Georgia Tech Research Corporation

NSF Awards · FY 2024 · 2024-09

The Internet relies on inter-domain routing for transmitting information globally across independent networks spread out over the world. The Border Gateway Protocol (BGP)designed to enable that routing has a critical security vulnerability enabling eavesdropping, interception, espionage, and manipulation of Internet traffic. The Resource Public Key Infrastructure (RPKI) is the most widely adopted security mechanism to verify routing data in BGP and limit the impact of attacks and misconfigurations. Although RPKI was standardized in 2012, in 2024 about 50% of routed address space is still not in RPKI. Understanding why RPKI adoption has stalled, from the organizational perspective, can contribute to ongoing policy and technical efforts worldwide to secure the core technologies underlying the Internet. To that end, this project focuses on analyzing (i) which stakeholders are lagging in implementing RPKI, (ii) what specific barriers to adoption those lagging networks face, and (iii) what approaches are most effective in promoting adoption more widely and helping overcome those barriers. This research advances the knowledge of organizations’ RPKI adoption processes by combining quantitative and qualitative data collection efforts, to better understand how to protect the critical routing infrastructure, and where to target efforts for the most impactful outcomes. This project creates a new dataset of RPKI adoption data with aggregated statistics by organization characteristics and develops a comprehensive socio-technical framework of the adoption process based on measurement results and operator interviews, providing deep insights into operators’ decision-making processes, institutional stances on RPKI, IP management workflows, and operational challenges. Finally, this work generates targeted recommendations to improve RPKI adoption with accompanying analysis of which would yield best results and how they could be most effectively combined to address the needs of different stakeholders. 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

Safety is a crucial requirement for systems employing reinforcement learning in domains such as robotics, autonomous driving, and power systems. In this project we consider safety as the avoidance of known unsafe states and prevention of unknown unsafe behaviors. To achieve this safety goal, we propose a suite of model-based reinforcement learning approaches that span training, deployment, improvement, and evaluation. The project consists of the following research thrusts: 1) Training policies that are robust to distribution shift via distributionally robust approaches; 2) Continual policy improvement via Bayesian risk-averse learning; 3) Adapting policies to non-stationarity via online change detection; and 4) Rigorous simulation via space-filling experiment design to gain understandings of a given policy in various environment settings. If successful, the proposed research will make significant contributions to the existing literature on safe reinforcement learning (RL) by developing new theories and methodologies. In particular, the proposed research has the following innovations: 1) formulation of safety measures as general objectives beyond the standard cumulative form and development of solution approaches for this general formulation; 2) consideration of both intrinsic uncertainty and model uncertainty to ensure that the resulting policy performs well and satisfies a specified risk level in the real environment; 3) bridging the gap between Bayesian RL and safe RL for continually improving models and policies while maintaining the safety of the deployed policy; 4) near-optimal policy learning algorithms that adapt to piecewise non-stationary environments; and 5) rigorous simulation approach for policy evaluation to identify unexpected unsafe behaviors before they actually happen. Because of the generality of the proposed approaches, the resulting techniques will have broad applicability in various domains that utilize reinforcement learning and require safety considerations. This research integrates well with the courses that the PIs have developed and teach. The PIs are committed to promoting broad participation within their research communities by actively engaging students in research and mentoring for academia careers, outreaching to K-12 students, and fostering greater participation of a wide variety of researchers. 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

Floating, single-celled algae, or phytoplankton, form the base of marine food webs. When phytoplankton have sufficient nutrients to grow quickly and generate dense populations, known as blooms, they influence productivity of the entire food web, including rich coastal fisheries. The present research explores how the environment (nutrients) as well as physical and chemical interactions between individual cells in a phytoplankton community and their associated bacteria act to control the timing of bloom events in a dynamic coastal ecosystem. The work reveals key biomolecules within the base of the food web that can inform food web functioning (including fisheries) and be used in global computational models that forecast the impacts of phytoplankton activities on global carbon cycling. A unique set of samples and data collected in 2021 and 2022 that captured phytoplankton and bacterial communities before, during, and after phytoplankton blooms, is analyzed using genomic methods and the results are used to interrogate these communities for biomolecules associated with blooms stages. The team mentors undergraduates, graduate students, and postdoctoral researchers in the fields of biochemical oceanography, genome sciences, and time-series multivariate statistics. University of Washington organized hackathons develop publicly accessible portals for the simplified interrogation and visualization of ‘omics data by high schoolers and undergraduates and are implemented in investigator-led undergraduate teaching modules and the University of Rhode Island Ocean Classroom. The research team also returns to Orcas Island, WA, where the field sampling takes place, to host a series of annual Science Weekends to foster scientific engagement with the local community. Phytoplankton blooms, from initiation to decline, play vital roles in biogeochemical cycling by fueling primary production, influencing nutrient availability, impacting carbon sequestration in aquatic ecosystems, and supporting secondary production. In addition to environmental conditions, the physical and chemical interactions between individual phytoplankton can significantly modulate blooms, influencing the growth, maintenance, and senescence of phytoplankton. Recent work in steady-state open ocean ecosystems has shown that important chemicals are transferred amongst plankton on time-dependent metabolic schedules that are related to diel cycles. It is unknown how these metabolic schedules operate in dynamic coastal environments that experience perturbations, such as phytoplankton blooms. Here, the investigators are examining metabolic scheduling using long-term, diel sample sets to reveal how chemical and biological signals associated with the initiation, maintenance, and cessation of phytoplankton blooms are modulated on both short (hrs) and long (days-weeks) time scales. Findings are advancing the ability to predict and manage phytoplankton dynamics, providing crucial insights into ecological stability and future oceanographic sampling strategies. Additionally, outcomes of this study are providing a new foundational understanding of the succession of microbial communities and their chemical interactions across a range of timescales. In the long term, this research has the potential to identify predictors of the timing of phytoplankton blooms, optimize fisheries management, and guide future research on carbon sequestration. 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

