Purdue University

university

West Lafayette, IN

Total disclosed

$196,822,262

Award count

441

Distinct programs

First → last award

1991 → 2031

Disclosed awards

Showing 201–225 of 441. Public data only — SR&ED tax credits are confidential and not shown.

Integration of Cross-Disciplinary Skills and Dispositions into a Computer Science Degree Program$273,295
NSF Awards · FY 2024 · 2024-10
This project aims to serve the national interest by developing resources and approaches to support teaching and assessing cross-disciplinary skills and dispositions within a computer science degree program. Prior research has shown that cross-disciplinary skills (e.g., teamwork) and professional dispositions (e.g., adaptability and resilience) are crucial in STEM occupations. Yet, fostering and assessing undergraduate success in developing non-technical competencies is challenging and best achieved when embedded across the curriculum. The goal of this project is to work with the Tuskegee University’s Computer Science Department to integrate essential cross-disciplinary skills and dispositions into its computer science degree program as part of a general continuous improvement model. This project is a partnership of a long-standing computer science program of study with a clear vision for continuous improvement and a team experienced in the design of instruction, assessments, and program evaluation. Cross-disciplinary skills and dispositions must be clearly defined in order to be taught and assessed consistently. The project's activities are framed around six goals. First, is to identify behavioral indicators (criteria) and assessment approaches for cross-disciplinary-skills and dispositions. Second, is to incorporate cross-disciplinary-skills and dispositions across the Tuskegee CS curriculum. Third, is to incorporate teaching strategies and assessments of cross-disciplinary-skills and dispositions into Tuskegee CS core courses, based on behavioral indicators. The fourth project goal is to enhance Tuskegee’s longitudinal evaluation and continuous improvement model. The fifth and sixth goals are to develop open education resources and to conduct evaluative research. The project team will develop and share research-based teaching, assessment, and evaluation instruments and protocols, in addition to delivering academic presentations and contributing papers to the computer science education research literature. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
CAREER: Brain-Machine Interfaces for Speech$600,932
NSF Awards · FY 2024 · 2024-10
Decoding what a person intends to say, through analysis of electrical signals recorded directly from the brain, has transformative potential to restore the ability to communicate through speech to those who have lost it. Neural speech decoders have been deployed along these lines in academic studies, but they are not yet good enough to displace non-invasive, low-tech alternatives: extremely high accuracy has been achieved only at the price of task complexity or generality. This project aims to soften this trade off considerably by collecting much more data and making better use of it. This will entail the integration of recent ideas in machine learning, efficient experimental design, and data collection from a large pool of volunteers. The ultimate goal of the project is to enable implantable decoders for restoring speech to persons who have lost it through ALS, stroke, or other traumatic brain injury. Modern machine learning relies on techniques (artificial neural networks) that scale very favorably with the amount of available training data, but intracranial recording (iEEG) data are scarce. Accordingly, the project is organized around a set of methods for increasing the amount of effective training data. A major focus is pre-training, including self-supervised learning, i.e., training models to solve "pretext" tasks involving iEEG data but no annotations, speech or otherwise; generative models for speech audio, trained entirely on audio waveforms, for iEEG-to-audio; and large language models (trained entirely on text) for iEEG-to-text. A second major focus is transfer learning across multiple subjects; that is, training a single model on multiple subjects' data. At least two dozen participants from several collaborating hospitals and research groups are anticipated to provide data. The project also includes an educational component aimed at creating an interdisciplinary workforce in the areas of neuroscience and machine learning, and for making the problem of algorithm design for brain-machine interfaces accessible to a wider audience. 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.
Collaborative Research: Power Systems Dynamics from Real-Time Data: Modeling, Inference, and Stability-Aware Optimization$181,004
NSF Awards · FY 2024 · 2024-10
Rapid decarbonization and deployment of flexible, distributed resources in the electricity energy sector are quickly transforming the real-time operation paradigms of the interconnected power grid infrastructure. These changes have led to growing concerns over power system dynamics and stability, due to the reduced capability of grid inertia and increasing levels of external disturbances and variability. Meanwhile, the electricity infrastructure has benefited significantly from the ongoing deployment of sensing and cyber resources, which give rise to a huge amount of high-rate, high-quality data and information collected during real-time operations. Thanks to the enriched data availability, machine learning advances are envisioned to play an increasingly important role to address the challenges in power system dynamics and stability. This project aims to bridge domain-specific machine learning tools to transform the current grid dynamic modeling, inference, and stability-enforcing solutions. At a societal level, the anticipated outcomes can improve energy efficiency and security, and facilitate higher and smoother penetration of renewables and carbon-free resources. This project will further benefit industry practices with advanced algorithmic solutions, as well as education efforts by providing student training opportunities and reaching out to pre-college students via interactive demos. This project will develop data-enabled and physics-informed modeling, monitoring, and optimization algorithmic solutions targeting power system dynamics. The proposed activities put forth and explore three creative, original, and potentially transformative ideas: i) Correlating synchrophasor data collected at two arbitrary grid locations can efficiently unveil the impulse response of the associated linear time-invariant (LTI) system under certain assumptions, which can be waived leveraging physics-informed analysis; ii) Gaussian processes (GPs) constitute a powerful tool for inferring signals occurring in LTI systems, and thanks to the underlying physics, GPs can be uniquely adapted to learn grid dynamic signals and their derivatives from heterogeneous, noisy, spatially and temporally incomplete, and/or multirate synchrophasor readings; iii) Well-established grid stability metrics can be expressed as convex functions of the steady-state operating point, and stability-aware OPFs can be handled via a semidefinite program relaxation. The outcome will be a comprehensive suite of computational tools dealing with grid dynamics from learning to power system operations, evaluated by both real-event synchrophasor datasets, and synthetic datasets generated from realistic power systems such as a Texas 2000-bus case in collaboration with ERCOT. The research results will also be integrated into engineering educational activities at the secondary and higher education levels. In addition to standard dissemination venues, close collaboration with grid operators will assist in showcasing the effectiveness of the project findings on real-world systems and lead to quick adoption. 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.
