Georgia Tech Research Corporation

NSF Awards · FY 2024 · 2024-08

In recent years, Artificial Intelligence (AI) research has made rapid advances that led to numerous real-world applications. While some researchers explore fairness and bias in AI systems, few address how researchers navigate conflicting ethical issues in how data is trained to create these AI systems. This study will identify and articulate the ethical questions regarding Machine Learning (ML) training methods, emphasizing environmental cost, labor practices, financial cost, and data quality trade-offs when choosing ML training methods in research settings. These research findings will contribute a model for AI researchers to weigh data training methods for responsible AI research. Focusing on these ethical and financial considerations in a research setting will provide tools to evaluate research approaches that serve our nation best and will contribute to training students to consider these trade-offs as they move into industry roles. This study focuses on large language models (LLMs), such as ChatGPT, Llama, and Claude. While many approaches to AI and Natural Language Processing (NLP) rely on supervised ML through training data, LLMs employ pre-training of large-scale models on vast amounts of data collected from the internet. To enable LLMs to grasp the intricacies of language and align their outputs to match human preferences, they undergo pre-training on extensive datasets and fine-tuning on labels generated by digital piecework workers. Our study centers on the production of this fine-tuning data. NLP research is a fast-growing field, with over 5,000 papers published in 2021 and over a 50% increase in research production between 2017 and 2021. LLMs offer an excellent case study because various methods are used to train them - broadly categorized into supervised, unsupervised, and reinforcement learning approaches. These methods are often used in combination, and the choice depends on the specific goals of the model and the available data. The study consists of three primary objectives: 1) identify current practices among researchers of large-scale data sets to train LLMs by conducting interviews and surveys; 2) examine the trade-offs from multiple types of data training methods using comparative studies of (a) LLMs to generate training datasets with or without a human-in-the-loop, (b) digital piecework or paid crowd work (such as Mechanical Turk), and (c) dedicated data workers employed by research groups; and 3) develop ethical models and resources that communicate how to weigh data training methods based on cost, time, quality, diversity, environmental impact, and labor practices for responsible AI training. This project is funded through the ER2 program by the Directorate for Social, Behavioral and Economic 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 · 2024-08

Many electronic and industrial systems require increasingly higher heat removal capacities to accommodate the escalating heat produced by the micro-miniaturization of electronic components. Phase change materials have been commonly employed for thermal management; for example, fluids changing phase from liquid-to-vapor, and/or combinations of multiple fluids that change phases to improve thermal transport. This work proposes to study the use of novel multicomponent fluid mixtures to augment and improve the heat transfer rates. Multicomponent fluid mixtures in this study consist of mixtures of multiple fluids, solid particles that change phase to liquid, and refrigerants that can vaporize. The principal aim of this project is to provide a deep understanding of interactions among multicomponent fluid mixtures through both experimentation and simulations. This can then be used to further enhance the performance of many different types of heat transfer systems. The project will include significant educational activities, including the creation of educational materials for undergraduate and graduate programs using the associated research outcomes. This material will be modified for high school and middle school students through the creation of tutorials and video lectures. The investigation utilizes novel multicomponent fluid mixtures to augment internal convective and phase change heat transfer. The multicomponent fluid mixture refers to a novel mixture of multiple fluids and particles that includes phase change materials (PCMs), such as paraffin wax, suspended in liquid gallium alloys utilizing the stabilization properties of gallium oxide films around the suspended particles, in addition to a high vapor pressure refrigerant at saturation conditions. It is proposed that the gallium alloy properties can be improved by embedding PCM particles, such as paraffin wax that have a high heat capacity and low density. Further performance improvements are proposed through the deposition of a gallium oxide film on the channel wall, thereby increasing the wettability of the gallium alloy and consequently improving its mass transport. The proposed work will (i) develop a new understanding of the stability of various PCM particles/gallium alloy mixtures in a range of concentrations and the important properties of these mixtures; (ii) illuminate complex multi-fluid heat and mass transfer phenomena using liquid metals alongside high vapor pressure fluids; and (iii) illuminate the underlying mechanisms of the interactions in the multicomponent mixture. An Oscillating Heat Pipe (OHP) that is commonly used in thermal management applications will be studied with the proposed multicomponent mixture. The knowledge gained from this project has the potential to dramatically increase the efficiency of heat dissipation and transport in electronic and industrial 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

This study examines the impact of non-science isolation practices on scientific production and collaboration. The research deepens our understanding of how national scientific systems are interconnected with global economic and political dynamics, providing insights on the impact of scientific cooperation across countries and sectors. The study addresses fundamental issues at the intersection of science strategy, international relations, and research productivity including how restrictive measures and geopolitical shifts affect international scientific collaboration, researcher mobility, and resource exchange. The project serves the national interest by promoting the progress of science through a better understanding of the factors that influence global scientific cooperation. The results provide decision makers with empirical evidence to inform strategic decision-making in the governance of scientific activities. This knowledge is crucial for maintaining the United States as a leader in global scientific research and innovation. The findings from this study have broader impacts on national health, prosperity, and welfare by helping to navigate international scientific collaboration in an increasingly complex geopolitical landscape. The researchers employ quasi-experimental designs utilizing three recent global events. These events serve as cases to examine the causal impact of disengagement from the integrated global scientific community on scientific development. The study will use causal inference techniques, specifically the synthetic control method and difference-in-differences analysis. These methods are applied to investigate changes in scientific publication production and the exchange of scientific capital, including funding resources and researcher mobility. To address potential biases in international databases, the project constructs a comprehensive dataset incorporating publication records from both international and national sources. By using causal inference frameworks, the researchers offer nuanced insights into the consequences of withdrawing from the integrated scientific community while mitigating the influence of other factors. 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

