Author: orandall

Bolstered by a massive scale of ubiquitously connected smart devices, the emergence of Internet-of-Things (IoT) has brought about substantial conveniences to our daily life through a plethora of applications, of which many are safety-critical, including healthcare, surveillance, and autonomous driving, to name a few. For such safety-critical domains, the current toolkits usually fall short in uncertainty quantification, a key feature that is necessitated for informed decision-making. Given the enormous data collected by IoT devices on-the-go, scalability is another key enabler of real-time IoT sensing and management with low latency. Further, how to endow learning with adaptivity and robustness to unpredictable dynamics in IoT is of utmost importance, especially with humans-in-the-loop. Before embracing the full potential of safety-critical IoT, novel tools have to be developed to address these major challenges. Towards this goal, this CAREER proposal advocates fundamental research that aspires to advance the current tools for real-time IoT sensing and management, with direct impact on a number of safety-critical domains, including healthcare, transportation, and environmental sensing. Leveraging the PI’s institutional resources, the PI will transform the proposed research goals into educational activities, through i) mentoring graduate and undergraduate students, especially those from the underrepresented groups; ii) curriculum development that cross-fertilizes the fields of machine learning, communications, signal processing and networking; as well as iii) interdisciplinary collaboration with UGA’s Center of Cyber-Physical Systems. This seamless integration of research and education is central to the PI’s career path and is well aligned with UGA’s mission “to teach, to serve, and to inquire into the nature of things.” To further promote the societal embracing of the emergent IoT technologies, the PI is committed to disseminate the research outcomes to the general public, in particular K-12 students, through short courses, online videos, and workshops.

This proposal puts forth an ambitious plan by tailoring advances in contemporary Bayesian machine learning tools, namely, Bayesian function approximation, Bayesian bandit optimization, and Bayesian reinforcement learning, to address the aforementioned challenges. This fresh Bayesian flavor naturally innovates existing toolkits with uncertainty quantification and robustness, essential to safety-critical IoT. The resultant approaches will not only benefit key IoT-enabled tasks, but also markedly push the envelope of these disciplines by incorporating IoT-driven constraints. Specifically, three complementary and intertwined research thrusts will be pursued. Thrust 1 (T1) puts forth a fundamental uncertainty-aware function learning framework, which not only directly benefits the prediction-oriented IoT sensing task in T1, but also contributes to Bayesian optimization for open-loop blind IoT management in Thrust 2 (T2), where the decisions made by the IoT controller do not affect the IoT state. Thrust 3 further builds on T1 and T2 to scale up Bayesian RL for real-time closed-loop IoT management with full interaction between the IoT state and the IoT controller. The ultimate pursuit is a holistic framework that integrates novel algorithms with uncertainty awareness, scalability, and adaptivity for real-time IoT sensing and management, the associated rigorous analyses for robustness to unpredictable dynamics, and the deployment to real safety-critical IoT applications.

Funder: National Science Foundation 

Amount: $522,786 

PI: Qin Lu, College of Engineering 

Organoids are 3D, miniature versions of human organs, that help us learn about how organs grow and work. Organoids are useful for understanding human organ development, how cells talk to each other, and how our bodies fix themselves. This project focuses on making better organoids that act more like real organs. Organoids are usually made from special cells that can become many different cell types. With guidance, these cells organize themselves into a 3D shape that look like the real organ. Many organoids are not yet as good as they could be. First, researchers have made organoids for brains, kidneys, and lungs, but not all organs are represented as organoids yet. For example, adrenal gland organoids do not exist. Adrenal glands are critical for producing hormones that help us regulate stress, how much water to drink, and sexual development. Here, the aim is to make adrenal gland organoids. Second, most organoids are spheres, but most organs have different shapes; these shapes matter and adrenal gland organoids in the shape of real adrenal glands will be made. Third, big organoids sometimes have dead parts inside, because food and oxygen cannot reach the inside without blood vessels. Here, plant roots will be used to make straw-like tunnels into the organoids so that food can reach the inside. In a give-and-take approach, what is learned will be shared with others. In collaboration with artists, a room-size organoid sculpture will be created, and dances inspired by the science will be made. In return, the artists approach to the scientific problems will inspire the scientific paths. Additionally, middle and high school students will be taught through ceramic art infused science classes to model their own imaginary cells; and graduate students will create their own art-science liaison project.