Recent years have seen dramatic advances in our understanding of black holes and the accretion disks feeding them. The Event Horizon Telescope (EHT) is an array of radio telescopes spread out across the Earth, which can spatially resolve the event horizon of nearby supermassive black holes. One of the biggest outstanding questions is whether the accretion disk is spinning in the same direction as the black hole or is tilted with respect to the black hole. In such tilted systems the accretion disk can get warped. In this collaborative project, the PIs will leverage recent improvements to the EHT array with advances in (machine-learning based) image-reconstruction algorithms and high resolution numerical simulations to look for signatures of warping in EHT observations. The PIs will involve junior scientists including undergraduates and high school students in their groups, and will actively recruit students from marginalized identities for these positions. The PIs will also engage with the public through the media and public talks, and they will strive to develop visually stunning and informative materials to accompany the main results. The PIs will develop and analyze an extensive general relativistic magnetohydrodynamic (GRMHD) simulation library that spans a wide range of parameters. Their simulations will include the effects of accretion disk tilt, account for the two-temperature nature of the plasma, and incorporate the effects of radiative cooling. Tilt is particularly interesting, since recent numerical simulations demonstrated that warps form in tilted accretion disks around spinning black holes. Warped disks in high-accretion rate sources may form nozzle shocks, which dissipate energy orders of magnitude faster than magneto-rotational instability (MRI) driven turbulence. Subsequently, the PIs will compare their new simulation library with EHT observations. In addition, they have developed a new dictionary learning algorithm using simulations as a training set to more accurately analyze EHT data. Their algorithm does not suffer from spurious artifacts such as bright "knots" seen along the ring in previous EHT images, and it achieves a significantly higher spatial resolution than traditional image reconstruction methods. 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 study of puncturing and penetration of soft solids like hydrogels and biological tissues is important to many fields, from understanding animal bites to the engineering design of drug delivery and surgical devices. However, this study has been challenging due to these materials’ highly compliant, brittle, and time-dependent behavior. This award supports fundamental research into this problem from a combined theoretical, experimental, and computational perspective. This research will provide a quantitative understanding of puncture mechanics in soft solids. The new insights will enable the design of puncture tools that minimize peripheral damage in various biomedical applications. Furthermore, it will inspire the development of new materials to better protect workers and soldiers from puncture wounds. Innovative hands-on outreach activities will be integrated with the research to broaden participation from K-12 students and under-represented groups and to raise awareness of the importance of mechanics in daily phenomena. Previous studies in this area have primarily focused on the harder elastomers (shear modulus>100 kPa) using a simplified theory. In this context, the first goal of this project is to conduct comprehensive puncture experiments that yield quantitative measurements for ultra-soft solids. These experiments will vary penetration rates and puncture sizes and shapes on three types of gels that cover a wide range of nonlinear elasticity, toughness, and poro-viscoelastic properties. The second goal is to build a complete theory, informed by the experiments, for fracture nucleation and propagation in nonlinear poro-viscoelastic solids. The theory will be implemented in a tractable computational framework. The computational model will provide a predictive understanding of the puncture process. It could also enable the use of penetration tests to characterize the fracture properties of ultra-soft gels and tissues, which traditional testing methods find challenging. Developing such a model will also be useful for studying many other failure phenomena in soft solids. 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 three-year renewal REU Site: Southeastern Undergraduate Internship in Nanotechnology (SUIN) is hosted by the Georgia Institute of Technology and the Joint School of Nanoscience and Nanoengineering (JSNN), an academic collaboration between the University of North Carolina – Greensboro and North Carolina Agricultural and Technical State University. SUIN offers undergraduate students the opportunity to perform cutting-edge research at the forefront of nanoscale science and engineering (NSE). This rapidly developing interdisciplinary field will allow students to see the connections across all science and engineering disciplines and inspire the next generation of scientists and engineers. Research results obtained by the undergraduate participants will help in the advancement of understanding phenomena at the nanoscale and how this information can be used to improve applications in electronics, health, safety, environment, infrastructure important to our society. The program will take place at the Institute for Matter and Systems at the Georgia Institute of Technology (IMS/GT) and at JSNN at University of North Carolina – Greensboro and North Carolina A&T University, which are the home of the Southeastern Nanotechnology Infrastructure Corridor (SENIC), a site of the National Nanotechnology Coordinated Infrastructure (NNCI). The project will recruit students to develop research and communication skills and to form strong relationships with faculty, mentors, and staff and with each other. Professional development offerings will provide resources for participants to seek advanced degrees and NSE jobs. The SUIN REU program will provide thorough training and compelling hands-on research experiences at state-of-the-art nanotechnology facilities (cleanroom, characterization, and assorted labs) at the Georgia Institute of Technology and JSNN. A large pool of faculty, mentors, and staff, dedicated to engaging undergraduates in research, will provide projects in a broad range of nanotechnology fields with substantial interdisciplinary content (such as flexible electronics, nanomotors, batteries, environmental filtration, medical, etc.). The goals of the SUIN program are to: 1. Provide an experiential nanotechnology research project with training on advanced equipment and tools which exposes students to the breadth of NSE research topics; 2. Encourage students to explore and pursue graduate-level research and careers in STEM and NSE; 3. Recruit and provide opportunities for students from non-research institutions; 4. Enhance student communication and presentation skills and understanding of ethical issues in scientific research; and 5. Train interns in science communication, entrepreneurship, and the societal and ethical implications of nanoscale science and engineering. Undergraduate participants will be safely and quickly trained to use the processes and equipment needed to carry out their research projects. This advanced training will allow them to complete a well-defined nanoscale research project and present the results at the end of program convocation at a NNCI site. Participants will have opportunities to see the breadth of nanoscale science and engineering and its applications across many disciplines. 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 study of typical or random discrete structures in combinatorics and probability theory has become invaluable to understanding the modern world, from understanding the performance of algorithms on typical instances, to understanding the properties of large computer, social, or logistics networks. Developing powerful mathematical techniques to study random discrete structures can provide rigorous insight and a deep understanding of the behavior of these models. This project will use the ideas, methods, and intuition from the field of statistical physics to answer questions, discover new phenomena, and develop new methods in combinatorics and probability. The project will study the phase transition phenomenon in combinatorial enumeration problems and in large deviation problems in probability theory. As part of the project the PI will organize activities bringing together researchers from different fields, including combinatorics, probability, statistical physics, and algorithms. The project will also feature yearly workshops for Atlanta-area high school students, introducing the students to accessible and exciting topics in math and computer science related to the research aims of the project. One of the major aims of combinatorics is to understand the number and typical structure of large combinatorial objects, such as graphs without a forbidden subgraph, or sets of integers without certain arithmetic patterns. Methods developed to study such problems include the regularity method, the method of graph and hypergraph containers, and large deviation inequalities like Janson's Inequality. This project will use new methods based on tools like the cluster expansion from statistical physics and algorithmic tools from the study of approximate counting and sampling to prove precise results on asymptotic enumeration and typical structure in the settings above, and to dive into the critical regime in which global structure emerges in typical objects, such as when a typical triangle-free graph of a given edge density begins to align with a bipartition of the vertices. This emergence of global structure is analogous to order-disorder phase transitions in statistical physics models like the Ising model, and the aim of this project is to study the details of such phase transitions in combinatorial problems and in the study of large deviations in random graphs. 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