Collaborative Research: DMREF: Accelerating the Design and Development of Engineered Photonic Materials based on a Data-Driven Deep Learning Approach$375,000
NSF Awards · FY 2024 · 2024-10
Non-technical Description: Advances in photonic materials that generate, process, or detect light can transform diverse areas of science and engineering, including lasers, optical fiber communications, augmented and virtual reality displays, solar energy harvesting, as well as quantum computing and sensing. By rationally engineering the composition and/or structure of materials at various length scales, it is possible to dramatically enhance their optical responses and performance. However, the traditional approach for the discovery and development of new photonic materials relies on trial-and-error and case-by-case explorations, which are often time consuming and ineffective. This project will use advanced artificial intelligence techniques to develop new artificial photonic materials that can be engineered to have prescribed properties and surpass naturally occurring materials. The research seamlessly integrates materials science, photonics, engineering, physics and artificial intelligence. In tandem with research, the team will develop a multi-channel education program to enhance the learning experience of a broad spectrum of the society, and prepare the next-generation workforce and technology leaders. Technical Description: The project aims to accelerate the pace of the discovery, design, and implementation of new engineered photonic materials, particularly photonic metamaterials, with user-defined spatial, spectral, linear, non-linear and quantum properties through a data-driven approach. This approach will consolidate properties of constituent material compositions, their geometric structures spanning atomic length scales to micrometers, and their underlying symmetries and topology. The project consists of three research thrusts, including (1) establishing deep learning frameworks to construct photonic metamaterials with high efficiency and accuracy; (2) integrating information on the tailorable optical properties of the constituent material platform into deep learning models, to benefit the design and development of reconfigurable metamaterials; and (3) investigating hybrid material systems that couple topological photonic structures designed by deep learning with quantum emitters and optical nonlinearities. The team will accomplish the interdisciplinary research by fusing theory, computation, deep learning, materials engineering, fabrication and experimentation in a closed-loop manner. Through the project, new fundamental knowledge and insights about the interdependent relationships among structure, properties, performance, and processing across different scales will be gained. In alignment with the Materials Genome Initiative (MGI), the project will create a comprehensive library of different artificial meta-atoms and meta-molecules and their optical responses, and eventually drive transformative applications of photonic metamaterials for classical and quantum information processing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
FuSe2 Topic 1: Bridging Atomic Layers and Foundation Models: An Indium-Oxide-Based Versatile Neural Computing Platform$1,187,849
NSF Awards · FY 2024 · 2024-10
The rapid growth of artificial intelligence (AI), especially Large Language Models (LLMs) like ChatGPT, is revolutionizing the way people work, learn, communicate, and access healthcare. Due to the complexity of AI workloads and the limitations of today’s semiconductor hardware, computing with powerful LLMs incurs enormous energy costs and generates a significant carbon footprint. This Future of Semiconductors project will develop a holistic computing solution to provide reliable, private, and energy-efficient computing capabilities directly to end users’ devices, democratizing access to advanced AI. By making oxide semiconductors —a class of mature semiconductor materials used in display panels— extremely thin, down to atomic levels through nanofabrication, breakthrough performance and new technological advantages can be achieved. By synergizing these material advancements with novel chip designs and algorithms, a new computing platform developed through this research will run AI workloads more efficiently than existing silicon-based platforms. This project will engage in device-architecture-algorithm co-design to develop a complementary metal-oxide semiconductor (CMOS) compatible, indium-oxide-based computing platform, enabling reliable and energy-efficient edge inference with transformer models (e.g., LLMs). The research will advance scientific understanding of oxide semiconductors by experimenting with high-k/ferroelectric gate stacks in a unified atomic-layer-deposition (ALD) material system. By harnessing unique features of almost-atomically-thin indium-oxide transistors, novel compute primitives based on ferro-oxide-semiconductor field effect transistors (Fe-OSFETs) will be developed, natively supporting scalable precision. New sparsity and quantization algorithms will be created for transformers to fully exploit search-in-memory and compute-in-memory functionalities native to the indium-oxide compute primitives. In the proposed neural computing platform, the co-existence of a non-volatile mode for long-term data reuse and a charge-based mode with high endurance, allows effective co-optimization of both dynamic and static layers in transformers for energy-efficient, reliability-aware inference. The development of an open-source process development kit (PDK) through this project will further enhance device-to-system co-design and prototyping capabilities with emerging OSFETs. Ultimately, a system demonstrator for an LLM edge inference engine will advance the frontiers of edge machine intelligence, supporting a broad range of societally impactful applications. Finally, the project team is committed to educating a diverse group of students with vertically integrated mindsets and skillsets to form the next-generation workforce, fostering a sustainable and equitable future for society through joint innovations in AI and semiconductor technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Socially Transformative Engineering Pedagogy for a Sustainable Future$104,150
NSF Awards · FY 2024 · 2024-10