The overall goal of this project is to generate miniature engineered adrenal gland organoids in the native shape of the organ and to provide a vasculature-like structure that will allow them to mature. Goal 1: Make organoids that look like and function as adrenal glands. Since the shape or organs is important, it will be copied. The plan is to use 3D printing to make molds in the correct organ shape. Then, the cells will be added into these molds to grow the organoids. The engineered new organoids will be compared to spherical organoids to see if they work better. Goal 2: Make organoids that can grow bigger and live longer. Most organoids stop growing or die when they get too big. Here, plant roots will be stuck into the organoids to create straw-like structures that will allow food and oxygen to get to the inside of the organoids and keep the cells there alive, even if the organoids get big and old. In the end, this project will help researchers learn more about how to make better organoids. This project will culminate in an organoid symposium, where everyone can see what is learned about organoids through science, education and art.

Funder: National Science Foundation 

Amount: $524,174 

PI: Nadja Zeltner, Franklin College of Arts and Sciences, Department of Biochemistry and Molecular Biology 

Large and disruptive measles outbreaks are largely predictable with modern forecasting technology and publicly accessible data. We envision a future where global-scale, country-level risk assessment empowers the advocacy case for targeted immunization in advance of outbreaks. As it stands, WHO regularly publishes retrospective lists of large disruptive outbreaks. Broadly speaking, this grant funds the development of a method to forecast this list, enabling global partners to set focus countries for augmented attention and bespoke intervention. As a more standard forecasting problem, this concept pulls technical ideas from experts with a variety of backgrounds, ranging from epidemiology to statistics and to computer science. Along those lines, this project will broaden the domain expertise associated with measles data and analytics, with likelihood of spillover into in other predictive modeling areas associated with vaccine preventable diseases.

The introduction of safe highly effective measles-containing vaccines has led to large reductions in measles cases and deaths while also increasing the interannual variability in measles incidence. Fortunately, complex dynamical transitions of measles transmission may often have simple underlying exogenous explanations such as changing birth rates and vaccination rates. Here, we propose to use classification methods to build global-scale data-driven measles outbreak prediction models. Classification methods have been enormously powerful for developing risk maps for infectious disease in the context of emergence. However, these methods can lead to spurious inference or over-confident predictions due to the inclusion of unrelated predictors or bias in the creation of training and test datasets. We will leverage our team’s considerable experience with measles epidemiology to i) curate a suite of predictor variables that are informed by dynamic systems theory and ii) develop training and test data sets based on epidemiologically relevant strata. The primary deliverable of this project will be an R package that will process data inputs and predict country-level measles outbreak risk (per specified prediction target). This output can significantly contribute to decisions about resource allocation for the control and surveillance of measles, enabling global partners to set focus countries for augmented attention and bespoke intervention.

