In this research program, we will develop an integrated computational framework to predict the combined effect of light, cofactor, substrate(s), and protein sequences on photoenzyme reactivity and the mechanisms that lead to this effect, and will develop a physics-informed design strategy that makes use of descriptors derived from both ground and excited electronic states to control the activity and selectivity of photoenzymatic reactions. This will fill the gap in computational enzyme design where the excited electronic states are not normally considered. In specific, we will 1) develop machine learning-enhanced simulation methods to efficiently simulate both the ground and excited electronic states of photoenzymes to assist mechanistic studies and to inform the prediction and design of photoenzymes, and 2) develop a photoenzyme design strategy centered on descriptors derived from both ground and excited electronic states computed by molecular simulations. We will use flavin-dependent “ene”- reductases (EREDs) as the prototype system for the computational tool development and testing since there have already been a collection of computational and experimental data for EREDs, where the computational data are from the PI’s group. This research program will not only deepen our understanding of photoenzyme mechanisms, but will also greatly facilitate the design and prediction of photoenzymes for non-natural reactions important in pharmaceutical synthesis. In the long term, it will also facilitate the identification of natural enzymes that may have previously unknown photo-driven reactivity, which may become new protein scaffolds for developing novel photoenzymatic reactions or become new drug targets.

NSF Awards · FY 2024 · 2024-09

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering. Amidst the global shift toward green and sustainable urban transportation systems, substantial investments have been made in infrastructure to facilitate the adoption of electric vehicles (EVs) in the United States. The placement of an EV charging station (EVCS) potentially has significant broader impacts on peoples' mobility, activity patterns, and visitation to nearby businesses during charging sessions. This provides an opportunity for policy makers to support local businesses (e.g., cafes, restaurants, grocery stores), particularly small and medium-sized enterprises, which play a pivotal role in maintaining community health, especially in vulnerable communities. This SAI project tackles the question of how and where to best place EV charging stations to ensure they not only meet the needs of drivers but also boost the economic resilience of small businesses and promote social equity. The project integrates theory and methods from computational social science, urban resilience, behavioral science, and complex systems to address a pressing societal need -- the equitable, resilient, and sustainable deployment of EVCSs. This project leverages large-scale datasets including mobile phone GPS, charging station usage data, and real-world intervention experiments to understand the broader social and economic impacts of EVCS placement on mobility, social dynamics, and the resilience of businesses. This complex systems approach introduces a new paradigm of infrastructure development and management that significantly extends the scope from individual behavior to social and economic community-wide effects, offering a more comprehensive understanding of the EVCS ecosystem. The optimization and visualization platform will enable agencies and businesses to evaluate hypothetical deployment scenarios, promoting a multi-dimensional approach to infrastructure design. The open-source and public-facing platform ensures that its benefits are not confined to the academic realm but are extended to diverse community stakeholders, reinforcing the project's commitment to inclusive and comprehensive urban development. 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 · 2024-09

This project seeks to understand and improve the ability of neural networks to make accurate predictions on multiple tasks simultaneously. This problem has applications in many areas, such as computer vision, natural language processing, social networks, transportation, and bioinformatics. A key objective is to model the complex relationship between tasks when deploying large neural networks. The modeling of task relationships, together with active measurements of the performances of subsets of tasks, will enable the design of new algorithms for multitask learning. This project will also examine model generalization of fine-tuning and develop new measurement tools to better understand information transfer from multitask neural networks. These new developments will enable applications across multiple disciplines and facilitate the creation of public datasets. There are three components to this project. The first component develops surrogate models to measure task relationships in multitask learning (MTL). A scalable modeling framework will be developed to select subsets of tasks, by utilizing both statistical and geometric properties of large (pre-trained) models. This framework applies to many settings, such as learning the compositionality of data augmentation, and fine-tuning language models from multiple data sources. The second component examines clustering methods for MTL system design objectives, such as egalitarian criteria. The methods will be integrated into a boosting framework to improve task performance. The last component aims to design efficient fine-tuning methods, by utilizing new measurements of the generalization of large neural networks. Along with developing new MTL and fine-tuning methods, the research team will explore new AI applications, particularly those with heterogeneous features. The team will create a dataset encompassing years of traffic accident records from eight states to facilitate road safety research using MTL. Through the project, the investigator will integrate graduate and undergraduate students into the research process, train them in the new methods, and advise them on related thesis topics. The project will involve synergistic activities such as conference workshops, sessions, and local symposiums. 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.