As the nation tackles the challenges of energy transition, K-12 education must prepare a future STEM workforce that can not only apply STEM skills but also address reasoning through complex sociotechnical problems involving social justice. Conscientious engineering design can help youth develop these competencies. However, few secondary school programs or STEM curricula focus on promoting students’ reasoning fluency through conscientious engineering design. Aligned with the principles of socially transformative engineering and focused on students of color, this project involves the design and implementation of a novel STEM education curriculum. The curriculum will support the development of secondary students’ abilities to reason through ambiguous and ethical challenges through design projects and to transfer these competencies to everyday life and future workplaces. The project will empower youth as transformative agents with abilities to conscientiously negotiate risks and benefits as they scope and analyze complex problems, generate ideas and solutions, and reason through ethics. The project will contribute to the cultivation of a new generation of STEM professionals who are able to design engineering solutions for environmental sustainability and societal equity. The project meets NSF’s mission to catalyze research and development that enhances all students’ opportunities to engage in high quality education. The project’s partnerships with public school leaders, teachers, and informal educators will further support transformative learning over time with the implementation of the curriculum to future cohorts of students. The curricular products of the project will be published on publicly available websites and journals to promote easy access. The framework for this project integrates three theories—the legitimation code theory, justice-based science education pedagogy, and futures-thinking literacy—to deliver fundamental insights into how secondary school students develop reasoning fluency and STEM knowledge through engagement in socially transformative engineering work. Four key tenets guide the curriculum design work for this project: multicultural ingenuity, ethical integrity, reasoning fluency, and transformative agency. Working with 50 secondary school educators in school contexts that primarily serve students of color, the project involves the design, implementation, and study of a curriculum that will support the development of approximately 600 students’ abilities to reason through ambiguous and ethical challenges through design projects. This project will advance research on transformative agency and reasoning capabilities of minoritized youth in particular as they engage in engineering design. The project has three objectives. First, it will refine curricular resources in alignment with Socially Transformative Engineering Pedagogy and offer professional development with a sociocultural perspective that privileges students’ cultural knowledge and discourse patterns. Second, it will promote futures reasoning and social justice by advancing the capabilities of educational technologies for sustainable infrastructure design using computer-aided design, including but not limited to AI applications such as generative design and personalized learning. And third, it will improve theory on how youth develop reasoning fluency and transformative agency through socially conscientious design. Engineering projects such as designing a self-sustaining microgrid community will foster students' learning and reasoning as they draw on their cultural funds of knowledge and deliberate the consequences of their design choices. Discourse and content analysis methods will be used to study the nature of fluency in student reasoning. Methods such as cluster analysis will be used to identify patterns of variations. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. 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.
SLES: Whitebox Testing, Debugging, and Repairing for Multi-module Autonomous Vehicles in Near-Collision Traffic Scenarios$799,921
NSF Awards · FY 2024 · 2024-10
The advances in artificial intelligence and machine learning have empowered the development and adoption of autonomous vehicles, including self-driving cars and delivery drones. However, the increasing number of incidents involving autonomous vehicles has raised public concerns about their safety and reliability. Ensuring end-to-end safety of such systems is critical but challenging given the sophisticated multi-module systems operating in these vehicles and the enormous number of possible traffic scenarios, especially complex and previously unseen scenarios. Though many testing and verification approaches have been proposed, they are mainly designed for a single vehicle in simple scenarios, which limits their applicability to modern multiple-module systems in which multiple models and conventional algorithms are used in tandem for perception, prediction, planning, and control. This project seeks to reason about the inherent interaction among multiple modules in an autonomous vehicle to systematically identify, debug, and repair unsafe behavior in realistic and diverse scenarios. It will provide empirical assurance of and boost public confidence in the end-to-end safety of these vehicles. Techniques developed in this project will be open-sourced and will be broadly available for building safe robotic systems in various sectors. The project integrates research and education through curriculum development, student advising, and K-12 outreach activities with a focus on recruiting and mentoring students from underrepresented minority groups. This project will develop principled algorithms and practical tools that systematically discover unsafe behavior in a system via a deep exploration of realistic and diverse traffic scenarios and repair the system to enhance end-to-end safety. The key contributions include (1) a method for automated test-scenario construction that decouples high-level semantics and low-level details through a novel Domain Specific Language-based synthesis algorithm, (2) a search-based testing method that efficiently explores the enormous space of possible scenarios and identifies collision-inducing scenarios through a layered abstraction of multi-module autonomous systems and hierarchical optimization, and (3) a new adaptive debugging and repair technique that strategically diagnoses and fixes different kinds of safety bugs in different modules at different levels of granularity. The safety enhancement achieved by the developed framework will be rigorously quantified and validated both in simulation and in physical vehicles. 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.