Funder: Bill and Melinda Gates Foundation 

Amount: $583,459 

PI: Amy Winter, College of Public Health 

The University of Georgia (UGA) proposes a renewal of PREP@UGA that builds on the program’s successes to date and continues to advance diversity of the biomedical research workforce. Diversity of the biomedical research workforce is critical to the success of the NIH mission and necessary for research innovation, translation, and justice. However, ongoing, difficult work is needed to advance diversity within and beyond UGA. PREP@UGA has a successful record of recruiting, supporting, and preparing postbaccalaureate students in pursuing further education and careers in biomedical research. To date, PREP@UGA has trained 53 Scholars with 87% matriculating into biomedical research PhD programs at research-intensive universities. Seven PREP@UGA alumni successfully completed doctoral training and are now in Postdoctoral Fellowships in academia and government or working in industry or academia. Thirty-three PREP@UGA alumni are in good standing in their PhD programs. The program is well-positioned to continue this important work because of its close alignment with UGA’s Diversity and Inclusive Excellence and 2025 strategic plans, which have catalyzed changes in institutional policies and practices to address systemic issues that have limited diversity. In this renewal, PREP@UGA will (1) continue to use a multi-pronged approach to recruit diverse cohorts of six postbaccalaureate Scholars who identify as underrepresented and/or as having a disability; (2) continue to engage Scholars in intensive research training with quality mentorship; (3) enhance the collective and individualized professional development for Scholars; and (4) enhance Scholars’ networking in ways that enable them to bring their whole selves to their research, educational, and career pursuits. We propose three programmatic enhancements in response to results from evaluation and findings from education research. First, we will strategically expand the pool of faculty mentors so that Scholars can participate in research that more fully represents the disciplinary breadth of biomedical research on campus. This change has the added value of increasing the racial, ethnic, and gender diversity of PREP@UGA’s pool of quality research mentors. Second, we will establish a cadre of career mentors who are diverse in terms of their biomedical research discipline, career path, and personal characteristics (e.g., race, ethnicity, gender, ability status) for additional support and advice for navigating academic research and career pursuits. Finally, we will add value to the current, effective professional development activities by adding two modules. The first will support Scholars in building their knowledge and skills to understand and evaluate statistical analyses in published research. The second will introduce Scholars to notions of community cultural wealth and funds of knowledge to affirm the strengths they bring to the biomedical research endeavor and support them in using science for social justice.

Funder: National Institutes of Health 

Amount: $1,689,870 

PI: Kimberly Klonowski, Franklin College of Arts and Sciences, Department of Cellular Biology 

Plants synthesize complex molecules for defense and signaling using specialized metabolic pathways. These plant natural products enhance their own evolutionary fitness and many of these molecules have been used with great success as pharmaceuticals to treat a wide range of human diseases as exemplified by Paclitaxel and Vinorelbine. However, our access to plant specialized metabolites can be limited, as these molecules are often produced in small amounts as part of complex mixtures and restricted to specific cell-types. While metabolic engineering and synthetic biology has the potential to improve our access to these compounds, these approaches require in-depth knowledge of the biosynthetic genes, transporters, and/or regulatory elements of the specific pathway. Next-generation omics technologies have made elucidation of plant natural product pathways more streamlined over the last decade, yet the discovery of plant natural product genes remains challenging relative to that in microbial systems. As a consequence, successful examples of metabolic engineering to improve access to the wealth of pharmacologically active molecules encoded in plant genomes are still few in number. For example, while the plant anti-cancer agent vinblastine has been reconstituted in yeast, the titers are not commercially viable. We have recently developed a single-cell omics-enabled, genome-to-pathway discovery pipeline that accelerates the discovery of natural product biosynthetic pathway genes and their associated regulatory sequences, including transcription factors and cis-regulatory elements. In Aim 1, we will prepare single cell- omics datasets from plant materials for pathway discovery of eight key plant natural products with anti-pain, anti- inflammatory, anti-malarial, and anti-cancer activity. In Aim 2, we will select and validate biosynthetic pathway genes for these compounds in N. benthamiana. New and innovative metabolic engineering strategies for improving access to plant natural products are still needed. In Aim 3, cell-type specific regulatory sequences for these eight biosynthetic pathways will be identified. In Aim 4, synthetic biology will bes used to engineer Catharanthus roseus callus-derived suspension cultures to produce anhydrovinblastine and a subset of the eight natural products from this study. Single-cell omics will be used to examine the heterogeneity of natural product production in populations of wild-type and engineered C. roseus suspension culture cells. Even if commercially appropriate cultures are not developed within the scope of this proposal, this project will provide the first rigorous omics datasets on a plant cell culture system. In summary, the state-of-the-art omics approach in this project will lower the barrier for gene discovery of plant-derived natural products. Utilization of synthetic biology approaches empowered by cell-type-specific knowledge of biosynthetic pathways will enable a renaissance in the sustainable production of clinically-relevant compounds in plant suspension culture.