Modern artificial intelligence (AI) and machine learning (ML) systems are trained using massive datasets and complex models combined with optimization algorithms. Traditional "greedy" methods, which make incremental improvements at each step, often fall short in both efficiency and adaptability when faced with problems at this scale. This project proposes a novel framework for algorithm design based on the Hamiltonian dynamics, a fundamental concept in physics and mathematics that uses the conservation principles to describe the interaction of multiple objects. Such dynamics appear naturally in many branches of computational sciences but are rarely used as a fundamental principle in algorithm design. Motivated by emerging challenges in ML, this project aims to develop a systematic methodology that leverages Hamiltonian conservation to solve problems in optimization, random sampling, and game theory. This project has the potential to revolutionize our understanding of computational and statistical problems by introducing a new class of algorithmic principles for training modern ML systems. This project will also advance the curricula for algorithms in computer science and electrical engineering, with unique training opportunities for undergraduates and graduate students, the development of open-source software, and a dissemination of ideas via joint workshops. This project will explore a framework called “the LCP scheme”, which stands for Lift, Conserve, and Project. This proceeds by taking a parameterized decision space, appropriately lifting the problem to incorporate additional variables, applying the conservation property of the Hamiltonian dynamics to update the problem state in the augmented parameter space, and finally projecting the state back into the original space. This scheme provides a fresh perspective for analyzing several known algorithms, and developing new ones, in the domains of optimization and random sampling, as well as to understand the behavior of players in multi-agent systems. This project will develop a robust algorithmic complexity theory for implementing the continuous-time Hamiltonian dynamics as discrete-time sequential procedures with an emphasis on the large-scale modern applications. By introducing concepts such as invariance, conservation, and the principle of least action, this project will provide a more nuanced view of the state evolution of computational objects that can help overcome many limitations of the standard algorithm design paradigm. 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 year 2023 has witnessed the hottest days on Earth since 1940, highlighting the urgency of the fight against climate change and excessive carbon emissions. In this mission, motors and generators, or collectively electric machines (EMs), play a pivotal role. Through EMs, over 90% of global electricity is generated, 45% of which is turned into mechanical work. EMs are at the heart of electric vehicles (EVs), wind energy generation, and various industrial processes, propelling these essential applications forward. To push EM performance boundaries, it is crucial to explore the multiphysics design space, which encompasses the interplay of various physical disciplines, such as electrodynamics, heat transfer, and structural mechanics. The multiphysics design space is confined by EM topologies, i.e., arrangements of constituent parts in EMs including steel, copper, magnets, etc. A natural question to ask is: what are the best arrangements of these constituent parts? The proposed research is poised to systematically answer this fundamental question and accelerate the exploration of high-performance and highly sustainable EMs. Parallel to the research, the PI’s education goal is to systematically foster diverse multiphysics designers, including those who are underrepresented. The PI’s education activities will include constructing a website-based learning platform named TENSOR to create an inclusive multiphysics learning hub, implementing a duality and analogy-based multiphysics education technique for K-12, undergraduate, and graduate students, and offering an open-access new EM design course to the frontier EM workforce. In the existing design paradigm, new EM topologies are often conceived by designers and the conception relies on their intuition, resulting in sporadically revealing new design space and corresponding performance space. To overcome the limitations of this intuition-based design paradigm, the overarching goal of this CAREER project is to pioneer a new design paradigm — multiphysics synthesis which realizes guided exploration of multiphysics design space for EMs using tensorial analysis. To achieve this goal, three research thrusts are planned. First, primitive EMs, governed by electrodynamics and expressed in hyperdimensional space, will be used as starting points to derive new topologies. Second, the hyperdimensional models of EMs will be embodied in the 3D space for performance evaluation and optimization. Third, the methodologies in the first two thrusts will be generalized to incorporate physics beyond electrodynamics and fulfill multiphysics synthesis. In all three thrusts, tensorial analysis originated from mathematics (geometry) and theoretical physics (relativity theory) will be applied. 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

Per- and poly-fluoroalkyl substances (PFAS) are a group of over 12,000 artificial chemicals. Because of their excellent stability, PFAS are widely used in commercial products and industry. Unfortunately, widespread PFAS use has resulted in environmental contamination. This problem is made worse by the extreme resistance of PFAS to breakdown. For this reason, PFAS are sometimes referred to as “forever chemicals.” It is estimated that over 80% of drinking water sources in the USA are contaminated by PFAS at concentrations potentially harmful to human health. The magnitude of this public health issue has led to research focused on developing effective approaches to treat PFAS contamination. Electrochemical oxidation (EO) shows promise as an efficient and cost-effective method for the destruction of PFAS in water. In EO, the transport of PFAS from bulk water to the reaction surface (the anode) limits the rate at which PFAS can be destroyed. Modifying the anode with thin tips on the surface creates a locally enhanced electric field treatment (LEEFT) that increases the electric field strength thousands of times. While LEEFT can increase electric-field driven PFAS transport (and thus greatly enhance the efficiency and rate of PFAS destruction), there are significant knowledge gaps regarding PFAS electromigration and destruction that prevent optimization of the process. This project will address these knowledge gaps by investigating the destruction of various PFAS on anodes with different structures under different electric field strengths. Successful completion of this project has great potential to benefit society through the development of LEEFT technology to improve PFAS destruction with significantly reduced cost. Additional benefits to society result from training and outreach to increase scientific literacy and help develop the Nation’s STEM workforce. Although EO is among the most efficient and cost-effective methods for PFAS degradation, the approach is limited by suboptimal degradation efficiency. Improving total PFAS mass transport to the anode has been proven effective to achieve higher PFAS degradation efficiencies. Although total mass transport is the sum of diffusion and electromigration, researchers generally seek to optimize diffusion while ignoring the contribution of electromigration due to the typically low electric field strength under most operational conditions. LEEFT can significantly enhance electric field strengths by several orders of magnitude under low voltages, creating electromigration flux comparable to or surpassing that of diffusion. The goal of this project is to systematically study the influence of electric field strength on PFAS electromigration, EO degradation of PFAS, and the combined efficacy of LEEFT in enhancing electromigration and degradation. The degradation of various PFAS by Ti4O7, anodes with different surface morphologies will be investigated. A feature of this project is the focus on addressing environmentally-relevant PFAS concentrations ranging from nanogram to milligram per liter. The effects of background anion concentrations on PFAS destruction will be assessed to evaluate the treatability of this process. Successful completion of this project will facilitate translation of research outcomes into practical applications for PFAS treatment. New knowledge gained from this project will be integrated into summer programs tailored for K-12 students that focus on exploring and addressing complex societal challenges with significant 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 · 2024-08