Collaborative Research: NeTS: Medium: A Systematic Approach to Diagnostic Testing of Next-Generation Radio Access Networks$200,000
NSF Awards · FY 2024 · 2024-10
The radio access network (RAN) is an important subsystem of 5G networks and beyond – providing universal coverage and ubiquitous Internet access to billions of mobile users. Open radio access network (O-RAN) promotes an open system approach, where components and products from different vendors can interoperate with each other, thus accelerating technology innovations. This thus calls for proper testing which has not received its due attention from the research community. This project seeks to identify and address this new research topic of mobile network diagnostic testing, particularly for the O-RAN subsystem. New diagnostic testing methods are developed to not only determine whether the tests pass or fail, but also diagnose the root causes to learn why. These new designs and their gained insights lead to improvements in operation correctness, interoperability, performance and security of the upcoming RAN infrastructure and jumpstart the growth of O-RAN and future mobile technology ecosystem. The project also seeks to influence the standardization of new 5G releases and 5G beyond technologies. It recruits fresh talents and train a new generation of students and engineers for future mobile Internet design. This project explores a systematic approach to mobile network diagnostic testing, spanning from fresh-view modeling and abstraction, efficient algorithms, to novel instruments for tracing and diagnosis, to address all limitations of RAN testing. The research tasks enhance RAN diagnostic testing in three aspects: (1) near-complete test coverage by exploiting dependency among multiple procedures and multiple interfaces of RAN software to generate missing test cases and pinpoint their root causes, (2) non-intrusive, end-to-end tests to assess full-stack performance and potential threats of applications on commodity 5G devices, and (3) test efficiency to optimize test runs and reuse prior test results by identifying repetitive operations and assessing the updates over evolving RAN technology releases. The proposed testing designs are integrated to an open-source toolkit and evaluated in the lab testbed and real-world O-RAN systems with industry collaborators to facilitate technology transfer. 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.
Ideas Lab: PEL Engineering Playground: Intelligent Augmented Reality for Personalized Learning Informed by Environmental Identity Development$300,000
NSF Awards · FY 2024 · 2024-10
This project will develop an innovative approach to personalized engineering education by creating the Personalized Engineering Playground (PEP), an intelligent Augmented Reality (AR) system informed by Environmental Identity Development (EID) theory. By tapping into students' formative childhood experiences in nature and linking them to engineering concepts, PEP aims to create more engaging, emotionally resonant learning experiences for engineering and technology students. This approach has the potential to attract and retain a more diverse group of students in engineering fields, addressing workforce demands and fostering a new generation of environmentally conscious innovators. By learning what formative experiences students who chose to pursue engineering think led them there, we could inform the curriculum for K-12 education, extending upon this pilot study. This insight into the motivations and inspirations of current engineering students can help develop more targeted and effective outreach programs for K-12 students, potentially sparking interest in engineering among more underrepresented groups. Such an approach could increase diversity in the engineering pipeline and, ultimately, the profession. The project aligns with the NSF's mission to promote the progress of science and advance national health, prosperity, and welfare by enhancing engineering education through cutting-edge technology and personalized learning experiences. By integrating environmental consciousness into engineering education, PEP will contribute to developing engineers who are better equipped to address pressing ecological challenges and create sustainable solutions for the future. This two-year pilot study will focus on developing and testing a prototype version of PEP, targeting undergraduate engineering and technology students at Purdue University. The project will address two main research questions: (1) How have undergraduate engineering students' formative childhood experiences influenced their interest in pursuing engineering as a career, and how can we utilize these experiences to inform the development of an EID-informed AI/AR engineering learning platform? (2) Is there a knowledge gain on the presented engineering topics for different cohorts when students engage with a PEP module? The study will employ a mixed-methods approach, combining quantitative and qualitative data collection through surveys, embedded AR module questions, and analysis of learners' interactions with the platform. The research will involve two distinct cohorts of approximately 100 students each. Cohort 1 will participate in the initial survey phase, providing insights into their formative environmental experiences and their connections to engineering interests. Cohort 2 will engage directly with the developed AR/AI module during the pilot phase, offering hands-on feedback on its effectiveness and usability. The project will develop one AR module based on a common formative experience identified through student feedback, focusing on fundamental engineering concepts. This module will be integrated as a regular lab component within the MET 230 (Fluid Power) course at Purdue University. Expected outcomes include a functional AR prototype with embedded AI, insights into the effectiveness of EID-driven engineering education, and a framework for expanding PEP to engage additional age groups and engineering disciplines. The project will also yield valuable data on the relationship between early environmental experiences and engineering career choices, leading to more personalized approaches for teaching engineering concepts to K-12 students and attracting a more diverse future engineering population. By bridging the gap between childhood experiences in nature and engineering education, PEP has the potential to create more engaging, relevant, and impactful learning experiences that could attract a more diverse group of students to engineering fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Develop a human-mouse chimeric brain model for studying tau pathology in human neuron in vivo$157,000