Neural plasticity is a feature of the brain that allows people to learn new skills and adapt to new circumstances. In humans and other animals, the capacity for learning generally declines with age. However, powerful or traumatic experiences can re-open windows of opportunity for heightened neural plasticity. Researchers for this project assert that loss of vision represents one such experience that can open new avenues for learning and plasticity in the mature brain. The proposed work will test this assertion through both rigorous scientific experimentation and educational community outreach. The primary research objective of the proposal is to determine how vision loss alters the rules of plasticity in both visual and auditory cortical areas of the mouse brain. The primary educational objective of the proposal is to develop science and engineering workshops for blind and visually-impaired youth in the Atlanta area. This work has the potential to catalyze new strategies for rehabilitation and inform the design of educational environments for diverse learning needs. The central hypothesis of this research is that vision loss re-opens critical periods for plasticity throughout the brain. The proposed work will focus specifically on plasticity in the mouse visual and auditory cortices following an extended period of visual deprivation during adulthood. The investigators will first evaluate deprivation-dependent rejuvenation of thalamocortical and experience-dependent plasticity in the primary visual cortex. They then examine how auditory projections to primary visual cortex are mechanistically and functionally altered by visual deprivation. Finally, they test whether visual deprivation has the capacity to restore juvenile-like experience-dependent plasticity to the auditory cortex. This work is conducted using a combination of in-vivo and ex-vivo electrophysiology, optogenetic stimulation, and specialized rearing environments. The research is performed alongside an educational program designed to promote STEM engagement in blind and visually-impaired (BVI) youth and foster engineering empowerment within the BVI community. 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 award supports research that contributes new knowledge related to electroacoustic transistors and logic devices to be employed in transformative wave-based technologies, thereby promoting the progress of science, and advancing prosperity and welfare. If successful, these devices will enable materials and structures to carry out computation in situ, offloading computing burden from traditional central processing units (CPUs). Furthermore, conventional electronics may not be suitable for use in extreme temperature, pressure and radiation conditions, and thus as envisioned in hybrid computing systems a portion of the computation may be offloaded to a mechanical device while the higher-level tasks can be carried out by a conventional CPU from a safe and/or shielded proximity. The studied acoustic transistors and logic devices have the potential to impact many electromechanical applications, such as structural switches for industry 4.0, embedded sensors for soft robots, and multiplexers and demultiplexers for communications in next-generation wearables. The devices will operate using reconfigurable topological interface states, which can be dynamically introduced throughout the material system using piezoelectric transducers and switching circuits. Topological interface states are known for their robustness to disorder and back-scattering immune wave propagation, resulting in very low-loss communication, ideal for the planned acoustic logic. Outreach efforts will introduce a large number of underrepresented students to the frontiers of research in physics of waves and electromechanical systems, which will in turn inspire a next generation of scientists and engineers. This research aims to make fundamental contributions to the design, development and exploration of electroacoustic platforms for logic. It will achieve this goal by developing electroacoustic transistors using reconfigurable topological interface states and subsequently demonstrate logical operations leveraging transistor action using theoretical, computational, and experimental methods. Such interface states are anticipated to open routes to commercially viable logic devices robust to inevitable fabrication disorder and defects. Akin to ubiquitous surface acoustic wave (SAW) devices, these electroacoustic hybrid logic devices may have advantages over their fully electronic counterparts in terms of size and cost, and are expected to be more efficient (e.g., low latency output) than software solutions which require analog to digital conversion and digital signal processing. Reconfiguring a single multipurpose topological insulator structure will result in reorientation of topological pathways to produce output corresponding to various logical operations such as AND, OR and NAND. 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