Tissues are social cellular communities. Decoding how distinct cells coordinate their internal molecules and how their interactions give rise to structural tissue shapes is a vital task in informing our understanding of health and disease. Emerging molecular mapping methods have cataloged the chemical maps of individual cells in tissues. The project will develop computerized models of transcript, protein, and metabolite locations across multiple length scales using geometrical rules and data fusion workflows. The project will provide open-source tools for enriching biological insights in tissue images and will create platforms for integrating these concepts as part of immersive educational outreach activities. Opportunities for middle school and high school students, along with the teachers, will be provided to participate in hands-on and digital research in mathematical tissue biology. Single cell, spatially-resolved 'omics methods have revolutionized our understanding of how tissue composition is altered in the progression between states, such as progressing from health to disease. Machine learning methods have been critical to the rapid advances researchers have made in the spatial bioinformatics field. The need for the predictive use of biological maps using graph-based and latent space representation is increasingly recognized as key to our ability to decode tissue structure and function relationships. The goal of the project is to map and interpret hidden tissue features using open-source learning algorithms in image-based spatial-omics data. The specific aims are to 1) Design a cross-scale graph-learning model from subcellular protein interactomics and microstructural tissue topographies for predicting signaling organization and cell communication; 2) Design a cross-modality variational autoencoder model of joint proteo-metabolomics in single cells for dissecting tissue chemical variances in spatial metabolomics and proteomics data. This research will characterize the molecular and structural differences across subcellular and tissue architectures in normal and aberrant phenotypes identified from spatial multi-omics data. The results of the project can be found at the lab website: https://www.coskunlab.org/. 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 Research Advanced by Interdisciplinary Science and Engineering (RAISE) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. The global photovoltaic industry is expected to surpass $300 billion of new investment by 2030. The U.S. is actively pursuing solar manufacturing for advanced technologies and perovskite solar cells are one of the most promising candidates to achieve large scale deployment at low capital expenditures. While the progress has been phenomenal and commercialization efforts are currently underway, there is an opportunity to design and fabricate perovskite solar cell materials that are economically feasible and environmentally sustainable in the end-of-life. In this project, the team will investigate how to recover, reuse, and recycle the components of metal halide perovskite (MHPs) from all layers of the device. Without a clear understanding of the fundamental chemo-mechanical interactions and their implications to cost and environment of end-of-life phase, manufacturers will not be able to design devices that are truly sustainable from cradle-to-grave. Therefore, the primary objective of this project is to develop recyclable perovskite solar cells with a minimal environmental impact and low production costs. This research program holds significance for society as it establishes a framework for the recovery and recycling of solar cell materials, enabling large-scale deployment of these cost-effective technologies in an environmentally sustainable manner. Furthermore, it contributes to the education and training of the next generation of industry professionals. The focus of this RAISE project is to understand fundamental aspects of interfaces that will enable the development perovskite solar cells that can be recycled at the lowest economic and environmental cost. Chemical and mechanical interfacial interactions will drive the enviro-economic analysis to make an interdisciplinary RAISE program. The knowledge imparted from studying these interfaces can be used to guide the recycling process of these materials. The team will converge to develop the recycling of the devices with different materials and architectures, guided by fundamental questions and hypotheses. These questions include: 1) What are the chemical reactions that occur at interfaces, and can we prevent or reverse them? 2) How can we selectively remove different layers through solvent engineering? 3) How does interfacial chemistry affect layer delamination, and can we control them for improvements in recyclability? 4) What are the processing conditions (including chemical and mechanical) that will enable the least environmental impact and lowest cost? The team comprises experts in thin film fabrication, characterization, device design, fabrication, testing, mechanical analysis, and the assessment of the environmental and economic impact of solar cells. The team will investigate the recyclability and environmental consequences of different cell layers. Critical challenges in this field include: (i) Understanding the chemical and mechanical behaviors at interfaces in perovskite solar cells during device testing under electrical bias. (ii) Identifying environmentally friendly materials for recycling. (iii) Providing fundamental science training to students, preparing them for careers in the solar cell industry. This project will enable materials design with a focus on sustainability. 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

A network telescope is a research infrastructure also used for cybersecurity operations. It monitors traffic reaching Internet address space that is not assigned to any hosts. This traffic is therefore unsolicited—a sort of pollution—and is constituted of an evolving mix of diverse traffic components originating from across the whole Internet. For more than two decades, network telescope instrumentation has enabled research breakthroughs by allowing global visibility into a wide range of Internet phenomena: from the automated spread of malicious software such as Internet viruses to Internet blackouts. Measurement and analysis of such macroscopic phenomena are of key relevance for the security and reliability of the Internet infrastructure but also provide data and inspire progress in interdisciplinary studies. However, due to the increasing scarcity of IPv4 address space, the size of telescopes has been progressively eroding over the years, to the point that some organizations stopped operating them or reduced their size, which lessens their research and educational utility. Another new issue with this research infrastructure is that malicious actors have been learning to “blacklist” network telescopes and can avoid them when scanning the Internet. A novel methodology, called dynamic network telescopes, overcomes these issues. A dynamic telescope is based on deploying programmable switches at the border of the organization’s network to continuously discover utilization of internal space and adaptively adjust the scope of traffic capture. This project deploys the very first implementation of such novel solution at the Merit network telescope, one of the largest network telescopes available to the research community. Merit is an independent nonprofit governed by Michigan’s public universities, which owns and operates America’s longest-running regional research and education network. In addition, through workshops and collaborations this project engages the research community to discuss the new research opportunities and benefits observed through its deployment at Merit and to identify strategies to extend its deployment to other research and education organizations. 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