NIH Research Projects · FY 2025 · 2024-09
Project Summary/Abstract Alzheimer’s disease (AD) is the most common cause of dementia, affecting 6.7 million Americans aged 65 and older. Tau pathology, characterized by the abnormal aggregation of tau protein, is a prominent pathological hallmark of AD that shows the strongest correlation with cognitive decline compared to other AD pathological hallmarks. However, current tau models have limitations in replicating tau pathology observed in AD brains, and most therapies targeting tau developed in animal models have failed in clinical trials, largely due to species differences. This underscores the urgent need to develop translational human cell-centric tau models capable of addressing the critical gap in understanding the mechanisms underlying tau pathology, particularly its spatiotemporal formation and spread, and its relationship with cognitive decline in AD. The long-term goal is to create tau models in human neurons in vivo to investigate mechanisms of tau pathology and develop therapeutic interventions for tau pathology in AD and other tauopathies. In a recent study, we successfully generated a unique human-mouse chimeric brain model that enables the maturation, aging, and survival of human neurons for over 18 months. Furthermore, human neurons are widely distributed and functionally integrated into the host brain. Based on these results and the seeding activity of pathological tau, which can induce normal tau misfolding and form tau pathology, we hypothesize that injecting pathological tau protein isolated from AD postmortem brain tissues into chimeric mouse brains will result in tau pathology in human neurons in vivo. The objective of this project is to develop a human-mouse chimera tau seeding model that recapitulates human tau pathology in human neurons in vivo without introducing MAPT mutations, and that can be used to investigate the spatiotemporal formation and spread of tau pathology in human neurons and monitor associated cognitive dysfunction. Additionally, we aim to elucidate transcriptomic signatures of human neurons during tau pathology progression to gain deeper mechanistic insights. Furthermore, we will compare tau pathological endpoints between human and mouse neurons within the same brain environment using the chimera model. We propose the following aims. Aim 1: To characterize a human-mouse chimera tau seeding model in terms of species- specific spatiotemporal patterns of tau pathology and associated cognitive dysfunction. Aim2: To determine disrupted cellular pathways and identify species-specific transcriptomic perturbations associated with tau pathology progression in human neurons using the chimeras. The successful completion of this project will advance our understanding of tau pathology and lead to a novel human-mouse chimeric brain model for studying tau pathology in human neurons in vivo. This innovative model will be a valuable pre-clinical tool for understanding human tau pathology and testing interventions targeting tau pathology in AD and other tauopathies.
A bio-math approach to elucidate stem cell dynamics in Ceratopteris gametophytes$207,261
NIH Research Projects · FY 2025 · 2024-09
The development of multicellular organisms raises a key question: how are cell behaviors dynamically controlled to produce tissue and shape? Whether in plants, animals, or humans, misregulated cell division and growth can lead to developmental defects, i.e., organ dysfunction, reduced reproduction, and disease. While some regulatory pathways have been uncovered in different species, the quantitative prediction of cell behavior at single-cell resolution in multicellular organisms—though critical—is incomplete. This project will address this gap by combining experiments, predictive modeling, and data-driven methods in Ceratopteris (fern) gametophytes. Ceratopteris gametophytes comprise a single layer of cells that grows from 1 to nearly 500 cells in 12 days, transforming from round to heart-shaped and developing pluripotent meristems (stem cells). Ceratopteris gametophytes are highly suited for an interdisciplinary approach to understanding cell dynamics. They allow for non-invasive time-lapse confocal imaging to trace every cell and are efficient for perturbations. With the long-term goal of determining stem cell behavior using Ceratopteris gametophytes as a model, this 3-year project will uncover the cellular and molecular interactions at work during tissue growth and meristem notch formation, and develop a data-driven approach to associate vertex models with efficient particle models. This project has 3 aims: Aim 1 will develop the first mathematical models of cell behavior in Ceratopteris gametophytes and establish a cycle of testing empirical hypotheses in silico and model-generated predictions in vivo. It will uncover how spatiotemporal differences in cell division and growth drive meristem formation and organ morphology through mechanical perturbation experiments, quantitative time-lapse imaging, and vertex modeling. Aim 2 will elucidate the signaling molecules behind these spatiotemporal differences through a close coupling of new chemical and genetic perturbation experiments and hybrid modeling. Aim 3 will tailor a recent method for learning equations to account for changes in cell number, multi-scale dynamics, and the connected structure of cells in plant tissue. This research will use this approach to associate vertex models with simplified—more efficient—particle models and discover equations of motion directly from in vivo nucleus trajectories. Together this work will shed light on how cell division, growth, and differentiation are controlled to maintain meristems and define gametophyte shape, and it will increase the applicability of data-driven methods to collective cell dynamics more broadly.