Infectious diseases continue to place a substantial burden on human health, globally. Biomedical studies of diverse pathogens have led to a growing body of knowledge on how pathogens cause us harm. Yet these studies largely leave open the question of why harm the very source of your livelihood, your host? Existing theory on the evolution of virulence points to the importance of tradeoffs between transmission and harm to the host, that imply that some degree of harm is necessary in order to transmit to the next host. While influential, the current tradeoff models make a number of assumptions that do not hold for the large number of bacterial pathogens that can also prosper in environments outside of the host, therefore weakening the importance of host-to-host transmission. The current project seeks to develop new theory to understand how virulence is shaped by environmental pressures in bacterial pathogens with more complex environmental lifestyles. The project calls these highly flexible organisms ‘environmentally derived opportunistic pathogens’, reflecting their ability to grow in diverse environments, unfortunately including humans. The project will combine diverse computational, experimental and theoretical tools to develop a predictive understanding of the environmental and genomic forces that together govern the evolution of virulence. Environmentally-derived bacterial opportunistic pathogens are a growing global concern, with increasing numbers of deadly human infections driven by bacteria previously considered to be primarily environmental organisms. A cornerstone of infectious disease control is the development of predictive epidemiological models. Yet in the case of environmental opportunists, conventional modeling approaches are limited. The most influential approach is to construct compartmental models tracking the dynamics of susceptible and infected individuals, coupled by processes of direct (host-to-host) transmission and virulence. This approach has been adapted to address environmental opportunists via the addition of indirect transmission through an ‘environmental reservoir’ compartment. These models have provided qualitative insights into potential evolutionary dynamics of opportunistic virulence, given assumed tradeoffs with transmission, but are limited by an absence of data on whether virulence and transmission (direct and indirect) tradeoff at all. Machine learning tools have more recently been applied to opportunistic pathogens, with the goal to predict virulence phenotypes from genomic data of clinical infection isolates. Yet this approach is limited by an exclusive focus on clinical isolates (‘winners’ of the infection process, potentially systematically different from environmental isolates) and an absence of transmission analyses. This project will build an extensive library of clinical and environmental strains of the model opportunistic pathogen Pseudomonas aeruginosa (PA), and probe variation across strains using a mixture of machine learning and mathematical modeling tools. The project hypothesizes that that virulence and transmission (direct and indirect) are strain-specific, predictable from genomic features, and not constrained by classical virulence/transmission tradeoffs. To test these hypotheses, the project will pursue the following aims: (i) Develop and test predictive models of strain-specific virulence, transmission, and environment of origin; (ii) Develop and test predictive models of virulence and transmission evolution, as a function of environment and ancestral genotype. 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 award supports research in relativity and relativistic astrophysics, and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. This award supports LIGO science at the Georgia Institute of Technology. The discovery of gravitational waves by NSF's Laser Interferometric Gravitational-Wave Observatory (LIGO) has revolutionized astrophysics and unveiled a cosmic symphony of colliding black holes and neutron stars. Gravitational waves provide a new window into the dynamic universe, promising exciting insights into the nature of gravity, the origins of heavy elements, and the evolution of the cosmos. By detecting and interpreting these ripples in spacetime, this project advances the understanding of the universe and its most extreme phenomena. The award also supports the training of a diverse group of students, nurturing the next generation of scientists and promoting inclusive excellence in STEM. Through public outreach activities, the team supported by this award will share the excitement of gravitational wave discoveries with the broader community, inspiring curiosity and a sense of wonder about the universe. This project aligns with NSF's mission by expanding the frontiers of knowledge and benefits society by cultivating a diverse, globally competitive STEM workforce. The goal of this project is to advance LIGO science by detecting, characterizing, and interpreting gravitational wave transients in LIGO-Virgo-KAGRA data. The team of scientists and students supported by this award will employ a combination of morphology-independent techniques, template-based approaches, and multi-messenger strategies to optimize the detection of gravitational waves from compact binary mergers and other astrophysical sources. Key objectives include improving software infrastructure to reduce computing time and enhance accessibility, mitigating the impact of noise transients on LIGO science, and participating in the fourth LIGO observing run. The team will also support student-led exploration of novel approaches to detecting gravitational wave transients. By advancing gravitational wave detection methods and contributing to LIGO's scientific priorities, this award will deepen the understanding of black holes, neutron stars, and other extreme cosmic phenomena, while also supporting the development of data analysis techniques and fostering collaboration within the LIGO Scientific Collaboration and the broader astrophysics community. 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