To effectively operate in a complex world that is designed by and for humans, robots require reliable and adaptable dexterous manipulation skills. Indeed, such skills will enable robots to revolutionize a wide range of domains, such as agriculture, assistive robotics, manufacturing, warehouse automation, robotic surgery, military, and consumer robotics. While modern deep learning-based approaches to dexterous manipulation are starting to demonstrate impressive capabilities, they often rely on significant technical expertise and require vast amounts of data and computing resources that are prohibitive outside of well-resourced laboratories. This Faculty Early Career Development (CAREER) project aims to develop efficient computational methods capable of learning robust dexterous manipulation skills without relying on significant user expertise or vast amounts of data and computing resources. To this end, this project will develop innovative and efficient approaches to learn reliable dexterous manipulation skills that offer significant improvements in three important dimensions: efficiency, self-sufficiency, and reliability. As such, this project will reduce technical and financial barriers to entry and enable the wide-spread adoption of dexterous manipulation in several critical domains. Further, the project will deliver insights and open-source technologies with broad relevance to many robotics problems involving high-dimensional systems and highly nonlinear dynamics (e.g., legged locomotion). The project includes a multi-faceted education and outreach program to educate high school students in some of the most rapidly-growing areas of science and expose them to careers in research and academia. This project will also establish and strengthen ties to neighboring Historically Black Colleges and Universities and Minority Serving Institutions via a shared seminar series and a student research exchange program. Despite decades of rigorous development of control methods and significant recent strides fueled by deep learning, achieving reliable autonomous dexterous manipulation remains challenging, with human-level dexterity still an elusive target. This state of affairs is likely due to three often-overlooked aspects of existing methods: i) high barriers to entry due to demands for expensive computational resources and annotated data, ii) inability to handle new tasks without relying on significant user expertise (e.g., for reward or controller design, hyperparameter tuning, and data annotation), and iii) unreliable behaviors due to inscrutable and unpredictable learned policies. The overall objective of this project is to enable robots to learn reliable dexterous manipulation skills without relying on significant human effort and computational resources. The project will tackle its objective’s fundamental challenges by combining the computational efficiency and reliability of operator theoretic tools from dynamical systems theory with the self-sufficiency and expressivity of unsupervised representation learning. The project will develop algorithms to i) learn dexterous manipulation skills from observations, ii) learn multi-sensory representations and models from self-guided play, and iii) analyze and guarantee learned manipulation skills. 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 · 2024-07

The Networking Technology and Systems Early-Career Investigators (NeTS-ECI) Workshop will provide NeTS researchers an opportunity to understand, engage with, and address the challenges associated with developing and executing a research agenda as a new Principal Investigator (PI) in the domain of computer and information networking. NeTS-ECI allows early-career researchers to interact with their peers as well as leaders in the space, in interactive discussions and hands-on breakout activities aimed at developing all aspects of their career from developing research agendas to advising students to long-horizon research success. Each participant will have the opportunity to provide research materials and a short research pitch which will be presented at the workshop and discussed with their peers and mentors with expertise in the space. We expect the workshop will facilitate collaborative network, collaborations, and professional development. The output of the workshop will be a report summarizing the presentations, breakouts, and discussions. The workshop has historically, and will continue, to play an important role in developing early-career researchers in computer and information networking. 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 · 2024-07

Dispersive equations are ubiquitous in nature, arising in areas such as water waves, optics, lasers, ferromagnetism, particle physics, general relativity, nonlinear elasticity, and many others. Examples are nonlinear Schrödinger equations that govern Bose–Einstein condensates, a fascinating phenomenon predicted by quantum statistical mechanics, for which the 2001 Nobel Prize in Physics was awarded following experimental verification. Solitons, or coherent solitary waves, are an extraordinary and still mysterious feature of solutions to dispersive equations. The project’s overarching goal is to establish (in)stability results for solitons, further study their dynamics even in the presence of (in)elastic collisions and blow-up phenomena and understand the soliton resolution conjecture for general solutions. The project provides research training opportunities for graduate students. Combining techniques from partial differential equations (PDE), harmonic analysis, asymptotic analysis, dynamical systems, and spectral theory, this project explores qualitative descriptions of the dynamics of dispersive waves from three distinct perspectives. The first objective is to understand the dynamics of multi-solitons in dispersive equations, focusing on their stability, uniqueness, and (in)elastic collisions. The second objective is to examine the asymptotic stability of (topological) solitons under the influence of long-range scattering and internal modes. The third objective is to utilize integrable structures to investigate the dynamics of physically important integrable systems and their perturbations. 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 · 2024-07

Over the past few decades, the study of complex networks has evolved into an important and dynamic field of research due to the ubiquity of relational data. Statistics plays an increasingly significant role in network analysis because it provides a toolbox for extracting information from noisy network data. Statistical inferential methods have been successfully applied to analyze a broad range of real-world networks, including social, biological, and technological networks. The overall objective of this research is to further advance the theory and methodology for statistical inference with network data. By modeling large-scale networks as random graphs, this research will develop novel algorithmic techniques and analytic tools for inferential tasks on networks. Furthermore, this project will provide research opportunities to undergraduate and graduate students with diverse backgrounds, broadening their participation in interdisciplinary research through summer programs. By integrating research topics with teaching, the PI will also develop innovative statistics curricula that foster students' interest in data science. The research will focus on the following aspects of statistical inference on random graphs. First, to analyze networks with node attributes, the project will study a class of random geometric graphs, as well as associated detection and recovery problems. Second, a major computational challenge in network analysis lies in the recovery of hidden combinatorial structures. The project will address a variety of such combinatorial structural learning problems, such as geometric graph matching and vertex ordering in nonparametric models. Third, while many real-world networks are hypergraphs, the tools and results are relatively limited, and this project will fill the gap by developing new models and methods for analyzing random hypergraphs. For each of these problems, the research will follow a principled approach to characterize the information-theoretic and computational limits. The project will also develop and implement novel efficient algorithms with provable guarantees, including combinatorial methods based on subgraph counts and improved spectral methods. In summary, the research will substantially forward the development of statistical methods for random graphs, leading to long-term advances in our understanding of network data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