Interdisciplinary Summer Institute on the Analysis of Complex, Large-Scale Longitudinal Data$311,650
NIH Research Projects · FY 2025 · 2024-09
Contemporary large-scale NIH initiatives have led to the emergence of many high-quality publicly available longitudinal datasets that that include complex data of various types, sources, and domains (e.g., biological, social, individual, family, neighborhood, etc.). However, use of these datasets without training can lead to scientific setbacks, including work that is imperfect, misleading, or even incorrect. There is an urgent need for educational programming to train researchers both within and outside of academic careers on the innovative and responsible use of publicly available, large, and complex longitudinal datasets. This R25 application is to develop and offer an “Interdisciplinary Summer Institute on the Analysis of Complex, Large-Scale Longitudinal Data”, refining it each year based on evaluation data (aim 1). We will also leverage this program to train graduate students to teach advanced longitudinal methods to participants from multiple disciplines (aim 2). Thus, we will serve two groups: program participants (aim 1), and Purdue graduate student teaching assistants (TAs, aim 2). During an immersive week-long summer institute each year, we will train 50 interdisciplinary participants including students, postdocs and faculty across academic institutions (Y1-Y3), expanding to also include professionals in non-profits, governmental agencies, and industries (Y2, Y3). The course is organized in 10 topics: publicly available longitudinal data sources, introduction to longitudinal data analytic methods, data visualization, missing data, longitudinal categorical data analysis, sampling weights and clustering/ stratification, time varying and time-invariant covariate inclusion, combining multiple data sources, embedded family-based designs, and an intro to sociogenomics—emphasizing cross-cutting themes of data management, visualization and communication, causal inference, measurement and modeling decisions, meaningful effect sizes, and representativeness. Lecture examples and assignments will focus on substance use and associated factors and will use the Adolescent Brain and Cognitive Development study data, although participants will be encouraged to use whatever dataset is most relevant to their own research interests. The summer institute will also feature TAs and additional faculty instructors circulating the room in each session to support students in need of extra assistance in real-time, as well as review and office hour sessions, experience in interdisciplinary environments, networking, and joint practice opportunities to help establish collaborations. We will also train 6 graduate student TAs each year, who will gain supervised experience in content development, instruction (via review sessions), consulting, course evaluation, and leadership within interdisciplinary environments. We have carefully designed recruitment strategies to train students across an array of disciplines career stages and paths), and a multi-pronged evaluation plan. Our program faculty includes 8 faculty experts in longitudinal data analysis and instruction, representing different fields and career stages.
FuSe2 Topic 3: Strain and Temperature Ex-Situ Processing of Ferroelectric Oxides (STEP FOx) for BEOL Performance$1,168,342
NSF Awards · FY 2024 · 2024-09
Nontechnical Description The demand for energy needed to store and process data is growing at an unsustainable rate. Data centers alone consumed over one percent of all global electricity use in 2022 and are projected to double their consumption in the near future. Much of this energy is not even used for doing actual computation. It is instead spent simply moving data to, and from, memory. To overcome this problem, dense, monolithic memory solutions built into, or on top of, the computing logic are needed. Ferroelectric hafnia-based compounds provide a potential solution. Ferroelectrics have a switchable electric polarization with potential for use in energy efficient devices. Critically, hafnia-based compounds can be integrated into modern logic devices while maintaining their memory-enabling ferroelectric properties. Unfortunately, ferroelectric hafnium oxide devices do not yet meet required endurance targets when fabricated in realistic geometries under the required processing conditions. This project addresses this challenge with a co-design framework that links materials science, advanced thermal and mechanical characterization, and machine learning with memory element design. Thus, investigators will maximize ferroelectric performance and endurance of ferroelectric hafnia in geometries typical of modern memory devices. This project addresses the multi-faceted reality of modern semiconductor systems which requires a multidisciplinary workforce. The project will provide research, internship, and touring opportunities built organically from our project’s significant “non-electrical” thermo-mechanical component. These opportunities, combined with direct messaging to students outside electrical and computer engineering, enhance semiconductor recruiting from under-represented, but vital, backgrounds. Technical Description The project’s technical objective centers on developing the material processes and resulting devices that maximize ferroelectric performance and endurance of trench capacitors analogous to those used in dynamic random access memory (DRAM) within the process envelope of the back end of line (BEOL). Realizing this objective will require addressing fundamental questions on how to cultivate the ferroelectric phase, defect, and endurance properties under these constraints. To answer these questions, the project will leverage post-synthesis, nanophotonic enhanced laser anneals with in-depth defect, phase, and strain characterization. Two major technical outcomes will result. First, the project will demonstrate non-planar, hafnia-based devices exhibiting greater than 10^15 switching endurance under BEOL conditions. Second, a suite of thermomechanical tools for imaging phase, strain, and defects in scaled layers (<50 nm) will be developed. The characterization suite’s development will occur in tandem with first-principles analysis to enable a machine-learning driven “process finder”. In total, the project will have impact beyond ferroelectric memory devices: The project is not creating a single process for a particular material; it’s enabling a paradigm for “in place” material development extensible to other dielectric 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.
