Worcester Polytechnic Institute

NIH Research Projects · FY 2025 · 2025-05

SUMMARY/ABSTRACT Stress granules (SGs) are dynamic, membrane-less cytoplasmic condensates of proteins and mRNAs that form in response to stressful stimuli. SGs have been linked to a broad variety of cellular processes and disease states and thus are of broad biological importance. However, much remains unknown about the structure and function of SGs. SG composition is specific to stress type, cell type, and disease state. So called “canonical” (Type I) SGs form downstream of integrated stress response activation, and are thought to be anti- apoptotic, such as those caused by acute arsenite exposure and heat shock. Other stress conditions, such as ultraviolet (UV) radiation, cause the formation of “non-canonical” SGs (Type II) with altered composition, unique kinetics, and unknown function. Many studies have thoroughly documented the composition and dynamics of canonical SGs caused by arsenite and heat. However, similar studies of non-canonical subtypes, like UV SGs, have not been performed. Our preliminary data concur with reports that UV SGs are essentially lacking in poly(A)+ RNA. We further observe for the first time that UV SGs lack the apoptotic scaffolding protein RACK1. Our results suggest that UV SGs, unlike canonical SGs, may be pro-apoptotic. Several studies have suggested that RNA-RNA and RNA-protein interactions contribute importantly to the liquid-liquid phase separation (LLPS) required for SG formation. However, if it is true that UV SGs do not contain a significant amount of RNA, this creates a paradox surrounding the role of RNA in SG formation. Our overarching hypothesis is that UV SGs represent a sort of “minimal SG” in that they appear to contain fewer proteins and RNA components than canonical SGs. We reason that UV SGs can be used as a model to understand the protein-protein interactions that drive SG formation. The goal of the proposed research is to define the composition, understand the mechanism of formation, and determine the function with respect to apoptosis, of UV SGs. To achieve these goals, we propose the following specific aims: Aim 1: Characterize the components of non-canonical UV SGs. We will test the working hypothesis that UV SGs represent a “minimal SG” and contain fewer proteins and RNAs than canonical arsenite-induced SGs, utilizing a SG purification strategy in tandem with live and fixed cell imaging. Aim 2: Elucidate the relationship between non-canonical UV SGs and apoptosis. Our working hypothesis is that phase separated UV SGs do not sequester apoptotic factors and therefore confer no benefit to the cell with respect to apoptosis. The studies proposed here are significant because they address several key unanswered questions in SG biology, including the composition of non- canonical SGs and the dynamic relationship between SG subtypes and apoptosis. Our approach is innovative in that we are utilizing SG subtypes as a mechanism to elucidate the role of RNA phase separation and protein-protein interactions within SGs in a way that has not, to our knowledge, been proposed.

NSF Awards · FY 2025 · 2025-05

Liquid drops that rest on solid surfaces are called “sessile drops”. This project will study the behavior of sessile drops when air flows past the surface. High-speed imaging shows that in this case sessile drops oscillate before they become dislodged from the surface. The reason for these surprising oscillations is not well understood. The experiments in this project will uncover the causes underlying the drop oscillations. This project will benefit many practical applications, including improving surface washing, reducing ice growth on aircraft, and improving heat transfer operations. This project will benefit society by improving energy efficiency and by training students in advanced experimental techniques. Outreach programs will be conducted to attract high school students to scientific careers. This project will investigate the oscillatory dynamics of sessile droplets exposed to steady shear flow of the surrounding air, focusing on the interplay between drag forces, surface tension, and fluctuating pressure fields. The research addresses two fundamental questions: (1) how does the interaction between drag force and surface tension affect droplet oscillations, and (2) what is the role of the surrounding pressure field in modulating oscillation patterns. High-speed shadowgraphy will be used to capture the dynamics of droplets, while Particle Image Velocimetry (PIV) will be used to reconstruct the surrounding velocity and pressure fields. Experiments will systematically vary surface tension and the velocity of the surrounding air to quantify the competing forces responsible for droplet deformation and oscillation. The analysis will combine force balances with image processing techniques to track droplet motion and scaling laws to interpret the results. By characterizing the inertial effects induced within the droplets, the project aims to provide critical insights into the mechanisms governing contact line depinning. Contact line depinning is the critical step to dislodging the droplet from the surface. The project will generate experimental data and insights to inform future predictive models describing the oscillation frequency and amplitude of the sessile drop. The outcomes will advance the fundamental understanding of droplet dynamics and will benefit applications involving droplets that dislodge from surfaces, such as advanced energy systems, heat exchangers, or anti-fog surfaces. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