With the support of the Chemistry of Life Processes (CLP) program in the Division of Chemistry, Professors Shina Kamerlin of Georgia Institute of Technology, Sean Johnson and Alvan Hengge of Utah State University are studying the evolution of archaeal protein tyrosine phosphatases (PTPs). PTPs are a family of enzymes that play crucial roles in regulating cellular signaling processes. A characteristic feature of these enzymes is a flexible region that plays an important role in controlling catalytic activity. However, it is not well understood how the flexibility of this region is regulated. The proposed experiments will explore how these motions are achieved in archaeal PTPs and evolutionary ancestors. The proposed work will shed light on the role of enzyme dynamics in evolution, and will ultimately impact multiple fields from drug discovery to protein engineering. This pursuit allows graduate students to acquire specialized training in computational biophysics, structural biology, enzyme biochemistry, and NMR spectroscopy. The project includes outreach and mentorship activities to increase the participation of women and other underrepresented students in STEM. Further, the project endeavors to bring science to the general public through participation in festivals, lecture series, and radio broadcasts. This research project seeks to quantitatively characterize the evolution of loop motion and allostery in archaeal protein tyrosine phosphatases, by using advanced biomolecular simulations, intimately coupled with X-ray crystallography, kinetic characterization, and NMR spectroscopy. This will be achieved through characterization of both several extant PTPs, as well as a range of ancestral archaeal PTPs predicted from ancestral sequence reconstruction. Comparison of these enzymes to human and bacterial PTPs will shed light into the factors governing loop motion in PTPs, and how it has changed over evolutionary time. This provides important fundamental insight into enzyme evolution more broadly, as well as identifying features that can be exploited for protein engineering. 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 · 2024-07

This Level 3 Engaged Student Learning project aims to serve the national interest by creating environments in engineering design courses, which are pivotal for persistence in engineering. By implementing evidence-based teaching practices in engineering design courses from first year (cornerstone) to final senior design projects (capstone), the project aims to improve retention of engineers. While more individuals from these communities are enrolling in engineering programs, students often face unwelcoming academic environments. As such, this project seeks to address a critical need in the engineering community: developing and validating scalable teaming models that foster success in undergraduate engineering communities for all students. In order to achieve the project goals, three aims are proposed: 1) identify the impact of INTEGRAL training materials on students’ persistence in engineering and their ability to develop collaborative teams across varying university settings; 2) foster instructors’ abilities to facilitate teaming through a validated train-the-trainer program, and; 3) increase sustainability by identifying what factors impede or enhance effective implementation in different university settings. The methods used in this project include both short-term and long-term components in collaboration with external evaluations. The mixed-methods approach is intended to lead to richly contextualized and generalizable data sets, transforming understanding of learning environments in STEM. The implementation and evaluation of the project's teaming educational practices seeks to impact 29,000 students and 140 faculty members across 12 campuses from two public universities. The systems focus (student, faculty, campus, and university lenses) are likely to lead to scientific advancements in how educators prepare, train, and implement teaming practices across different educational levels and institutions. 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 · 2024-07

When water from a kitchen faucet splashes at the bottom of a sink, it's sometimes possible to observe a circular ridge where the splashing water abruptly changes thickness. This ridge is called a hydraulic jump. This seemingly mundane and innocuous behavior may, in fact, be key to understanding why it is often difficult for modern, high power electronic devices to keep their cool. To unravel this enigma, spaceflight experiments, which are free of the confounding effects of gravity, will be performed on a simple model of an important class of cooling devices known as heat pipes. Should, as expected, the mechanism of hydraulic jumps be observed and characterized in this setting, this will set the stage for new approaches for developing novel methods of cooling high power applications such as CPUs/GPUs, electric vehicles, and large-scale energy storage facilities for solar and wind farms. Additionally, project researchers will deliver non-technical lectures on the proposed work to students and to the general public, with the goal of exposing a broader audience to the excitement of cutting-edge research in science and engineering. The proposed research aims to resolve a decade-old fluid dynamics puzzle involving driven free-surface flows in confined geometries. Prior experiments observed unexpected liquid flooding at the hot end of containers under high heat, contradicting models predicting drying due to evaporation and thermos-capillarity. This unresolved mystery impedes effective thermal and fluid management device design. Inertial effects, previously neglected, are hypothesized to be critical. Specifically, high-speed liquid jets and hydraulic jumps in driven corner flows with free surfaces may be the underlying mechanism causing hot-end flooding. The research will conduct flight experiments, measuring fluid velocities under conditions where, in past studies, flooding was observed. Both flight and ground-based measurements of velocimetry, temperature, pressure, and meniscus shape will guide and validate the development of theoretical and numerical models of two-phase (liquid and vapor) flow. The broader impacts include advancements in thermal and fluid management in diverse applications. Moreover, the project will promote education and diversity by training new scientists, encouraging underrepresented groups' participation, and engaging the public through outreach efforts. 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 · 2024-07