FuSe2 Topic 2: High Resolution Imaging of Defects in Semiconductors: Detection, Reliability, and Mitigation$1,204,958
NSF Awards · FY 2024 · 2024-09
The project aims to address emerging challenges in the field of materials and computing, specifically focusing on Heterogeneous Integration (HI) in Packaging (HIP). As the density of Interconnected Circuits (ICs) in 2D and 3D packages increases, new defects that affect reliability are emerging. These defects can lead to losses in performance, reliability, and overall lifespan of semiconductor devices. By developing rapid 3D metrology techniques, and incorporating machine learning, this project aims to accelerate the rate of detection of fabrication defects thereby accelerating the development of next generation of semiconductor packages. The findings of this project have the broader potential to revolutionize the semiconductor industry by improving the reliability and performance of electronic packaging. Furthermore, the project aims to enhance materials education and public awareness of the importance of semiconductors and HIP, 3D imaging, and computational modeling of semiconductor packages. The technical aspect of the project involves the development of advanced metrologies and predictive multiscale modeling tools. These tools will be designed to capture defects within HI packages at multiple length scales and predict the most critical defects to device failure. The project proposes a transformational approach for rapid defect detection in next-generation HIPs and the quantification of the impact of these defects on package reliability. This approach includes a framework for probabilistic and uncertainty analysis to assess the probability of failure of a particular component. The project will involve a rigorous fusion of 3D materials characterization, materials science, mechanics, and machine learning, coupled with numerical and analytical modeling across different length scales. The broader impact includes opportunities for undergraduate students of various backgrounds and mentorship of students who wish to go into academia. A diverse educational and outreach program is integrated within the research program. This coordinated approach will enable a new paradigm for defect detection and reliability, as well as training the next generation of students for the semiconductor industry. 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.
Research: Prompting Socially Engaged Divergent Thinking in Engineering Design by Leveraging Generative AI$414,234
NSF Awards · FY 2024 · 2024-09
Engineering students often struggle to identify and account for the full range of members of society in their engineering design work. Students may additionally struggle to assess various potential impacts of designed technologies on society. These challenges stem from several factors including time and scope constraints on curricular projects and lack of institutional support for instructors who aspire to teach about social impacts of engineering. The consequence is that engineering graduates may be ill-prepared to integrate the public well-being into their engineering work; worse, they may view such societal considerations as unimportant. This project will investigate opportunities and obstacles related to using Generative AI – specifically ChatGPT 3.5 – to support undergraduate engineering students in identifying and accounting for broad populations and societal impacts during engineering design. Through pilot testing, the research team has found that ChatGPT can provide suggestions that encourage engineering students to consider society more broadly in their engineering design work; however, research is needed to understand the implications of this use of Generative AI for student learning and for engineering curricula. This project will contribute to national calls for resources to ensure students can use emergent technologies such as Generative AI in ethical and effective ways, as well as the RFE program’s call for “research that addresses ways in which new technologies (such as artificial intelligence and machine learning) are changing engineering education.” This project will investigate three research questions related to the potential of Generative AI to support socially engaged divergent thinking in design projects, or the ability to identify and integrate diverse members of society and wide-ranging societal factors into one’s engineering thinking. These questions are (1) According to engineering faculty, what are the strengths, weaknesses, opportunities, and threats of incorporating Generative AI into engineering design courses? (2) How does Generative AI impact engineering students’ socially engaged divergent thinking during a design challenge?, and (3) What do engineering design content experts view as the pros and cons of using Generative AI for prompting socially engaged divergent thinking? These research questions will be addressed through interviews with engineering faculty (RQ1), think-aloud data from engineering students who will use ChatGPT 3.5 to complete a simulated design task (RQ2), and workshops with engineering design scholars (RQ3). This project will produce new knowledge regarding how Generative AI is and could be used within engineering design curricula. While our focus in this study is engineering design education, project findings can inform efforts in other engineering curricular contexts. Another core project outcome will be a toolkit for socially engaged Generative AI use in design projects that will incorporate the perspectives of engineering design instructors, engineering students, and engineering design scholars. By engaging multiple stakeholder perspectives throughout this project, the research team aims to develop a toolkit that is accessible, appropriate, and robust for use in diverse engineering curricular design contexts across the US. This toolkit will be publicly available to augment existing engineering design pedagogy and to support engineering students in integrating broad sets of stakeholders and societal considerations into their design work. 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.