NON-TECHNICAL SUMMARY The REU Site: Center for Early Research Experiences in Functional Materials at Worcester Polytechnic Institute (WPI) offers mentored research opportunities and professional development to ten undergraduate students each year during a 10-week summer program. The program focuses on students from two- and four-year institutions in Central New England who have little to no prior research experience. Participants will engage in interdisciplinary projects, working closely with faculty and graduate student mentors who specialize in areas such as materials for flexible and wearable electronics, sustainable photovoltaics, and responsive biomaterials for robotics and therapeutic applications. These projects are designed to foster collaboration between students with different academic backgrounds, with two students from different academic majors collaborating on each project, co-mentored by faculty and graduate students from different departments. In addition to research, the program includes a comprehensive series of professional development seminars aimed at preparing students for the modern workforce and introducing them to the complex challenges within the field of functional materials. REU students will also develop teaching modules on materials science, which they will share with local high school students. The summer culminates with the WPI Summer Research Showcase, an event that typically features over 100 student presenters. This provides students with the opportunity to disseminate their research findings, practice their presentation skills, and expand their professional networks. TECHNICAL SUMMARY The REU Site: Center for Early Research Experiences in Functional Materials at Worcester Polytechnic Institute (WPI) provides a ten-week summer program for ten undergraduate students to conduct mentored research and develop professional skills. Participants will work on interdisciplinary projects addressing challenges in materials science, such as developing materials for wearable electronics, biomaterials for medical applications, and energy generation and storage technologies. A key aspect of the program is hands-on materials characterization, where students will employ advanced techniques, including Raman spectroscopy, transient optical absorption, THz spectroscopy, atomic force and scanning electron microscopy, and nitrogen-vacancy (NV) center-based diamond microscopy for high-sensitivity magnetic field imaging. Students will also perform theoretical and computational studies, such as density functional theory (DFT) modeling, to complement experimental findings. Teams will consist of researchers from at least two disciplines—such as physics, chemistry, materials science, mechanical engineering, biomedical engineering, and chemical engineering—reflecting the collaborative nature of modern research and industry. This program offers immersive experience, equipping students with technical expertise and a multidisciplinary perspective essential for advancing functional materials research and applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Non-Technical Summary Suturing is often used in surgeries to close wounds and this technology has been practiced for thousands of years. Yet, it is an uncomfortable and damaging process—causing pain, tissue damage, infection, scarring, and leakage. Hydrogel bioadhesives have thus emerged, aiming to replace sutures. Since their first development over a half-century ago, they have substantially benefitted surgeries in many ways, such as simplified procedures, short operation time, less discomfort, and reduced post-surgery complications. Hydrogel bioadhesives are soft, wet, and biocompatible and used just like common glues or duct tape. Existing hydrogel bioadhesives are mostly designed for emergency applications such as wound closure and sealing. However, when prolonged adhesion is required, typically in long-term implantation, they are unfit, owing to mechanical mismatch with target tissues and unstable adhesion in the physiological environment. The mechanical mismatch constrains natural tissue movement to cause damage, insufficiently supports tissue physiological functions, and alters the local cell microenvironment to induce abnormal cellular behaviors. Unstable adhesion leads to premature adhesion failure. To address these challenges, this project develops a class of bioadhesive implants by modular design. The bioadhesive implants integrate a liquid adhesive layer and a solid hydrogel layer, where the former provides fast, stable, and strong adhesion between the target tissue and the hydrogel layer, and the latter provides tissue-specific mechanical compliance. This project supports the fundamental research to determine the optimal formulation of bioadhesive implants, investigate adhesion kinetics and properties under various operation conditions as well as long-term adhesion behaviors under fatigue mechanical loads, and elucidate relations between adhesion supramolecular processes and adhesion behaviors. These findings expand the knowledge of bioadhesive design, principles, and mechanisms, and advance various implantable therapies such as bioelectronic medicine, regenerative medicine, and on-target drug delivery. This project also highlights the broader impacts on education and outreach by engaging grades 4-12 and undergraduate students to learn the basics of hydrogels, perform the hands-on making of hydrogels, and participate in hydrogel and bioadhesive research, and by broadly disseminating this research to the general public to increase their awareness of hydrogel bioadhesive technologies and amplify the scientific and societal impacts. Technical Summary This project develops a class of bioadhesive implants by modular design, which integrates a liquid adhesive polymer layer and a solid tissue-elasticity-matching hydrogel layer. The bioadhesion is realized by penetrating adhesive polymers into the target tissue and the hydrogel layer and then gelating inside them to form a new polymer network, connecting them through topological entanglements. The polymer gelation kinetics can be tuned to enable fast adhesion and the topological entanglements create deep adhesion to allow strong and stable adhesion. The bioadhesion does not require functional groups from hydrogels, thus broadening the choices of hydrogels (including chemically inert hydrogels) to be used to match the elasticity of diverse biological tissues. The research objectives are (i) design, formulate, and characterize bioadhesive implants, (ii) investigation of complex adhesion behaviors ex vivo, and (iii) mechanistic understanding of linking supramolecular processes to adhesion. To obtain the optimal formulation and guide the practical use of bioadhesive implants, experimental studies will be performed to evaluate adhesion energy, adhesion kinetics, adhesion behaviors associated with adhesive thicknesses, penetration depths, material properties of tissues and hydrogels, and fatigue loading, as well as biocompatibility. To fundamentally understand the adhesion mechanism, theoretical modeling will be established to elucidate the critical supramolecular process of diffusion and gelation of adhesive polymers on the effective development of bioadhesion and how they are influenced by the material properties of hydrogels and tissues. This research also offers an important and emerging topic for educational and outreach activities on hydrogels and bioadhesives and facilitates clinical collaboration and translation. 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