Robots that are capable of performing complex tasks in everyday environments and safely interacting with humans have the potential to greatly benefit society. The benefits include such things as providing assistive care to older adults to collaborating with in industrial settings. However, developing such capable robots remains extremely challenging, as it requires testing and improving robot systems across an enormous variety of possible real-world situations. This research project aims to transform the robot development process by creating a powerful simulation framework that can automatically produce diverse, realistic virtual scenarios for training and evaluating robots. The researchers will develop novel machine learning techniques to simulate various ways that humans would interact with robots, create diverse and realistic 3D environments, and generate artificial sensory data that closely mimics what robots would experience in the real world. The research will enable robot developers to rapidly test and refine their systems across millions of virtual scenarios before physical deployment, potentially accelerating the development of safe, capable, and versatile interactive robots while significantly reducing development costs and risks associated with real-world testing. The research addresses the limitations of current scenario-based development in robotics, which is costly, time-consuming, and difficult to scale due to the need for physical deployments in diverse environments. To overcome these challenges, this project aims to shift the bulk of robot validation and data generation to simulation. Specifically, the research will develop a novel compositional generative simulation framework that integrates three key components: (1) a generative model for long-horizon interactive behaviors of non-robot participants; (2) a model for generating dynamically- and geometrically- consistent image and multimodal sensor observations; and, (3) a scene graph generator for static and dynamic objects and their layouts. The integrated simulator will produce a broad range of scenarios, including long-tail and safety-critical events, with user-specified levels of granularity. The team will evaluate the framework by using it to validate existing robotic systems and to generate training data for a learning-based mobile manipulation system operating in realistic home environments. This approach aims to significantly improve the scalability and effectiveness of robot development processes, potentially accelerating the deployment of robust and versatile robotic systems across various domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Classical algebraic geometry studies solution spaces of systems of algebraic equations, which arise naturally in many areas of sciences and engineering. Although the solutions over complex numbers are better understood, the solutions over real numbers or positive numbers are often more meaningful in the contexts where the equations arise. The PI will use the modern technique of tropical geometry to study real and positive solutions of systems of polynomial equations in many unknowns. Tropical mathematics arises over the (max,plus)-algebra where addition is replaced by taking the maximum and multiplication is replaced by the usual addition. The tropical equations are often easier to solve, and some discrete features of the solution set over real or complex numbers can be computed from the solution set over the tropical numbers. This project aims at developing the tropical geometry specifically for solving equations over real numbers or positive numbers. Applications include development of new computational tools with applications in optimization. The PI will continue her work on mentoring postdocs, graduate students, and undergraduate students; organization of conferences; outreach to K-12 students; and promotion of inclusiveness and equity in the mathematical sciences. The PI will study important classes of real algebraic varieties and real semialgebraic sets using tropical geometry. These families include determinantal varieties, nonnegative and sums-of-squares polynomials, principal minors of positive semidefinite matrices, stable and Lorentzian polynomials, discriminants and resultants, and semialgebraic sets arising from positivity in polytope theory including Ehrhart theory and the theory of Minkowski weights. In particular, the PI will investigate computational problems, topology properties, and lifting problems for inequalities from tropical to classical algebraic geometry. The proposed work will promote interactions among various fields of mathematics and advance knowledge in foundations of tropical geometry, real algebraic geometry, and geometric combinatorics. The proposed problems have connections to optimization (low rank matrix completion, nonnegative and sum-of-square polynomials), computational algebra (principal minor assignment problem, discriminants and resultants), and convex geometry and polytope theory (Christoffel–Minkowski problem, weighted Ehrhart theory). 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 will advance US prosperity, welfare, and science by developing efficient and rigorous techniques for solving complex decision-making problems when multiple conflicting objectives must be weighed, the value of potential solutions is uncertain, and error is costly. Such decision-making problems occur in all sectors of the US economy, including health and defense, and are very difficult to solve, both conceptually and algorithmically. This award supports research that investigates a novel formulation of complex, multi-objective decision-making problems under uncertainty aimed at facilitating tradeoffs among multiple objectives while allowing for statistically valid algorithms that achieve high efficiency via novel comparison techniques, recycling data, computer simulation, and parallel computing. Through educational and outreach activities, including providing implementations of the approaches in a free public domain, this award will increase US productivity and help decision makers manage tradeoffs and meet contractual and managerial obligations when solving complex problems under uncertainty. This research will develop a novel approach to large-scale multi-objective simulation optimization that involves placing constraints on all performance measures. The constraints are stochastic in that performance measures can be estimated through stochastic simulation, and subjective in that their thresholds can be adjusted as necessary. Subjective constraints allow for the development of a multi-pass approach that uses feasibility with respect to more strict thresholds to prune inferior systems. To efficiently identify the preferred system with statistical validity, solution procedures that use multiple indifference-zone parameters to reduce conservativeness while avoiding the collection of unnecessary observations for detecting small differences between performance measures and thresholds. For additional efficiency, the new procedures employ green simulation with recycled observations for different thresholds. This approach also avoids direct pairwise comparisons between systems, leading to reduced communication and synchronization among processors and facilitating parallel computing implementations. 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

Astronomers predict that most massive galaxies in the Universe will have merged with at least one other massive galaxy during their lifetime. As supermassive black holes inhabit the centers of massive galaxies, scientists therefore expect to find that a fraction of galaxies will have a pair of black holes at their centers. The orbit of these black holes will slowly decay, and the two black holes will interact with their surroundings and shine brightly. This research team will develop an innovative model of how these dual black holes shine at radio wavelengths. A web interface developed as part of this project will allow anyone to access a database of predicted radio bright black holes and plan observations to search for them. The investigators will also develop a workshop on oral communications skills for graduate students at Georgia Tech. Results of the workshops and lessons-learned will be published. This research team will develop an innovative model of dAGN activity at radio wavelengths and is built on four pillars: (1) the catalog of massive galaxy mergers predicted by the IllustrisTNG cosmological simulations, (2) calculations of the orbital evolution at kpc-scales due to gaseous and stellar dynamical friction of a secondary SMBH in a post-merger galaxy, (3) the relativistic jet power found from simulations of moving black holes through magnetized gas, and (4) first-principles modeling of the radio synchrotron spectrum from the jet cores. By considering different SMBH orbits in each IllustrisTNG merger, the model will generate catalogs of kpc-scale dAGNs as a function of redshift, with each system associated with a specific evolution of the dAGN radio luminosity and spectrum. A web interface developed as part of this project will allow access to the model database and provide actionable predictions on the expected radio source properties from ngVLA surveys of post-merger galaxies. 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 research supported by this NSF Civil, Mechanical and Manufacturing Innovation/UKRI Engineering and Physical Sciences Research Council (CMMI-EPSRC) award aims to advance simulation-based manufacturing process digital twin (DT) technologies by developing a self-organizing DT framework that continuously validates and calibrates the simulator and controls the manufacturing system with minimal human intervention. Despite the recognized importance of simulation-based DT for decision-making under uncertainty, significant barriers exist in its application, particularly in the continuous alignment of simulation models utilizing several key performance indicators observed from the manufacturing system and the need for quick, optimal control responses during contingencies. This research will directly address these challenges by making significant contributions to the mathematical foundations of DTs and enhance the competitiveness of US and UK manufacturers through industry collaborations. Real-time manufacturing DT technology can significantly promote national welfare by enhancing the efficiency, reliability, and resilience of manufacturing processes. This leads to more consistent and higher quality products at a lower cost, benefiting consumers and increasing the competitiveness of domestic industries. The simulation DT technology is applicable to other critical sectors such as healthcare and defense. Therefore, the methodological advancements achieved from this research can also benefit public health and national security. The specific goals of this research are establishing and verifying mathematical and algorithmic frameworks for 1) online validation of the DT with multidimensional multi-epoch data, 2) self-calibration of the DT simulator, 3) optimal control for contingency scenarios, and 4) parallel computing for rapid optimization. For the online validation, a hypothesis test that incorporates multi-dimensional multi-epoch data to detect statistically significant discrepancies between the model-generated and the system KPIs will be created. If the DT simulator fails the hypothesis test, then the online calibrator is automatically triggered. The calibration will be formulated as a simulation optimization problem that minimizes a statistical distance between the distributions of the simulated and system KPIs. To solve this problem efficiently, a new “batch-then-project” Bayesian optimization (BO) algorithm will be established that can efficiently tackle high-dimensional problems. To utilize a calibrated simulator in online contingency responses, the DT requires a simulation optimization algorithm that finds the optimal set of categorical actions efficiently without enumerating all possible combinations. This research will explore embedding actions on a graph to measure the similarity between two sets of actions and exploiting it to make statistical inference on the optimality. To provide a practical solution in real time, all algorithms will be designed to utilize parallel computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project will open one of the last unexplored windows on the Universe: the ultrahigh-energy neutrino window. It promises transformative impact, as detection of neutrinos at these extreme energies will answer some of the most profound questions in astroparticle physics and push the boundaries of our understanding of the Universe. This study will prove the concept for Trinity, a future experiment for air-shower imaging. This advanced neutrino instrument would greatly improve US research infrastructure in this field and help to maintain leadership. The award helps to create next-generation scientific leaders by introducing undergraduate students to frontier research, and by training graduate students and early-career postdoctoral scientists. The work will demonstrate the effectiveness of air-shower imaging with compact Cherenkov telescopes for VHE-UHE-neutrino detection. The Georgia-Tech group has installed and operates a small Cherenkov telescope, the Demonstrator, on Frisco Peak in Utah. The Demonstrator is the most sensitive operating air-shower imaging VHE-UHE-neutrino detector. It images the upward-going particle shower caused by a tau neutrino entering the Earth at a shallow angle and interacts to produce a tau that emerges from the ground and decays in the atmosphere. The technique is known as Earth-skimming. The project expects to: observe the first-ever VHE-UHE neutrinos, prove the long-term stability and reliability of the detection technique, demonstrate remote operation of the telescope, and study background events and advanced analysis techniques. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. 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