This project investigates the social and ethical impacts of Emotion Artificial Intelligence (AI), in which AI is used to try to infer people's emotions; for example, if given a picture of a smiling face, or text that contains cheerful, positive words, an Emotion AI algorithm might predict 'happy'. Systems using Emotion AI are becoming more commonly used to assess people in everyday life contexts from work and education to medicine and criminal justice. However, Emotion AI algorithms pose risks because even the best systems often make errors; algorithms may also be systematically skewed if they are built using datasets that have discrepancies in the amount or types of data they contain about different groups of people, or in the labels attached to that data. This project's goal is to bring everyday people, particularly those who might be most vulnerable to errors and biases, into the conversation about when, and how, it is appropriate to use Emotion AI techniques. To do this the team will conduct a series of workshops and design activities with diverse teens and young adults, as well as technology workers, that will (a) inform designers of Emotion AI-based systems about what people expect and fear, and (b) increase AI literacy for both the workshop participants and their communities. This project will document the opinions, hopes, fears, and future visions that workshop participants share about Emotion AI. Through the research, the project aims to support education, diversity, and helping change the future of Emotion AI to benefit society. This reimagining of the use of Emotion AI will be driven by two main methodological approaches: critical AI literacy and design futuring. Critical AI literacy is a set of skills that enables people to effectively use AI while also considering AI's benefits and risks. Design futuring is a method for critically considering ethics and unanticipated consequences of technology through creating alternative futures in which the technology has different, and not necessarily positive, effects on society. The project team will host workshops with teens and young adults in the Atlanta area to develop a library of both critical AI literacy materials and Emotion AI design futures, grounded specifically in approaches to Emotion AI based on Facial Expression Analysis, in which computer vision techniques are used to identify faces and apply the Facial Action Coding System approach that analyzes facial sub-expressions to infer emotions. These materials will then be used as prompts for discussions with developers that create Emotion AI-based systems about how they weigh benefits and risks in their work. The work will advance both the area of Emotion AI by integrating more diverse perspectives about it, as well as the underlying methods of design futuring by developing approaches to making futuring more diverse and participatory. 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 · 2024-07

This award supports the "New Messengers and New Physics" initiative, identified as a priority area by the Decadal Survey on Astronomy and Astrophysics 2020. Gravitational waves, the latest breakthrough in physics, have become accessible for study through NSF's Laser Interferometer Gravitational-wave Observatory (LIGO). Since the landmark detection of gravitational waves from a binary black hole merger, the LIGO and Virgo detectors have observed hundreds of compact binary coalescences including binary black holes, binary neutron stars, and neutron star black holes. These observations facilitate extensive research, advancing our knowledge in fundamental physics, astrophysics, and cosmology. This award will advance multi-messenger astrophysics by increasing the number of joint observations and enhancing methods for joint analysis of gravitational wave and electromagnetic data, potentially leading to the next significant breakthrough in the field. The award will support summer undergraduate research fellows through Georgia Tech's NSF REU program and, in collaboration with other astrophysics faculty, develop an astrophysics curriculum for the Vertically Integrated Project, a program that provides opportunities for undergraduates to participate in research activities as part of a team. Additionally, the PI will organize astrophysics booths at the annual Atlanta Science Festival to engage the community with the latest findings and the excitement of gravitational-wave astronomy. Multi-messenger observations of compact binary mergers can significantly enhance our understanding of neutron star matter, and the physics of gamma-ray bursts and kilonovae, and serve as a tool for measuring the Universe's expansion and testing general relativity. Facilitating these observations and conducting low-latency analyses are top priorities as outlined in the LIGO-Virgo-KAGRA Observational Science and Operations White Papers and the LIGO Scientific Collaboration Program. This project focuses on the crucial tasks needed to achieve these high-priority goals. Studies indicate that during the fifth observing run, with the A+ upgrades to the LIGO detectors scheduled to commence before the project's end, LIGO will likely detect several tens of binary neutron stars and neutron star–black holes annually, with a few detectable before they merge. With the current and forthcoming wide-field transient facilities, it will be possible to promptly observe these events electromagnetically. This award is vital to fully leverage the scientific potential of already funded observatories. 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 · 2024-07

Data science and machine learning have transformed modern science, engineering, and business. One of the pillars of modern-day machine-learning technology is mathematical optimization, which is the methodology that drives the process of learning from available and/or real-time generated data. Unfortunately, however, despite the successes of certain optimization techniques, large-scale learning remains extremely expensive in terms of time and energy, which puts the ability to train machines to perform certain fundamental tasks exclusively in the hands of those with access to extreme-scale supercomputing facilities. A significant deficiency of many contemporary techniques is that they "launch" an algorithm with a prescribed "trajectory," despite the fact that the actual trajectory that the algorithm will follow depends on unknown factors. Contemporary optimization techniques for machine learning essentially account for this by "tuning" algorithmic parameters, which means that the target is typically only hit after numerous expensive misses. Another significant deficiency of contemporary techniques is the restrictive set of assumptions often made about the optimization being performed, which typically includes the assumption that the machine-learning model is being trained with uncorrupted data. Modern real-world applications are far more complex. This project will explore the design and analysis of adaptive ("self-tuning") optimization techniques for machine learning and related topics. One goal is to produce adaptive algorithms with rigorous guarantees that can avoid the extreme amounts of wasteful computation that are required by contemporary algorithms for parameter tuning. Another goal is to extend the use of these algorithms to settings with imperfect data/information, which may be due to biased function information, corrupted data, or novel techniques for approximating the objective. Finally, many applications ultimately require the learning process or model to satisfy some explicit or implicit constraints. Optimization methods for such machine-learning applications are still in their infancy, largely due to their more complicated nature and further dependence on algorithmic parameters. This project aims to design a unified framework for analyzing adaptive stochastic optimization methods that will offer researchers and practitioners a set of easy-to-use tools for designing next-generation algorithms for cutting-edge 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 · 2024-07