Biodiversity loss is one of three critical interlinked challenges facing humanity today, alongside climate change and pollution. Nature crime, particularly, undermines the effectiveness of activities to reduce biodiversity loss. Among these crimes, illegal wildlife trade (IWT) is egregious and a cause and consequence of biodiversity loss. This illegal activity spans all US states and territories. The proposed research will use new science-practitioner partnerships to overcome scientific knowledge failures about a) data generation and classification and b) data integration and application. The science team will use sharks, rays, and turtles as conservation examples and include expertise in molecular biology, wildlife forensics, operations research, network disruption, computer and data science, conservation criminology and human geography. This research will enhance conservation efforts both in the US and globally by increasing scientific awareness and improving the precision of knowledge about the scope and rate of loss of sharks, rays and turtles involved in the illegal wildlife trade. Findings will contribute valuable data about these species and will also be integrated with other data to better understand fundamental changes of socio-environmental systems. Additionally, the results will inform science-based strategies for disrupting illegal wildlife trade including crime prevention, restorative justice, and law enforcement measures. The science team will also engage with undergraduate students through project-based learning, support PhD dissertations, and provide specialized training for law enforcement and their partners through a 5-day tool-training workshop. Furthermore, the researchers will collaborate with diverse stakeholders by sharing information, co-designing initiatives, and offering decision-making support. The research aims focus on novel species identification technologies, online market analysis, data integration, and operational strategies to address fundamental challenges hindering effective action for IWT. The goals and scope of this research include: 1) producing near real time species-level genetic identification for 185 species using unique High-Resolution Melt (HRM) profiles; 2) designing and developing new machine learning frameworks that explore various HRM ranges, utilizing advanced deep learning and transfer learning approaches using data augmentation techniques; 3) advancing the accuracy of species identification through improved analysis of HRM curve profiles; 4) conducting large-scale data collection, innovative data labeling, and automatic classification to provide data openness, high recall rate, effective IWT post classification models, and a visualization tool; 5) integrating physical and virtual crime ecosystems using spatially interoperable data from experts and non-experts; 6) addressing adversarial challenges through adaptive learning and sequence creation to improve decision-making under uncertainty; and 7) developing a model structure capable of accounting for complexity of real-world networks. This research will advance science understanding and help overcome conservation knowledge failures, thereby aiding efforts to decrease the acceleration of biodiversity loss from IWT. This project is jointly funded by the Divisions of Environmental Biology and Integrative Organismal Systems through the Partnership to Advance Conservation Science and Practice Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Proteins are the catalytic agents in the cell and carry out most cellular reactions. Control of the abundance or activity of cellular proteins can be used to modify cellular pathways and, by extension, control the health and viability of an organism. While systems for targeted destruction of proteins of interest are available for research and therapeutics in animals, very few tools exist to control protein abundance in plants. Instead, plant scientists are currently reliant on methods that regulate protein abundance at the level of mRNA expression, which are inherently slow. This research will enhance the capabilities of a recently developed tool to control protein degradation in plants called E3 DART, thus providing the opportunity to control the abundance of a protein target at the protein level. Specifically, this work will expand the mode of activation of E3 DART and widen its applicability to multiple plant species of research and commercial importance. The Broader Impacts of this work include its intrinsic merit as the optimized tool will enhance fundamental plant biology research and may be deployed in the future for applied agronomic innovations. Agronomic industry innovations that could benefit include developing novel herbicide resistance traits, engineering pathogen resistance by degrading pathogen effectors or design of new haploid technologies for faster breeding. Additional activities include outreach and development of teaching tools for museum activities, high school and/or undergraduate courses, and plasmid designs and plant lines deposited in public repositories. The research team will continue to mentor young scientists to develop a strong workforce in STEM. Inducible protein degradation systems are an important, but untapped resource for the study of protein function in plant cells. The recently developed E3-targeted Degradation of Plant Proteins (E3-DART) is a protein degradation system based on the activity of a Novel E3 Ligase (NEL) from Salmonella. The goals of this work are to optimize the E3 DART system such that it can be chemically controlled and, combined with other recombinant strategies, used in proof-of concept experiments to test the function of specific endomembrane proteins. This complementary set of tools, which are lacking in model plant systems, will provide deeper insights than previously possible into the highly dynamic, temporal, and spatial molecular mechanisms of organelle biogenesis and endomembrane trafficking. The specific aims of this research are to: 1) Develop a ligand-inducible E3-DART system; 2) Control E3-DART activity with novel recombinant tools; and 3) Develop proof-of-concept methodology with E3-DART to study endomembrane protein function and synchronized secretory protein trafficking. A robust system to control protein degradation will have a significant impact on plant biology. Key for the development of such systems is to engineer plant lines in which the degron-tagged protein of interest functionally complements a mutant, and the E3 DART activity and target protein degradation are controlled in a tunable and reversible manner. Such capability will allow for future characterization of the function of essential proteins involved in dynamic cellular processes in plants in ways not achievable with existing 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 · 2024-12

Crude oil extracted from reservoirs contains water dispersed in oil in the form of emulsions. Separation of water from this emulsion (demulsification) is an essential step in the oil production process to get pure oil and reduce corrosion of oil transportation pipelines. Ultrasound based water-oil separation has gained popularity in recent years as a powerful technique to achieve this separation. Studies in recent years have led to a preliminary understanding that acoustic cavitation (formation, growth, and collapse of vapor bubbles due to ultrasonic pressure oscillations) can play an important role in enhancing the efficiency of this ultrasound based demulsification process. However, the exact mechanism behind how cavitation can aid demulsification is not yet clearly understood. The proposed research aims to provide a detailed understanding of the effects of acoustic cavitation on water-oil demulsification through an integrated experimental and computational approach. The project will also encompass educational activities through three different channels: (1) development of a learning module for high school students through a precollegiate outreach program (2) undergraduate projects that will feed into the project’s vision (3) recruitment and cross disciplinary training of students. The technical goal of this project is to develop a comprehensive understanding of how acoustic cavitation can lead to an increased phase separation efficiency of water-oil emulsion. To achieve this goal, the project will focus on characterizing two major phenomena involved in demulsification: (1) generation of mechanical forces that weaken/rupture of the surfactant interfacial film and (2) droplet coalescence leading to phase separation. This proposal intends to study in detail the mechanical forces associated with cavitation and how they affect surfactant film stability in a water-oil emulsion leading to phase separation. The project will address fundamental questions regarding the connection between bubble and droplet dynamics, fluid motion and resultant mechanical stresses that are transmitted to the interfacial film, and ultimately the effect of all these parameters on droplet coalescence and demulsification. The experiments will enable the study of how the repeated growth and collapse of cavitation bubbles destabilize and rupture the emulsion, using high speed visualizations and Particle Image Velocimetry. On the numerical side, a Computational Fluid Dynamics model will be used to perform parametric studies of key variable across a wide range of physically relevant conditions. The knowledge gained and the methodologies developed through this project will provide the necessary scientific foundation to explore the role of cavitation in the proposed and other ultrasound-based technologies. 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 seamless ubiquitous mobile broadband coverage is an important objective of Next-generation (Next-G) wireless Networks, they are expected to be also part of the enabling infrastructure for many future vertical applications. These requirements demand Next-G wireless networks to be space-aerial-ground (SAG) integrated, open networks supporting multiple functionalities and native features. However, at-scale experimentation, testing and validation of innovative research, research and development (R&D) outcomes and technology translation in such open and integrated network technologies is currently not possible due to the lack of a large-scale testbed with open access to the broader community. Building such a testbed is a major investment that is not possible by individual researchers or institutions. This workshop brings together key stakeholders from both the wireless and aerospace research and development communities, including subject matter experts from relevant government agencies, private industry and academic institutions, to formulate a comprehensive roadmap that will define elements and design requirements of an open, integrated, space-air-ground network testbed composed of satellite, aerial and terrestrial segments. The testbed is expected to serve both R&D community and technology translators by providing a versatile, accessible, and programmable platform capable of conducting experiments within the context of integrated sensing-compute-communications, seamless multi-domain ubiquitous connectivity and vertical-driven technology development (both space-verticals and wireless-verticals) using the Open Radio Access Networking (O-RAN) principles of modular, open, and standardized interfaces. The in-person workshop will feature keynotes and group discussions to develop an understanding of the essential features, minimum capabilities, design requirements and use-cases for launching a national shared Open and Integrated Networks testbed. To stimulate discussion and collect the perspectives from attendees belonging to a diverse range of technical communities on different aspects of the testbed roadmap, each breakout session will be comprised of several parallel group discussions. Information and insights collected from each of the sessions will be used to compile a technical report including a set of recommendations defining a roadmap for the design, development and launching of an integrated open space-aerial-ground wireless networking testbed with desired functionalities and capabilities that will benefit both scientific community and technology translation. 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