Earth’s largest mountain belts and high plateaus are supported by thick, buoyant crust and an anomalous lack of dense lower crust and mantle lithosphere. This project investigates the processes by which the lower crust and mantle lithosphere are removed by detaching and sinking, known as foundering, and the imprint of such processes on the geologic record. This research focuses on the southern Puna plateau, NW Argentina, where multiple lithosphere removal events are hypothesized from geological, geophysical, and geochemical datasets. By generating new geological datasets and numerical modeling results, the project will test the geological evidence and physical feasibility of hypothesized modes of lithosphere removal. The project supports 2 PhD students and at least 7 undergraduate researchers, who will be recruited from underrepresented minority groups, as well as 2 postdoctoral researchers and 3 early-career PIs. The project builds connections between North American and Argentinian scientists via collaboration on the project, running a research field trip with multiple research groups working in the southern Puna plateau, and hosting a numerical modeling short course in Argentina. The themes of the project, including the effects of density on the evolution of mountain belts, serve as the basis for a new annual field trip and retreat to build belonging among students at Utah Tech University (UTU), which is a highly affordable, open-enrollment, undergraduate-serving institution. To test hypothesized foundering mechanisms, this study employs a combination of field and numerical modelling work focusing on the southern Puna plateau, NW Argentina, a case locality for building large mountain belts and plateaus. The Puna plateau preserves a unique sedimentary record of Cenozoic mountain-building, and Miocene lithospheric foundering has long been proposed from geophysical and geochemical datasets. Recent studies suggest foundering as an explanation for anomalous Miocene subsidence/shortening and uplift/extension of the Arizaro and Antofalla Basins, respectively, ~150 km apart. The opposite senses of deformation associated with these potential foundering events suggest that foundering may induce highly diverse modes of deformation in the overlying crust, potentially controlled by the thermal, compositional, or structural state of the crust. This multi-disciplinary project uses geologic mapping, measured stratigraphic sections, and geo- and thermochronology to better constrain the spatial-temporal distribution of deformation and subsidence in the southern Puna plateau, as well as cutting-edge numerical modeling to characterize controls on foundering style or other processes that may have driven deformation in the region. This integrated approach will allow rigorous testing of whether foundering is consistent with observations from the Antofalla and Arizaro Basins, and if so, what crustal parameters control the effects of foundering. 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

Existing medical devices designed for widespread use in daily life introduce an associated challenge of environmental impact, particularly regarding material waste and carbon emissions from the production cycle. Recent advancements in electronic technologies expedite the production of a significantly higher volume of new medical devices. Addressing sustainability issues necessitates a collaborative, multidisciplinary approach and a concerted effort to train the next generation of designers and engineers to prioritize sustainability and innovation. This National Science Foundation Research Traineeship award to the Georgia Institute of Technology will address this need by training master's and doctoral students in advanced sustainable medical devices that are both technologically sophisticated and environmentally responsible, offering traits like reusability and reliability, thus fulfilling a crucial national priority. The traineeship expects to provide a unique and comprehensive training opportunity for one hundred (100) students, including twenty-five (25) funded trainees, by combining multiple disciplines in medical devices, sustainable design, and manufacturing principles; collaborations with clinical partners and accelerators; hands-on experiences with health-related research; and a culture of innovative and translational research. Trainees will learn about a broad spectrum of disciplines, including bioengineering, public policy, physiology, industrial design, interactive computing, and medicine. By bridging the gap that currently exists between sustainability, device technology, biodegradability, and medical science, the traineeship will address the lack of a framework to facilitate interdisciplinary collaboration among engineers, scientists, designers, policy-makers, and clinicians. The traineeship’s major goal is to develop a multidisciplinary curriculum that combines methods from various domains to resolve ongoing challenges in developing reliable and personalized medical devices for healthcare. In partnership with clinical experts and medical product accelerators, the initiative will broaden students' perspectives beyond the current technology-first mindset and reflect the needs of patients and healthcare providers through sustainable technological solutions. Another goal of the traineeship is to attract a more diverse group of students to the interdisciplinary field of healthcare technologies and contribute to promoting diversity in workforce development. The project will develop a new Ph.D. concentration area in smart medical devices within the bioengineering program and a new M.S. degree program in the sustainable development of medical devices embedded in the colleges of engineering, science, and design. Curriculum materials and best practices will be disseminated for other institutions to emulate. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research 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 · 2024-08