Funder: National Institutes of Health 

Amount: $2,605,271 

PI: Robin Buell, College of Agricultural and Environmental Sciences 

A crop’s traits and 3D structure (the shape and architecture of plants, including both above- and below-ground parts) are the attributes that chiefly influence crop growth and yield and provide critical evidence for plant phenotyping (the characterization assessment of plant traits). Crop yield predictions can be made by assessing 3D plant structures using crop sensing methods. However, crop sensing results at different scales are usually analyzed in isolation, which overlooks essential connections. Moreover, while root systems play a central role in plant functions, current methods mainly assess crops based on above-ground crop structure due to the difficulty of accessing roots. Current methods use satellites for remote sensing and drones for local sensing, enabling crop assessment at varying scales; however, it is difficult to integrate these observations effectively, and the information stream is formidable. The overarching objective of this project is to develop a novel AI infrastructure to integrate these observations to model and assess 3D crop structures at multiple scales and enhance below-ground sensing capabilities. Using this infrastructure, 3D crop structures can be estimated accurately at the individual, farm, and satellite scales, facilitating crop assessment and yield prediction. The project dramatically enhances and accelerates the ability of growers and agronomists to assess critical crop field structural variation for both above- and below-ground components, enabling large-scale crop management. This project also benefits students, from the high school to the Ph.D. level, by applying multi-scale 3D models of above- and below-ground crop structures to immersive education methods (Virtual Reality (VR), Augmented Reality (AR), and online learning), which are well-suited to solving the challenges of distance learning, especially for subjects like agriculture requiring field study. The multi-scale sensing system is also capable of estimating 3D landscape structures and large-scale crop structures and can be utilized in other areas, such as Arctic Sea ice modeling, forestry, and climate change studies. This project aims to connect a plant’s structural phenotypes below- and above-ground and link in-situ measurements to satellite sensing data, thus enabling non-destructive crop root sensing and root-system status estimation based on observation of plant growth above-ground while at the same time empowering satellite images to assess these factors to furnish more local and detailed information. This project establishes a method for 3D crop sensing of individual plants, crop fields, and satellite regions to provide multi-scale crop structural evidence for crop assessment and yield prediction. This project also develops a novel AI neural network to sense root structures and predict traits based on sensing above-ground plant structures.

This project investigates methods for satellite-based 3D sensing and nondestructive below-ground root sensing. Novel AI infrastructures are explored to address critical issues in computer vision and remote sensing, efficient integration of multi-scale sensing, 3D structure prediction, and spatial-temporal 4D inference. Such an approach can lower the ceiling for operational adoption of satellite and in-situ imagery assessments, based on a scientifically underpinned, multi-scale, 3D assessment workflow. In addition to its essential and practical implications for agriculture professionals, this project also explores novel AI solutions within computer vision and remote sensing. Crop structures are highly diverse, complicated, and changing phenomena. Therefore, agriculture presents an ideal research domain for investigating novel AI methods. This research advances AI by 1) largely improving the fusion effectiveness of various remote sensing modalities from sensors mounted on different devices, 2) significantly enhancing the learning capability by connecting sensing outputs expressed in multiple scales, 3) enabling 3D structure prediction for objects across different domains, and 4) providing future status prediction based on 4D spatial-temporal neural networks.