This project aims to serve the national interest by training a skillful workforce to meet the growing technology challenges delineated in the CHIPS and Science Act of 2022. Sensor technology is among the fastest-developing branches in modern technology and contributes to all aspects of industrial applications. The transformation in miniature, intelligent and convoluted sensor systems will enable scientific and engineering innovations in the defense, space, civil, health, and environmental technology sectors. To embrace such a transformation, Illinois Tech’s ExLENT project, through the International Center for Sensor Science and Engineering (ICSSE), assembled a team of experts to provide cross-disciplinary, experiential training in combined areas of advanced manufacturing, artificial intelligence, biotechnology, environmental control, semiconductor and microelectronics. The goal of the project is to prepare underserved groups, particularly veterans, and underrepresented populations in STEM with the technical capability to tackle the multifaceted challenges in the sensor technology sector. The project consists of education and mentored research in sensor technology in an academic setting, followed by related industrial internship training. Specifically, through a 9-credit certificate program, Illinois Tech’s ExLENT project offers dedicated mentorship, hands-on training, and one-on-one collaborative career support through faculty mentors, industrial partners, and Veterans Administration (VA) associated organizations. Of note, the project will afford veterans unique opportunities for reskilling/upskilling and reintegrating into civilian society, creating a strong career path into the emerging technology workforce. This project aligns with the NSF ExLENT program, funded by the NSF TIP and EDU Directorates, as it seeks to support experiential learning opportunities for individuals from diverse professional and educational backgrounds to increase their interest in, and their access to, career pathways in emerging technology fields. 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 purpose of this project is to plan, pilot, and study approaches to nonprofit organizations’ resource-sharing and community building. The ultimate aim is to maximize nonprofits' potential for positive and efficient impact on their common geographies and missions. The research team employs community-engaged, human-centered design to improve and scale a previously developed resource-sharing technology tool. The tool is a scalable, generalizable platform that can play a critical role in supporting communities as they pursue contextual, localized solutions and collective impact. The goal is to facilitate stronger collaboration among nonprofit organizations to address some of the most pressing challenges they face. This project emphasizes community-driven, resource-rich collaborations with technology while activating community assets. The team is also studying differences between short- and long-term needs and collective impact, and the potential impact of their resource-sharing platform on mutual aid behaviors and values in the face of emergency situations. The team is actively expanding to new cohorts in Worcester, MA and in the Lehigh Valley, PA. Both are communities transitioning from post-industrial economies, while contending with problems of houselessness, immigration, and emergency response. New mechanisms are being developed to conduct recurrent multilateral combinatorial exchanges paired with community norms and practices to connect technology with human dynamics. Such a tool will enable nonprofit organizations to temporarily share their resources and develop a responsive community of practice. The research team is seeking to understand the tool and related ecosystem qualitatively and quantitatively and how it can have transformative impact on communities. This project will inform a framework to measure transformative impact and to illuminate how the resource sharing platform can aid in the collective impact of communities, meet emerging needs responsively and flexibly, and form cohorts to enable shared communities of practice. This project contributes to a functional understanding of the characteristics and success factors for collective impact efforts in addressing community needs and challenges. This project is in response to the Civic Innovation Challenge program’s Track B. Bridging the gap between essential resources and services & community needs and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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

This project will explore new ways to address complex mathematical problems by integrating advanced machine learning techniques with automated reasoning. By combining artificial intelligence with formal mathematical methods, the research team will advance the knowledge of some long-standing open problems in mathematics and computer science. These disciplines are crucial for the development of technologies that ensure software reliability, security, and efficiency --- key aspects in the digital age. The project not only supports the exploration of theoretical knowledge but also the practical application of these new algorithms to improve the tools that are integral to the technological infrastructure. The project will support two students, a mathematician and a computer scientist, who will closely work together to achieve the proposed goals. In technical terms, the project will use three specific open problems within graph theory and combinatorics as test cases to evaluate the effectiveness of new algorithms. The first objective will involve applying machine learning to develop efficient symmetry-breaking clauses to determine the values of small Ramsey numbers. Secondly, transformer-based methods will be used to generate small Folkman graphs. Lastly, the project will tackle a realizability problem related to point sets in a plane, aiming to understand and create configurations with larger than previously known planar discrepancies. This project will be a collaboration between mathematicians and computer scientists aiming to explore the synergy between machine learning and SAT solvers. The research team will improve methods for addressing these difficult problems, potentially obtaining both theoretical insights and novel computational techniques in mathematical and computational sciences. 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

This project aims to serve the national interest by significantly advancing the understanding of the impact of Generative AI on Architectural Engineering students’ ability to generate visual ideas. Architectural Engineering (AE), the application of engineering principles to the design and construction of buildings, relies heavily on visual communication between engineers, architects, and design critics as they work collaboratively on the design of buildings. Practitioners of AE engage in visual ideation as well as visual representation. Emergent technologies such as Generative AI (GenAI) and Augmented Reality (AR) hold great promise to enhance and improve students' visual ideation skills. These technologies permit the use of conversational prompts for quick visual ideation of abstract design ideas. Once designs have been conceptualized, the tools can be used to display immersive 3D models in an engaging manner to all members of the design team. Through these investigations in AE, significant lessons learned will likely inform design in other engineering fields. The goal of the project is to understand the impact of GenAI-empowered AR (GenAI-AR) combined with multi-user AR on students' collaborative learning in AE design. The PIs plan to develop two GenAI-AR modules for integration into existing AE design studio curricula. Three research questions are posited to guide the investigations designed to enhance understanding of visual thinking and communication during students’ collaborative learning in AE design. An interventional study will be conducted, where the comparison will be between a “control group” as the baseline in one year and two “experimental groups” in the following two years. A mixed method approach involving semi-structured group interviews will be used to elicit answers to questions regarding impact of the intervention on student engagement, self-efficacy and teamwork skills. A qualified evaluator will ensure that impact is assessed in the context of project goals in a way that lays the foundation for scaling up the intervention in future. Robust and comprehensive dissemination plans will exploit an open-source approach, with Unity modules arising from the effort available on GitHub to facilitate easy modification and adaptation for a range of design situations. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices 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.