Simulation models have greatly enhanced our understanding of various phenomena, such as climate change impacts, weather patterns, the spread of pathogens, and nuclear fusion. These models are essential for advancing clean energy and reaching goals like reducing emissions to net zero. Modern computing environments allow scientists to leverage these models for decision-making. Despite today's advanced computational capabilities, optimizing computational processes with physics-based simulation models involves trade-offs between model fidelity, data utilization, and computational resources. This project aims to analyze the computational resources needed for reliable, data-driven decision-making with physics-based models, focusing on enhancing the design of renewable tidal energy farms. Open-source computer code and simulation output will be created and archived. Through outreach activities, students will learn about the importance of computer-aided decision-making. The project will establish informative estimates of the computational resources required for optimizing physics-based systems, which are challenging infinite-dimensional optimization problems. Specifically, the complexity of deterministic and stochastic optimization problems governed by complex physics-based systems will be analyzed, focusing on those given by partial differential equations. A key aspect is analyzing the accuracy and reliability of low-fidelity and sample-based approximations of deterministic, risk-averse, and chance-constrained optimization problems. The focus is on objective and constraint functions with nonsmooth, nonconvex, and nonconvex dependence on decision variables and uncertain parameters. The project employs tools from high-dimensional statistics and nonsmooth analysis to quantify the generalization properties of sample-based and data-driven solutions. Theoretical findings will be empirically validated through simulations, with computer code and simulation outputs published open-source and archived. The results will be disseminated through research publications in scientific journals and presentations at workshops and conferences. Additionally, one Ph.D. student will be integrated into the research of this project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

In vertebrates, bones serve multiple functions, including organ protection, structural support during locomotion, and mineral homeostasis. Bones are not static structures but are dynamic multiscale tissues, comprised of cells, organic material, and inorganic molecules at the micro-level, transitioning to cortical and cancellous bone at the macro-level. This system continuously responds to local molecular and cellular milieu, physical stress and strain, and distant hormonal influences. Three main cell types dominate the cellular mechanisms governing bone microscale response: osteoblasts (create bone), osteocytes (maintain bone), and osteoclasts (degrade bone). While the gene expression and cellular mechanisms governing bone tissue are well-understood, fundamental principles of dynamic remodeling are only recently being investigated. Hence, the emergence of bone properties from lower-level processes remains an open question. This is particularly intriguing as the collective functions without any cell having knowledge of the global state. In this award, a physician specializing in orthopedic clinical research focusing on bone biology, fracture repair, and arthrodesis healing, and a physicist specializing in individual and collective organism and robot dynamic, will study osteoclasts, multinucleated giant cells. Osteoclasts play a critical role in bone resorption, preparing the system for remodeling. These cells crawl along surfaces, chemically eroding material, and leave tracks that are subsequently filled in by osteoblasts. While knowledge exists about osteoclast development and function, limited research has been conducted to comprehend the physics of osteoclast motility, bone degradation patterns, and how cell heterogeneity and morphology change in response to environmental stimuli and/or affect function. Further, the interactions among these cell collectives remain poorly elucidated. To advance understanding of bone resorption, the investigators will observe the movement of individual and collectives of multinucleated osteoclast cells in controlled laboratory experiments, mimicking bone tissue and various material surfaces. Scientifically, individual and collective dynamics hold significance for researchers in various fields, from active soft matter physics to computer scientists focusing on collective decision-making. Practically, this work forms the basis for deeply understanding bone tissue function and responses to environmental stimuli, offering potential for new translational treatment targets. Osteoporotic fractures, rising with age, contribute to an estimated healthcare cost exceeding 5 trillion dollars annually in the US and Canada. The investigators will connect Orthopedic Surgeons and physicists, fostering exploration and collaboration through invitations to the APS March Meeting, the annual iPoLS SRN meeting, and the Orthopaedic Research Society annual meeting. 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 · 2024-08

This project aims to serve the national interest by enhancing conceptual learning in quantum and semiconductor physics using interactive visualizations and simulations. This Engaged Student Learning, Level 3 project will conduct the first large-scale investigation into the design of educational tools and design features that can significantly improve conceptual understanding in quantum and semiconductor physics in particular, and STEM in general. The project will develop more inclusive tools for a diversity of use cases and assess their utility and effectiveness across different educational environments. While interactive visualization and simulation tools have made progress in mitigating certain student misconceptions, there is a lack of comprehensive studies examining the design efficacy of these tools. Existing research in this area is constrained by both limited size and scope. This project will expand and refine the interactive visualizations and simulation tools previously developed by the team with a focus on aiding the conceptual understanding of quantum mechanics and semiconductor principles among undergraduate students. Additionally, the project will carry out extensive studies on the effectiveness of these tools across diverse educational environments. As a consortium of researchers spanning five diverse universities and colleges, the team will investigate the following research questions: 1) To what extent can such tools change undergraduate students' conceptions of Quantum Mechanics and Semiconductor Physics and 2) How does the design of such tools affect students’ conceptions of these topics? The team will conduct two large-scale mixed-methods controlled studies using a concurrent triangulation design. Both quantitative and qualitative data will be collected and analyzed. Analysis methods include multilevel modeling, repeated measures multivariate analysis of covariance, and qualitative content analysis. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the 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 · 2024-08