Funder: National Science Foundation 

Amount: $549,928 

PI: Guoyu Lu, College of Engineering 

Coastal marshes provide a suite of vital functions that support natural and human communities. Humans frequently take for granted and exploit these ecosystem services without fully understanding the ecological feedbacks, linkages, and interdependencies of these processes to the wider ecosystem. As demands on coastal ecosystem services have risen, marshes have experienced substantial loss due to direct and indirect impacts from human activity. The rapidly changing coastal ecosystems of Louisiana provide a natural experiment for understanding how coastal change alters ecosystem function. This project is developing new metrics and tools to assess food web variability and test hypotheses on biodiversity and ecosystem function in coastal Louisiana. The research is determining how changing habitat configuration alters the distribution of energy across the seascape in a multitrophic system. This work is engaging students from the University of Louisiana Lafayette and Dillard University in placed-based learning by immersing them in the research and local restoration efforts to address land loss and preserve critical ecosystem services. Students are developing a deeper understanding of the complex issues facing coastal regions through formal course work, directed field work, and outreach. Students are interacting with stakeholders and managers who are currently battling coastal change. Their directed research projects are documenting changes in coastal habitat and coupling this knowledge with the consequences to ecosystems and the people who depend on them. By participating in the project students are emerging with knowledge and training that is making them into informed citizens and capable stewards of the future of our coastal ecosystems, while also preparing them for careers in STEM. The project is supporting two graduate students and a post-doc. The transformation and movement of energy through a food web are key links between biodiversity and ecosystem function. A major hurdle to testing biodiversity ecosystem function theory is a limited ability to assess food web variability in space and time. This research is quantifying changing seascape structure, species diversity, and food web structure to better understand the relationship between biodiversity and energy flow through ecosystems. The project uses cutting edge tools and metrics to test hypotheses on how the distribution, abundance, and diversity of key species are altered by ecosystem change and how this affects function. The hypotheses driving the research are: 1) habitat is a more important indirect driver of trophic structure than a direct change to primary trophic pathways; and 2) horizontal and vertical diversity increases with habitat resource index. Stable isotope analysis is characterizing energy flow through the food web. Changes in horizontal and vertical diversity in a multitrophic system are being quantified using aerial surveys and field sampling. To assess the spatial and temporal change in food web resources, the project is combining results from stable isotope analysis and drone-based remote sensing technology to generate consumer specific energetic seascape maps (E-scapes) and trophic niche metrics. In combination these new metrics are providing insight into species’ responses to changing food web function across the seascape and through time.

Funder: National Science Foundation 

Amount: $688,849 

PI: Jimmy Nelson, Franklin College of Arts and Sciences, Department of Marine Sciences 

Georgia has a rich agricultural history in fresh produce, owing to its long growing season. Creation of value-added markets would provide a valuable source of income to growers during times where fresh produce is not actively being grown and harvested. In rural areas where farms are primarily located, many entrepreneurs lack accessibility to appropriate business development resources and thus opportunities to break into value-added markets are limited. The proposed Value-addition Institute for Business Expansion (VIBE), an Agriculture Innovation Center (AIC), located at the University of Georgia (UGA) – Department of Food Science and Technology (FST) aims to support food businesses based in rural communities of GA to commercialize new value-added foods. The proposed AIC will provide technical assistance on food product development, scaled-up processing and access to mini-grants to producers throughout Georgia to enable them to take the steps necessary to commercialize their products. The AIC aims to do this by accomplishing the following objectives: (1) Perform a needs assessment in rural, distressed communities in Georgia, (2) Establish a Georgia-specific network of providers for business development services, (3) Build capacity for farmers seeking to enter value-added markets, (4) Develop multiplatform training materials and workshops related to process and product development, and (5) Administer mini-grants to producers to facilitate access to services and support required to start a new business or scale up an existing business. Georgia currently has a significant number of resources devoted to building and running a business, as evidenced by letters from our key partners, but there is a gap in programs specifically related to process and product development of foods that will bridge initial business development steps with production runs and marketing. Center personnel will evaluate the product prototype and work with clients to identify equipment capable of replicating the process and operating at the desired scale, with consideration to product characteristics (quality and safety) and cost analysis. Mini-grants will be distributed through a competitive application process. In addition to those companies who receive mini grants, we plan to provide consultation services to ~20 companies per year, evenly split between PD Casulli and co-PD Mis Solval. Preference will be given to rural communities in Georgia with high levels of distress.