NSF Awards · FY 2024 · 2024-10

Non-technical Abstract: Quantum computing offers the promise of solving intractable problems across many scientific domains including chemistry and biology. However, despite the recent advancements in quantum hardware, we are still far from the reality of quantum researchers having on-premises quantum computers. The main goal of this research is to democratize access to quantum computing resources by allowing researchers to work with qubits via the Internet. To achieve this evolution to what we have observed in classical cloud computing with the general availability of shared, public cloud platforms such as Amazon Web Service, this project studies the scientific foundation for the quantum cloud by developing general workflow and resource management algorithms. On the education front, this project integrates the fundamentals of quantum computing and traditional cloud computing techniques into the curriculum, aiming to provide theoretical and practical training to the students and prepare them for later innovating in various aspects of science requiring quantum computing and quantum networking knowledge. Technical Abstract: The project’s foundation is the design and implementation of a quantum cloud framework called QCloud that consists of novel job placement, job scheduling, and network scheduling algorithms based on proposed models of quantum circuits, gates, and remote gates. To effectively manage quantum cloud resources, the research team investigates algorithms for scheduling quantum programs considering various types of quantum errors to improve quantum application fidelity without sacrificing other traditional scheduling metrics. The project also looks at the development of a quantum cloud simulator with both computing and network modules. The simulator is an integral part of QCloud, providing a means to evaluate various components of Qcloud. Furthermore, the research team provides students with hands-on experience in building and running quantum programs with the developed simulator. The proposed research activities are built on PIs’ prior work in quantum cloud, quantum networks, and traditional cloud and edge resource management. If successful, the research can contribute to the evolution of the quantum cloud, provide a more diverse community with easier access to quantum resources, and build the quantum presence at the PI’s institute. This award was jointly funded by the Directorate for Mathematical and Physical Sciences, Office of Strategic Initiatives; and the Directorate for Computer and Information Science and Engineering, Division of Computing and Communication Foundations. 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 placenta is a critical temporary organ that develops during pregnancy impacting lifelong health of both mother and infant. Many pregnancy-related complications are the result of a placental abnormality, including preeclampsia. Preeclampsia is a condition often clinically diagnosed by the onset of high blood pressure after the 20th week of pregnancy, which can necessitate preterm delivery. Preeclampsia occurs in 1 out of 12 pregnancies resulting in 76,000 maternal deaths and 500,000 infant deaths each year worldwide. Understanding and treating preeclampsia is challenging because the placenta is also one of the least understood human organs. The proposed work focuses on developing new 3D models using placental cells acquired after delivery from patients with preeclampsia. In addition, the proposed work includes research opportunities for undergraduate and high school students to engage in the project. The PI’s lab will also lead biomaterials outreach activities, particularly showing how biomaterials are advancing women’s health, with middle and high school students through local services that provide after and out-of-school activities. Preeclampsia is a disease state of the placenta, an organ that is not well understood, necessitating new model systems to study it. Trophoblast cells are the main cell type composing the placenta, with important roles including nutrient and waste transport as well as invading the decidualized endometrium. There are few in vitro cell models available for preeclampsia, and currently there are no 3D in vitro systems using patient-derived preeclamptic trophoblast cells. The goal of the this project is to develop a preeclamptic trophoblast 3D spheroid model that enables studies of trophoblast invasiveness, a critical process for overall placental health. In Objective 1, preeclamptic trophoblast spheroid models will be developed and characterized. The spheroids will be incorporated within extracellular matrices mimicking the maternal endometrium. In Objective 2, the preeclamptic spheroid models will be used to investigate trophoblast cell invasion at the placental-endometrial interface. Comparisons will be made between spheroids developed using trophoblasts from either healthy patients or patients with diagnosed preeclampsia. The engineering advances of the proposed work include (1) Designing the appropriate culture conditions to develop spheroids that can be readily maintained and are repeatable, (2) Engineering the interface between the placenta and maternal endometrium by incorporating the spheroids within a methacrylated gelatin (GelMa) matrix, and (3) Developing the matrix composition and stiffness representative of the endometrium during pregnancy while also maintaining preeclamptic spheroid viability over time. 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 ever-growing diversity of edge devices, from CPUs for basic tasks to graphics processing units (GPUs) for graphics and neural processing units (NPUs) for machine learning, presents a challenge for edge cloud computing. While advanced wireless communication seamlessly connects billions of edge devices to the edge cloud, traditional homogenous edge cloud platforms struggle to handle diverse workloads and computation models efficiently. This research proposes a pioneering approach using Field-Programmable Gate Arrays (FPGAs) within an edge overlay framework. FPGAs can be dynamically reconfigured to act as various processing units, efficiently handling these diverse computational needs. The main goal of this research is to develop novel techniques for offloading heterogeneous tasks, ensuring high overall throughput, uninterrupted service, and fault tolerance. To demonstrate the effectiveness of the proposed techniques, this research will focus on a drone network surveillance use case. The developed approach has the potential to significantly improve edge computing's energy efficiency, resiliency, and scalability. This research will make a significant contribution by making powerful edge cloud computing more accessible. To achieve this, the researchers will develop new course modules at UMass and WPI focused on heterogeneous edge computing, institute a research workshop for sharing research ideas and showcasing work, and leverage targeted programs to recruit underrepresented students to research programs. These initiatives will empower undergraduate and graduate students to leverage edge cloud FPGA resources for various hardware and software experiments. The annual research workshop, organized and executed by graduate students, will be open to the wider community, further expanding the project's reach and impact. All findings, innovations, and developed software from this research will be openly shared to ensure they are freely accessible and usable by the research community, industry partners, and the public, promoting collaboration, further development, and practical 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