The goals of this multi-institutional NSF REU program are the following: i) Offer high-quality research experiences and professional development events that provide participants the skills necessary to succeed at scientific research, ii) implement an application process that fosters a cadre of participants each year reflecting the diversity of the U.S., iii) provide opportunities for REU participants to continue work on their REU research beyond completion of the program, and iv) strengthen partnerships between three local Atlanta HBCU institutions as well as with Georgia State University (GSU). Building upon the previous NSF AOG REU program (NSF 1852040), it is expected that the proposed partnership of Georgia Tech, Spelman College, Morehouse College, Clark Atlanta University, and GSU, along with recruitment of participants across the U.S., will provide an opportunity to recruit and train a diverse group of participants each year of the program. It is hoped that the program will help student participants increase their chance at being accepted within AOG-related graduate school programs, which, in turn, will prepare them to be successful within M.S. and Ph.D. degree AOG career positions and increase diversity within these fields. During each summer of the funding period, 10 REU student participants will work with a faculty mentor who has an active research group in an area of interest to the student. Undergraduates will participate in a diverse array of projects within AOG-related fields, ranging from the atmospheric sciences to climate science, oceanography, geosciences and planetary science. Students will play an integral role in designing their research project, including conducting a literature review, crafting a research question, goal or hypothesis and subsequently investigating this through data collection and analysis, derive conclusions related to their study and present their findings in written and oral form. Throughout the program, REU students will participate within regular group meetings, professional development sessions improving scientific research and communication skills as well as discussion focused on ethics and graduate school planning, and visits to regional private and public-sector organizations with AOG-related interests. Overall, the program will acquaint participants with a broad array of projects, enhance their presentation skills, and provide a perspective of AOG research in a scholarly, entrepreneurial, economic, ethical, and interdisciplinary framework. Furthermore, the program will benefit from shared activities with established REU programs in other disciplines within the College of Sciences at Georgia Tech. At the beginning of the program, as well as during weeks 2, 5 and 10, REU students will be asked to participate in an anonymous survey to provide feedback about their experience to inform REU personnel of successes within the program and where improvement is required. 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 · 2024-08

Partial differential equations (PDE) are widely used to model various problems involving spatial and temporal variables arising in physics, engineering, biology, finance, etc. The aims of the efforts to understand rigorously these mathematical models are twofold. On the one hand, the physical relevance and the validity of these ideal models are established through the comparison between the results from theoretical analysis and the experimental observations. On the other hand, once the meaningfulness of a mathematical model is supported by available experimental data to certain extent, the theoretical studies on these ideal models can provide properties and predictions of the original physical problems that are difficult to obtain from experiments. For physical systems involving temporal evolution, of particular interest are certain structural and asymptotic properties. These include special structures, such as equilibria, periodic and quasi-periodic orbits, chaotic orbits, and their qualitative properties like stability or asymptotic stability. In general, on the one hand, only stable states are physically observable in a system, while the ideal, but unstable, states are hardly observed due to their extremely sensitive dependence on the parameters. On the other hand, unstable states are also very important, in part because they and some of their associated structures serve as the boundaries separating different collections of stable states in a system. In this project, the principal investigator (PI) plans to focus on the local dynamics near steady states in several classical nonlinear PDE systems which belong to the general category of nonlinear waves and incompressible fluids. The complicated nonlinearity poses tremendous challenges in their mathematical analysis. A substantial part of the project is suitable for graduate students and postdocs and provides research training opportunities for these early-career mathematicians. More specifically, the project will study the dynamics of incompressible fluid PDE (inviscid, weakly viscous, or with density stratification) with free surfaces as well as a class of nonlinear Hamiltonian PDE. They are standard models arising in fluids, atmosphere-oceans, nonlinear waves, etc. Their solution flows generate infinite dimensional dynamical systems in function spaces. There has been extensive research on these systems with many important advances in recent years. However, due to the complicated spectra of the linearized problems, the highly nonlinear nature, regularity issues, and the multiple scales in space and time they involve, many questions, including some fundamental ones, are still not well understood. First, the PI will work on the two-dimensional water waves linearized at shear flows, including the bifurcation of instability and the linear inviscid damping for the gravity water waves and the spectra and linear flows of the stratified water waves. The second focus of the project is the nonlinear local dynamics of a class of Hamiltonian PDE including the local invariant manifolds for quasilinear Hamiltonian PDE, where the regularity issue poses a major challenge, and the unfolding bifurcation of small homoclinic type solutions in a singular perturbation framework. The PI will also study a potential flow approximation to weakly viscous water waves including formal justification via detailed multi-scale expansions involving boundary layers followed by rigorous proofs. Understanding and solving these problems, expected to be largely based on their specific mechanical and geometric structures, would result in substantial theoretical advances in these areas and possibly lead to the discovery of new physical and mathematical phenomena in the underlying systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Secure distributed computation has long been a common “playground” for cryptography and distributed computing/systems research. Despite novel ideas flowing in both directions and central questions such as resilience to misbehavior and efficiency being treated by both communities, the difference in focus and the aimed properties have resulted in distinct approaches and considerable gaps in terminology, models, definitions and overall language. This mismatch is ever more present in emerging technologies such as blockchain and decentralized ledgers (DLT). The project’s novelties are: (1) a common blockchain-focused language for cryptography and distributed computing and translations of results to this common language; and (2) the study of feasibility, scalability, and efficiency of both classical distributed-cryptography primitives and blockchain-inspired ones, in models that better capture the challenges and idiosyncrasies of the latter. The project’s impacts are to forment collaborations that will ensure a holistic approach to the modern challenges posed by the above emerging technologies thus avoiding pitfalls that can hinder these technologies’ potential. The study of cryptographic hardness and decentralized trust assumptions can lead to a more flexible yet realistic and secure cyberspace. This project will actively promote an interdisciplinary research agenda focused on these technologies at Purdue, Texas A&M, and Northeastern, and will actively pursue inclusion to computer science research of underrepresented groups in the field. A bit more concretely, the goal of this project is to address the above challenges by (1) creating a framework suitable for expressing foundational and modern questions from both cryptography and distributed computing, without ignoring privacy or computational considerations, a paradigm that is termed distributed cryptography, and theoretical transformations (“compilers”) for importing classical results into this framework, and further extending them under the cryptographic lens; (2) investigating feasibility, scalability, and efficiency of distributed cryptography primitives, such as secure multi-party computation, in models of execution and under assumptions inspired by DLT protocols; and (3) investigate how the paradigm of relying on a sparse resource, which is central in the blockchain literature (e.g., hashing power in proofs of work- and stake in proofs of stake-based protocols) can generically reshape distributed cryptography and allow us to circumvent long-standing impossibility results. 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.