This proposal aims to address the challenges of achieving optimal nonlinear control for dynamical systems in stochastic environments considering applications such as robots, aircraft, and automated manufacturing processes. Traditional methods to control these systems either provide sub-optimal solutions, lack rigorous analysis, or require a large amount of computation that could result in intractable solutions. Our research introduces a novel approach called spectral dynamic embedding, which aims to create efficient and reliable control algorithms suitable for a wide range of nonlinear systems. These methods will be tested in both virtual simulation environments and real-world robotic labs. The practical algorithms developed through this research can be applied to various applications, enhancing technologies in robotics, aerospace, manufacturing, energy, and beyond. The team will collaborate with industry partners to broaden the impact on society. Additionally, the project will involve students at various levels in cutting-edge research and experimentation, and also develop K-12 educational materials to inspire the next generation of scientists and engineers. The key innovation of this research lies in the unified spectral dynamic embedding approach, which reformulates the system dynamics in stochastic nonlinear control linearly to a nonlinear spectral feature space, rather than linearizing the dynamic model. This novel perspective allows for tractable dynamic programming or linear programming to solve the optimal policy and enables rigorous analysis of control optimality for general stochastic nonlinear dynamics. It also facilitates a simple learning procedure and computationally tractable exploration to accelerate data collection, both grounded in solid theoretical foundations. The research will develop computationally efficient methods for stochastic nonlinear control with either known or unknown models and will ensure the robustness and safety of the system. This interdisciplinary effort combines expertise in online control, reinforcement learning, optimization, statistical learning, and reproducing kernel Hilbert space to tackle this longstanding problem, aiming for transformative impacts on both control theory and machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

The number of devices connected to the internet is estimated to have reached three times the world population in 2023. Machine-to-machine (M2M) connections, also known as internet-of-things (IoT), are growing rapidly. These include wearables for health monitoring, video surveillance, manufacturing, and tracking devices. M2M devices are estimated to have reached 14.7 billion in 2023 with a staggering 4.4 billion being mobile. The M2M is the fastest growing mobile category -- even faster than smartphone category. One of the biggest questions related to this growth is: How will mobile M2M devices be powered in the future? There is currently no solid technological solution to address the power needs for mobile IoT devices. The objective of this project is to explore and study an innovative solution to power future mobile M2M devices. The proposed innovative microsystem enables wireless power transfer missions to sensors (chemical, physical or biological) in remote regions operating under low- or no-power conditions. The microsystem is comprised of an antenna array integrated with a resonator on one substrate. Therefore, the device offers a unique platform for studying the direct coupling of electromagnetic and acoustic waves. Two types of sensing mechanisms are made possible with the new microsystem. 1) IoT sensing powered by RF energy harvester: Examples include intruder detection, inventory tracking, wearables, and monitoring patients. 2) Direct detection using on-board antenna or resonator: Examples include angle of arrival measurement, wireless sensor networks, radar, and remote sensing. Enabled by direct coupling of an antenna array with a resonant piezoelectric transformer (RPT), the new microsystem architecture pursued in this project will potentially replace the rectifying antennas (rectennas) that have dominated the RF energy harvesting research landscape for more than 50 years but still have not achieved high efficiency at low RF input power range (less than −30 dBm or 1 microwatt). On the other hand, advances in micro- and nano-mechanical devices, have resulted in the realization of high-Q acoustic resonators (Q>1000) operating in the 1-10 GHz frequency band. As opposed to a conventional rectenna architecture in which an antenna is coupled to rectifying diodes, in the proposed architecture, energy is coupled from the electromagnetic domain to the mechanical domain using an antenna array, transmission line, and an array of high-Q RPT. The new microsystem will be the first demonstration of direct coupling of electromagnetic energy into the mechanical domain for energy harvesting purposes. Once the energy is transferred efficiently from free space to a resonator, it is processed in the mechanical domain for unique advantages. Specifically, high passive voltage gain (on the order of 100) can be achieved using resonators of high electromechanical coupling coefficient. As a result of this voltage amplification, RF rectifying diodes are properly biased at higher voltage to operate efficiently. Therefore, RF energy harvesting with high efficiency under low power condition can be achieved. In addition, it can lead to energy conversion in a wide frequency band by coupling two or more efficient RPTs with asymmetric ports that result in a transformer filter with a significant boost in the output voltage to enable much higher efficiency in RF rectifying diodes placed after the RPT transformer filter. 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 · 2024-06

Rapid advances in machine learning are driving the design of hardware to support the increased capabilities of modern machines with human-like intelligence. However, this comes with greatly increased complexity of hardware and a large carbon footprint for artificial intelligence (AI) applications such as for natural language processing. This has fueled design trends towards the use of low supply voltages, novel aggressively scaled devices and mixed analog-digital signal processing. The mix of these trends has made the underlying AI hardware vulnerable to operational uncertainties stemming from manufacturing imperfections and noise-induced transient errors. Such uncertainties can cause malfunction of AI driven applications with catastrophic consequences for safety-critical systems such as for transportation and robotics. Consequently, the goal of this project is to develop fundamental algorithms and infrastructure that allow error-resilient operation of AI systems under the above threats across diverse operating conditions, without significant impact on system cost and complexity. This will advance the delivery of highly dependable AI driven systems, so essential for rapid societal adoption of AI as well as for national defense. The project will support inclusive education of graduate and undergraduate students, development of educational and experimental infrastructure, and technology transfer to industry. To address the uncertainties above, the research team aims to develop methods for efficient post-manufacture testing and tuning of deep neural networks (DNNs) with energy-efficient analog crossbar arrays that have been shown in prior research to be vulnerable to manufacturing process variations, resulting in significant drop in DNN performance. The underlying test methods that target manufacturing variability will also be very effective in detecting hard defects such as electrical shorts and opens. Testing and post-manufacture tuning algorithms that offer significant yield improvement over the state of the art will be developed, with tuning times of the order of seconds as opposed to hours for current techniques, reducing device manufacturing and test cost. In parallel, algorithms for on-line error detection, diagnosis and correction of radiation and noise-induced transient soft errors in digital and analog accelerators for novel Artificial Intelligence computing paradigms will also be developed. The goal is to develop scalable error resilience frameworks for a wide range of learning abstractions including recurrent neural networks, transformers, reinforcement learning algorithms and spiking networks. The key objective is to achieve detection and correction of errors with near-zero latency and minimal computational overhead without extensive retraining of the neural network or modifications to accelerator hardware. This project is co-funded by the Software and Hardware Foundations (SHF) and Discovery Research PreK-12 (DRK12) programs. DRK12 is an applied research program that supports STEM education PreK-12. 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 · 2024-06