Immersive multimedia systems, such as volumetric video (VV), virtual reality (VR), and augmented reality (AR) systems, have become popular recently and attracted a broad range of user applications. While presenting a fully interactive and engaging user experience, the immersive multimedia poses unique security challenges associated with its fine-grained three-dimensional (3D) content. In particular, the 3D content (e.g., 3D human face) may be exploited by adversaries to issue biometric-based attacks (e.g., spoofing attacks against face authentication), causing significant security and societal concerns. This project aims to investigate and address such security challenges in volumetric videos by developing (1) an advanced face spoofing attack leveraging real-time environment lighting estimation and generation; and (2) an effective countermeasure injecting protective perturbations to the 3D content, which invalidates the spoofing attack while maintaining the original quality of experience. As a pilot study on the security implications of immersive multimedia, the proposed project will benefit the larger-scale adoption of VR and AR technologies in many fields of studies involving sensitive data and computations, such as teleconferencing, remote education, and healthcare. The project consists of three research thrusts. First, it demonstrates that the state-of-the-art face authentication systems, even equipped with advanced liveness detection mechanisms, can be effectively compromised by the 3D face models leveraging the proposed lighting estimation and generation approach. Second, it proposes a real-time perturbation generation mechanism to obfuscate and protect the sensitive 3D content from being exploited for spoofing attacks. Third, it develops an evaluation framework in both lab and community settings to verify the effectiveness of the proposed security approaches and expand the scope of the project to the broader communities. Overall, the project aims to fill the critical security gap in the popular immersive multimedia systems and pave the way toward larger-scale and user-facing deployments. 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 2024 · 2024-09

Congenital heart disease (CHD) is the most common birth defect and a leading cause of death and chronic illness in newborns, infants, and children. Prenatal screening and identification of CHD are critically important for the management and treatment of CHD, but its availability and accuracy are confined by the limited resolutions of current fetal imaging techniques. Personalized flow modeling has been widely used to augment medical imaging modalities for adult and pediatric heart diseases. However, to date, no validated models have been developed for fetal circulation. This project will focus on coarctation of the aorta (CoA), a common CHD accounting for 6-8% of live births with CHD. CoA occurs when a portion of the aorta is narrowed, usually at the isthmus, and blood flow through it is obstructed. Both false positive and false negative diagnoses frequently occur in contemporary prenatal screening and identification of CoA. If the CoA is significant and is not diagnosed and treated in a timely fashion, the newborn or young infant may develop cardiogenic shock or may die. There is an emerging need for a novel technique that provides new metrics and knowledge of the fetal aorta. The proposed work aims to (1) develop a novel, personalized flow modeling paradigm based on routinely used fetal echocardiography that assesses high-fidelity hemodynamics of the fetal aorta. (2) validate this novel paradigm in comparison with cutting-edge fetal magnetic resonance imaging techniques, including both phase-contrast and 4D flow sequences. (3) use this paradigm to conduct pilot studies identifying novel hemodynamic metrics that discriminate normal aorta and CoA in fetuses. This project will focus on wall shear stress, which was linked to vessel dilation and remodeling in general and recurrent coarctation in pediatric and adult patients with repaired CoA but has scarcely been discussed in the fetal aorta with CoA. The proposed work will address the challenges and gaps in the research of fetal circulation and fetal heart disease by producing a novel, validated cardiovascular flow modeling paradigm for personalized hemodynamics in the fetal aorta. This project represents the first-of-its-kind endeavor to develop a rigorously validated personalized flow model for fetal circulation. Our long-term goal is to develop paradigm-shifting computational models for fetal circulation that can be used to uncover the pathogenesis of all critical CHDs to improve diagnosis/prognosis and to aid in the personalized treatment/prevention of CoA and other critical CHDs. Additionally, this project will yield new data and knowledge on fetal aortic hemodynamics, which may enable a better understanding of etiology and improved diagnosis and prognosis of CoA. The tools and knowledge generated by this project will lay a solid foundation for future translational studies that could lead to improved screening, diagnosis, and outcomes for children with CoA.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY (See instructions): To date, the studies on tissue fluidity are limited to the epithelial-tissue paradigm, while a variety of tissue migration and morphogenesis involves cells in the partial epithelial-mesenchymal-transition (pEMT) state, including abnormal early development, tissue regeneration, and cancer growth, The long-term objective of the project is to unravel how to control tissue fluidity and flows with cells pocessing the hybrid epithelial/mesenchymal phenotype along the pEMT spectrum. Different from epithelial tissues, pEMT cell monolayers present a unique spatial distribution of force-bearing actin network, and it is not clear how tissue flow and fluidity is facilitated spatiotemporally in the pEMT tissue. The project aims to investigate the fluidity patterning in the partial EMT state at the large-scale tissue level and cell-cell aggregate level. To achieve the goal, we will develop a multiscale theory-experiment framework to elucidate the cell-cell intercalation and large-scale kinematics regulation in the in vitro tissue monolayer induced by a profound wounds. To investigate the distinction of the partial EMT state to the epithelial state in the fluidity control, we will study cell lines with different EMT potential under different treatment conditions that change their extent of partial EMT state and protocols known to perturb cell intercalations and tissue flow. To describe the large-scale tissue flow, we will leverage the morphoelasticity theory and develop novel numerical methods which solve the coupled system of nonlinear elliptic and time-evolution equations by constrained nonlinear optimizations. To describe the cell-cell intercalations among the tissue flow, we will hybrid the morphoelasticity theory with cell-cell junctional kinematics and mechanics, and solve the multiscale system as a nonlinear optimzation problem.