Funder: U.S. Department of Agriculture 

Amount: $978,545 

PI: Kaitlyn Casulli, College of Agricultural and Environmental Sciences 

A manufactured product is the end of a long chain of interwoven manufacturing steps that may span geography, industries, and manufacturing processes. Each step may be optimized, yet for a step there may be uncertainty about the larger context of its contribution that if known during production would allow for greater efficiencies, better quality, and improved productivity. If a global system analysis and optimization could, in addition, be conducted cooperatively with each step during production, the end product and overall production would greatly benefit. Advances in hybrid distributed control and optimization, in highly networked cyber-physical systems, and in artificial intelligence and machine learning have opened new opportunities in cyber-enabled manufacturing. This Future Manufacturing Research Grant (FMRG) CyberManufacturing project connects and coordinates the ensemble of various manufacturing processes, operating at different manufacturing stages under a cyber-coordinated close-knit system, defined by an analytical framework, known as STREAM, a multi-stage distributed future manufacturing system. This project provides a public online repository that hosts the STREAM data, models, simulators, controllers, analytics, and empirical studies, and that facilitates sharing research experiences about STREAM with the research and industry communities. The project creates new outreach and workforce development activities for K-12, undergraduate and graduate students, as well as working professionals in the field of manufacturing. These research results will be included in the university curriculum for advanced manufacturing, architecture design, machine learning, simulation, and system control and optimization. This project is an interdisciplinary research project with three interconnected research tasks: (1) STREAM architecture design and implementation. There is a novel bridging middleware architecture to enable seamless machine interoperability, and software- and hardware-based accelerators will be built to enable efficient communication and computing in cyber-manufacturing systems. (2) STREAM modeling and quality control. There is a novel multi-layer functional graphical model to address the high-dimensional functional dependencies over STREAM machines and a spatial-temporal iterative learning control method to achieve an accurate and efficient process quality control. (3) STREAM simulation and production control. To handle the multi-level dynamic process interactions in the system that collectively meet process quality, cost, and resource constraints, there are coordinated, high-fidelity multi-resolution simulators, assisting with the hybrid control of STREAM production. These research tasks are validated in an open-source additive manufacturing testbed for the manufacture of high energy/power density supercapacitors. The modeling and analysis methodologies form a rigorous basis for continuous improvement of quality, manufacturability, and productivity of future multi-stage and distributed manufacturing systems.

Funder: National Science Foundation 

Amount: $2,333,085 

PI: Hongyue Sun, Franklin College of Arts and Sciences, Department of Statistics 

Seasonal influenza viruses cause significant disease burden. While seasonal influenza vaccines are available, these are often poorly efficacious due to mismatch with circulating virus strains, particularly in the populations at greatest risk of complications from infection, including the elderly, infant, and immunocompromised. Influenza A viruses (IAV) also pose a pandemic risk for which seasonal influenza vaccines provide no cross protection. In 2018, the National Institute of Allergy and Infectious Diseases (NIAID) released a strategic plan for developing a universal influenza vaccine and subsequently released the FOA PA-18-859, “Advancing Research Needed to Develop a Universal Influenza Vaccine.” The long-term goal of this proposal is to develop a universal influenza vaccine. We hypothesize that a flu vaccine composed of broadly cross-reactive B-and T-epitopes covalently linked to an immune adjuvant will elicit potent immune responses and protect against diverse influenza A virus infections and associated disease. This expectation is supported by our prior studies which demonstrated a fully multi- component vaccine composed of tumor associated glycopeptide B- and CTL-epitope, a peptide CD4 T cell epitope and a TLR2 agonist (Pam3CysSK4) elicited robust immune responses and protected against a stringent tumor challenge in a murine cancer model. We will chemically synthesize multi-component vaccines that are composed of conserved peptide antigens derived from IAV and an adjuvant such as Pam3CysSK4 or monophosphoryl lipid A (Aim 1). In addition, we will employ an enzymatic glycan remodeling strategy to modify recombinant hemagglutinin (rHA), which is used as a licensed flu vaccine, with various TLR agonists and DC targeting moieties (Aim 2). It is expected that the self-adjuvanting rHA vaccines will enhance antigenicity of conserved but poorly immunogenic stalk domain thereby providing heterologous protection. We will also explore whether covalent attachment of immune-potentiators to recombinant neuramidase (rHA) can enhance its immunogenicity and provide broad protection (Aim 3). The

immunogenicity and efficacy of the vaccine candidates will be evaluated in murine and ferret vaccination and challenge models, using innovative approaches and antigen probes to assess polyfunctional T cell responses and geminal center (GC) B cell response. This research is significant as it directly addresses multiple NIAID priorities in their strategic plan for developing a universal influenza vaccine. The results of these studies could have a significant impact as the universal influenza vaccines developed should be suitable for advancement to pre-clinical studies. Moreover, the adjuvants and approaches developed in this proposal will be applicable to other vaccines and study of the host response to influenza infection, and will have broader impacts beyond universal influenza vaccine development.

Funder: National Institutes of Health 

Amount: $3,294,848 

PI: Geert-Jan Boons, Franklin College of Arts and Sciences Department of Chemistry