As the importance of computational thinking skills continues to grow across the entire modern workforce, it is necessary to engage a broad and diverse population of students in learning these skills. Combining the subject of computing with creative arts, such as music production, has shown success in increasing interest and engagement among diverse groups of learners. EarSketch is a learning tool and curriculum that teaches computer science concepts through computer-based music composition and remixing within a music production frame. EarSketch currently reaches approximately 280,000 K-12 students per year as a free web-based platform. This project will plan the creation of an Open-Source Ecosystem for EarSketch along with an organization dedicated to the governance and long-term sustainability of the EarSketch platform. This project will approach the planning of an Open-Source Ecosystem in three phases. First, an Ecosystem Discovery phase will use interviews and focus groups of teachers, students, community partners, and peer organizations to inform the final design and guiding principles of the Open-Source Ecosystem. A second phase will focus on a virtual community summit with users, developers, and community partners to further develop and gather feedback on details of the Open-Source Ecosystem as well as the governance model of the EarSketch platform. Finally, a steering committee will be charged with finalizing and adopting the guiding principles and design of the Open-Source Ecosystem. The goal of this project is to ensure that the EarSketch platform has a long-term model of community-driven governance and support so that it continues to be an open, freely available educational resource that engages more students in computing education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

Cyber-Physical Systems (CPS) are typically composed of interconnected hardware and software components, which individually may not be inherently highly reliable or secure. However, several CPS applications demand a high degree of safety, security, and reliability. Thus, the fundamental problem is constructing highly dependable CPS applications from building blocks that are, in themselves, not inherently reliable. There has been enormous progress made in understanding and patching various classes of vulnerabilities in large-scale distributed CPS. However, these efforts at designing and operating resilient CPS have often been stymied by the lack of understanding of the impact of any perturbation to the overall system, under the economic and policy constraints involved in any realistic real-world CPS. We define perturbations as failures due to: (1) unintended errors in hardware/software, (2) security attacks, (3) unexpected interactions among cyber-physical and human elements including natural disasters, and (4) incomplete cooperation among stakeholders. In this project, we address these shortcomings to challenges to create resilient, large-scale CPS through our CHORUS Frontier award. Chorus will develop rigorous, scientific mechanisms to enable CPS resilience against a large universe of perturbations. Our application domain is Connected and Autonomous Transportation Systems (CATS) and thus, the benefits of CHORUS will be demonstrated through improvements in safety and security in this domain. We will achieve goals of CHORUS through three interacting intellectually challenging thrusts in the project. Thrust 1 is on Modeling which will create executable specifications of cyber, physical, and human assets, their interconnections, and the economic and policy constraints. The models will capture various stakeholders in the system and their degree of information sharing and cooperation in defense of the target CPS. Thrust 2 is centered on Proactive planning and deployment. We will develop rigorous game-theoretic formulations to model the spread of perturbations (natural and man-made), their effects, and the appropriate resource allocations that can be applied for resilience at the planning stage, i.e., prior to system deployment. We will also consider which defensive investments are feasible under a rational versus a bounded rational behavioral model of the stakeholders. Thrust 3 focuses on Runtime distributed detection and response. We will determine, at runtime, the security state of the system, through novel uses of existing sensors in the system even though they are imperfect. This will then trigger the response mechanisms, which will be proven to be approximately optimal, through analysis and experimentation. In terms of broader impact, the greatest impact will be that CPS owners will gain a higher degree of trust in the operation of the CPS and policy-makers will understand what level of cooperation among multiple stakeholders in a CPS to incentivize. We will create compelling demonstrations of CHORUS on a connected vehicle testbed distributed between our academic institutions and our industrial partner GM. We will also organize an annual student security competition and develop two MOOCS, both having foundational material on resilient CPS and one focusing more on the CATS application domain. 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 · 2024-05

General awareness of the power of genetic knowledge is evident in the rapid use of direct-to-consumer genetic testing companies for people to learn about their own ancestries and to find previously unknown relatives, and the consequent solving of long-cold crimes by genetic genealogists. More generally, the genetic constitutions of plants, animals and humans influence their fitness for survival and their behavior and also can reveal their evolutionary histories. Advances in genetics rest on observations made in nature or in the laboratory, and numerical representations of those observations inform scientific theories as well as the decisions made by conservationists, plant and animal breeders, forensic scientists and physicians. Turning data into knowledge is the realm of statistics, and statistical genetics is changing as fast as genetics itself to meet the challenges Mendel could not have imagined when he collected data on seven characteristics of peas. Human geneticists now have data sets with over a billion genetic elements per individual, and the statistical tools of even ten years ago are often not sufficient to reveal the underlying information in so much data. Students in the biological sciences need help in learning about new statistical procedures and the Summer Institute in Statistical Genetics lets them interact with the people who developed those procedures and who have proven themselves effective as instructors and mentors. The Summer Institute in Statistical Genetics is an annual program of 18 short courses, held in three parallel half-week sessions over a period of three weeks each July at the University of Washington. Each course is led by two instructors from across the US and other countries. Participants can progress through a series of courses, often over multiple years, that begin with basic concepts in probability and statistics and end with advanced topics including Markov chain Monte Carlo methods and pathway and network analyses. They can choose courses that address data from natural or experimental plant and animal populations, or human populations. They can follow population or quantitative genetic streams, or learn about applications to conservation or forensic science. 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.