NSF Awards · FY 2024 · 2024-09

New artificial intelligence (AI) solutions, especially Large Language Models (LLMs), are now widely adopted by the general public. ChatGPT, for example, has over a hundred million daily users. With this widespread adoption, people have begun questioning the role of humans in traditional data pipelines. Nevertheless, many still believe that human intelligence is irreplaceable. Humans can do things AI cannot such as understanding nuances and context and interpreting subjective or ambiguous situations. Thus, this project explores efficient ways to utilize human intelligence in data pipelines, especially with the emergence of LLMs. Among data pipelines, data integration and discovery are the cornerstones in contemporary data science, aiming at understanding datasets, extending and improving them, while focusing on the data. Humans play an integral role in these pipelines as data collectors, generators, and annotators. Accordingly, analyzing their involvement and investigating how to optimally utilize them is a necessity. Therefore, analyzing their involvement and investigating how to optimally utilize them is essential. As contemporary research in the field shifts towards employing LLMs, human involvement will need to be adapted accordingly. To this end, this project will introduce methods to support humans in data integration and discovery, lay the foundations for studying human involvement in these pipelines, and establish new methods to evaluate and benchmark them. This, in turn, also has the potential to create new human jobs, such as prompt engineers and response validators. This work will also contribute to the transparency of solutions via proper prompting and validation of AI responses. Finally, this work is expected to benefit society by facilitating responsible and open data science. Its solutions will be made publicly available along with high-quality benchmarks that will have scientific value for comparisons and settling debates, advancing this important field. This project will develop new data integration and discovery solutions that account for human-in-the-loop processes and the emergence of LLMs. Human-in-the-loop typically refers to leveraging human intelligence in data science pipelines. Human-in-the-loop data integration has received attention in the research literature, from pay-as-you-go frameworks to crowdsourcing, with a common understanding that it requires domain expertise. In contrast, the explicit role of humans in data discovery has yet to be thoroughly explored. Implicitly, humans are consistently involved as data collectors, generators, and annotators. Therefore, the vision of this project is to effectively utilize human intelligence in data integration and discovery as the field increasingly employs LLMs. The research methodology builds upon, integrates, and extends work on scalable data integration and discovery; cognitive and meta-cognitive psychology; and interactions with large language models. This project will investigate fundamental questions about the efficient utilization of humans in data integration and discovery. Specifically, it will address the following research challenges: 1) Understanding the involvement of humans in contemporary data integration and discovery pipelines; 2) Uncovering human biases that interfere with or benefit these processes; 3) Designing future solutions to efficiently involve humans in data integration and discovery, especially with the rise of LLMs. Throughout the development, this project will also develop new evaluation frameworks that consider how humans interact with the data. A key motivation for data discovery is that common benchmarks are often created manually, without accounting for the biases introduced by their creators. This project aims to clean and improve such benchmarks, making them publicly available. Additionally, involving humans in-the-loop will enhance users' trust in the data and the outputs generated by LLMs. By understanding AI-generated solutions through prompting and validating responses, users can better utilize these solutions in their data science tasks. 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

Stroke is a leading cause of death and disability worldwide, creating a significant social and economic burden. It occurs when a blood clot blocks an artery in the brain, cutting off the blood supply and leading to the death of brain cells. To treat stroke, prompt removal of the clot via an inserted tube is critical. It has been noticed that the effectiveness of different clot removal devices and techniques varies depending on the clot's chemical and mechanical properties. However, there is currently no suitable method to determine the properties of a clot stuck in the brain in advance of the surgical process. Thus, the goal of this project is to achieve real-time assessment of clot mechanical properties to guide decision-making for higher clot removal success rates. This project aims to provide doctors with an imaging cable, with a special light source, that can be delivered through the tube to measure the clot's properties. To quickly and accurately interpret the light collected from the clot for chemical- and mechanical-property measurement, an artificial-intelligence algorithm will be created. The development of the imaging cable and the artificial-intelligence algorithm for clinical use will be enabled in this project by the advancement of knowledge in multiple areas including engineering, physics, data science, biochemistry, and medicine via the collaboration among engineers, scientists, and doctors. Additionally, this project will involve undergraduate, community college, and high-school students in research, with an emphasis on diversity in race, gender, and academic stages. The current standard of care for ischemic stroke caused by large-vessel occlusion is mechanical thrombectomy. The failure and complications of mechanical thrombectomy can be associated with the lack of understanding of the mechanical properties of the blood clot to be retrieved. Currently, the evaluation of clot properties can be only vaguely achieved preoperatively by magnetic resonance imaging or computer tomography, which cannot assist real-time decision making. The goal of this project is to achieve intravascular, in-vivo, real-time assessment of clot mechanical properties to guide thrombectomy decision-making for higher success rates. To achieve this goal, the project has three objectives: 1) to evaluate clot chemical composition in-vivo using a catheter with fiber Raman spectroscopy; 2) to predict clot mechanical properties based on its chemical composition via multi-physics modeling and machine learning; and, 3) to validate this in-vivo real-time intravascular approach in animal and human cadaveric models for clinical translation. Specifically, this project has the following tasks: 1) integration of a sub-millimeter Raman fiber probe into a catheter for neuro-intervention; 2) training of a convolutional neural network to interpret Raman spectra for clot biochemical compositions, addressing inconsistent spectral biomarkers and fiber-induced background noise; 3) development of a multi-physics dissipative particle-dynamics model of blood clots with cellular components designed to address variations in individual clinical cases; and 4) training of a neural operator to achieve real-time mapping from Raman signals to clot mechanics. This project has the following potential contributions. In physics and engineering, downsizing the Raman fiber probe to the unprecedented sub-millimeter scale demonstrates the philosophy of using machine learning to overcome hardware difficulties, which can drive sensor innovation. In biology and biochemistry, the miniaturized Raman probe will remove spatial constraints to enable Raman spectroscopy in a wider range of specimens and settings. In medicine and translational research, the multi-physics particle-based numerical model of blood-clot biomechanics can serve as an in-silico platform for investigating the pathogenesis of thrombosis and testing drugs and devices interacting with clots. In data science, this project showcases the neural operator accelerating scientific simulation for real-time, data-driven, clinical decision-making, which may promote machine-learning use for fast interpretation of clinical data. The project may promote sensor-enabled, data-driven, real-time, intraoperative decision-making for optimized thrombectomy with safe, fast, and complete clot removal. Given the fact that stroke is a leading cause of death and disability all around the world, this project promises significant societal impacts. 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 BCSER Individual Investigator Development (IID) project will build the PIs expertise in STEM education research study design, research methods, and data analysis techniques to complement their Aerospace and Mechanical Engineering expertise. In addition, to planned professional development the project will support a pilot STEM education research project that investigates key competencies in troubleshooting. The pilot is designed to result in new knowledge about key aspects of trouble shooting skill as well as develop a practical, low cost, space-efficient learning environment which could be used to teach trouble shooting skills. Despite the practical importance of troubleshooting skills in STEM research and workplaces, we do not know the best practices to teach structured troubleshooting to engineering students. Troubleshooting involves the identification and resolution of problems within a system or process, various psychological constructs and theoretical frameworks are deeply connected to the knowledge and skills required for effective troubleshooting. These include Novice to Expert Transition, Structured Troubleshooting, and Diagrammatic Troubleshooting. These frameworks are not isolated and often interplay, with structured and diagrammatic troubleshooting being more effective as one progresses from novice to expert. As expertise develops, individuals integrate structured methods with intuitive insights gained from experience, allowing for more efficient problem-solving. This work involves obtaining qualitative data on essential troubleshooting competencies required in the mechanical engineering industry. A troubleshooting learning environment is designed investigating skills involved in expert troubleshooting, using techniques in collaborating problem solving and diagrammatic representation/reasoning. Project findings will be shared through workshops and presentations at conferences. The success of this project will be assessed through regular meetings with the advisory board. The project is supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators' capacity to carry out high-quality STEM education research. 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

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. 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 aims to develop a highly flexible robotic arm called CoBRA (Continuum Bioinspired Robotic Assistant) to help people with daily tasks and improve their independent living skills. Inspired by origami, these soft robots are lightweight, adaptable, and safe for human interaction, making them perfect for use in homes and healthcare environments. The research is focused on creating robotic arms that can handle complex tasks like picking up items from shelves or opening cabinet doors and drawers, which are difficult for many existing assistive devices. The ultimate goal is to enhance the quality of life for people with disabilities or who use wheelchairs, to promote independence, and to ease the workload of caregivers. Furthermore, the breakthroughs in designing and controlling these soft robots could also open up new possibilities for their use in industries, manufacturing, and services. The research team is committed to exploring avenues for the translation of this technology, which could significantly benefit the robotics and healthcare industries. This award will also contribute to STEM education, further enhancing its societal impact. The research aims to advance the field of soft robotics, continuum robots, and manipulation control. This will be achieved by integrating a modular system architecture with customizable design algorithms, allowing for the design of robots tailored to specific assistive tasks. The project will incorporate advanced proprioceptive sensing and real-time smooth inverse kinematics, enhancing the robots' ability to adapt to external loads and avoid obstacles while performing precise movements. Additionally, the project will introduce advanced control strategies, including clothoid-based visual servoing and model predictive control, to ensure accurate and safe interaction with the environment. The project's overall goal is to deliver highly capable and reliable assistive systems that improve performance over existing tools and methods. If successful, these robots will be capable of carrying a cup full of water without spilling its contents and opening and closing doors. This will be achieved through an iterative process of building, modeling, controlling, and testing these robots in a comprehensive evaluation plan with real-world use cases. 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

For many manufacturing, logistics, and service applications, robots are required to pick up and manipulate objects. Even though this fundamental capability has been studied extensively in the last decades and a significant progress has been made, robots still struggle to reach the desired reliability levels, especially when they attempt to manipulate objects in cluttered and unstructured settings. In these settings, the variety of objects and the possible scene configurations are immense, making it extremely challenging to develop a single overarching method that can work in all the conditions. Instead of trying to develop a panacea, this Faculty Early Career Development (CAREER) project presents a fundamentally different approach: leveraging the capabilities of multiple different methods, combining their strengths and avoiding their drawbacks. The framework also creates conditions that boost the algorithms’ success by allowing the robot to efficiently collect more information about the scene. The outcomes of this research will be utilized to develop robotics solutions to environmental problems, e.g. waste sorting and recycling, and establish a first-of-its-kind environmental robotics undergraduate track. For combining the opinions of different algorithms, several ensemble learning methods will be developed, tailored to the robotic manipulation domain. A diversity analysis will be conducted, which will identify the differences between the algorithms and guide the ensemble development process. For enabling the robot to systematically collect data, active vision strategies will be developed for the underlying grasping algorithms and their ensembles. A study to develop a high-level decision-making algorithm is also planned to enable robots to determine the best suited dexterous picking strategy for a given manipulation scenario. 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 · 2024-08

Modified Project Summary/Abstract Section Brain stimulation therapies are important and effective treatments for people with depression and other mental disorders. The National Institute of Mental Health (NIMH) is supporting studies exploring how to make brain stimulation therapies more personalized and effective while reducing side effects. For all brain stimulation modalities, computational numerical modeling of the electric, magnetic and acoustic fields within a patient-specific head model is the leading and most promising way to develop improved spatial targeting methods and quantitatively determine the required stimulation dose. Similarly, neurophysiological signal analyses require extensive numerical computational modeling to identify active cortical domains from limited electrode voltage and magnetometer measurements. The present proposal will facilitate development of fast and accurate brain and human body modeling methods and techniques via a dedicated and growing computational conference on Brain and Human Body Modeling. Aligned with NIMH’s mission, the specific aims of this proposal will include the following: (i) Exchanging ideas, methods, and approaches on computational modeling electric and magnetic fields within the brain targeting neurostimulation modalities (TMS, TES, DBS, tFUS) and associated neurophysiological recordings (EEG, iEEG, MEG); (ii) Integration of modeling techniques into clinical practice; (iii) Hands-on training to keep pace with the rapidly evolving technologies by introducing four dedicated workshops in total as an integral part of the conference agenda; (iv) Participation by leading industry subject matter experts in the form of dedicated presentations, demonstrations, and booths/banners which will enable networking opportunities, multi-disciplinary collaboration, and potential employment in high-tech markets. The modeling techniques to be presented and discussed are aimed at providing better focality, targeting accuracy, and improve the overall efficiency of brain stimulation methods. They will also support promising combinations and derivatives of neuromodulation and neurophysiological recordings such as TMS-EEG and TES-EEG. No established computational methods for modeling these combined modalities currently exist. The conference will include related topics that share very similar, sometimes identical, computational methods and tools. We aim to synergistically share these computational tools for maximal benefit and to facilitate interactions across disciplines.