Elucidate Gene-environmental interactions employed by Pb in promoting ADRD in aging brains$769,298
NIH Research Projects · FY 2025 · 2024-09
Project Summary Alzheimer's disease and related dementia (AD/ADRD) affect a large percentage of population in the US creating a signficant health care burnden to society. A few environmental stressors have been identified as risk factors for ADRD, including lead (Pb). Exposure to these chemicals particularly during vulnerable developmental periods can result in adverse neurological outcomes later in life, including ADRD. However, less is known about the genetic risk factors that act as confounding factors with Pb contributing to ADRD onset. Furthermore, Pb exposure can affect aging hallmarks, while aging is a well-established risk factor for ADRD. Hence, it is crucial to understand how developmental Pb exposure interacts with aging hallmarks, ultimately influencing the biological “age” of the CNS and its repercussions. The goal of the proposed work is to identify and validate Pb- interacting genetic risk factors contributing to the etiology of ADRD in “age”-matched human neuronal models. Our preliminary studies verified the presence of ADRD pathogenic marks in 2D neuronal culture with developmental Pb exposure; and demonstrated the feasibility of using human mouse chimeric brain to provide the proper extracellular environment for neuron aging. Novel engineering platform to enable neuronal sorting based on the formation of tau aggregation; and track changes in age-related epigenetic features were also demonstrated which collectively form a strong technical foundation for our two research aims. In SA1, we will idenitfy genetic risk factors coupled with developmental Pb exposure that contribute to ADRD etiology. To do that, we will establish and validate reporter plamsids for ADRD pathogensis; screen for genentic risk factors in 2D culture models for Pb-coupling genetic risk factors; and validate select genetic targets in human neuronal models (2D/chimera). In SA2, we will characterize changes in aging hallmakers after Pb exposure and recaptiulate “aging” features in 2D culture. To do that, we will characterize the effects of Pb exposure on the installation of aging hallmarks in human mouse chimeric model and 2D culture focusing on nucleus, mitochondrion and mitochodnrial-nuclear signaling. The collective knowledge will guide the design of an “age” accelerating mixture that can facilitate neuronal maturation and degeneration in 2D cultures. Upon completion, we will develop an enabling platform for identification of GxE interactions intricated by developmental Pb exposure in aging brains; and verify our central hypothesis that Pb influences the development of ADRD by intertwining its effects with other genetic risk factors and the aging process.
CAREER: Solving Beyond-NP Satisfiability Modulo Counting Problems with Guarantees Using NP Oracles$527,281
NSF Awards · FY 2024 · 2024-09
Many real-world problems involve planning and decision-making. For example, during natural disasters, city planners must secure multiple paths to emergency shelters for their residents in case of natural disasters. Other examples can be found in disaster preparation, bio-diversity protection and secure energy supply. Another example in machine learning is that of confirming beyond doubt that a drug has a positive effect on a disease. This proposal introduces novel algorithms for such solving complex problems. This research will have a large impact on AI for social good and for science by solving real-world complex problems that require planning and decision-making. The developed algorithms will provide useful diagnosis tools for explainable AI and will have the potential to accelerate the learning of physics models in AI for science. The project will unite symbolic and statistical inference by introducing Satisfiability Modulo Counting (SMC). SMC unites the two types of inference because satisfiability solvers are one of the most widely used symbolic reasoning tools, and weighted model counting subsumes statistical inference. The investigator will design new algorithms with tighter guarantees than previous approaches for SMC. These algorithms will enable solving otherwise beyond reach tasks in statistical inference, structured learning, game theory, operational research, and inverse reinforcement learning. The investigator also intends to grow the field of symbolic and statistical AI integration via SMC solving, further extending the current community effort and bridging satisfiability and algorithmic research with applied AI and machine learning research. This will be achieved via: (1) demonstrating the efficacy of the developed algorithms in solving important real-world SMC problems in AI for social good and for science; (2) nurturing an eco-system of SMC benchmark creation, solver development, and idea communications via hosting SMC competitions; (3) developing workshops, courses and talk series. 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.
Frameworks: Cyberinfrastructure for Urban Tree Resilience and Environmental Equity (uTREE)$3,498,053
NSF Awards · FY 2024 · 2024-09
With 89% of the U.S. population and 68% of the world population projected to live in cities by 2050, rising urbanization will worsen already significant environmental challenges such as excessive heat, poor air quality, and rainwater runoff. With urban trees recognized for their potential to ameliorate these issues, many large cities are allocating resources for obtaining the accurate up-to-date tree inventories necessary for managing urban forests. However, cities currently collect data through labor-intensive, one-time manual surveys of public trees or via coarse low accuracy canopy cover estimation. In addition, these methods lack access to the significant portion of trees on private property. As a result, urban research and management communities lack critical information about tree density, tree species, locations of trees across different land types, and changes of tree counts over time and events. This project will address time-critical gaps of data completeness, accuracy, and equity and of tool availability with the development of the first-ever cloud-based cyberinfrastructure (CI) supporting national (and potentially worldwide) urban tree inventory estimation that in turn will improve multidisciplinary urban research, engineering, and planning to yield safer and more livable cities into the future. Moreover, sustaining the CI will need gradually less computation as the system benefits from trained generative modeling and transfer learning. In addition to broad dissemination to science and engineering communities and to urban stakeholders and practitioners, the effort will target recruitment of underrepresented minorities through campus programs in order to broaden participation of diverse graduate students in ecological and computer science-related disciplines. Researchers armed with the increased computational power and advancement of recent novel urban canopy parameterization models, urban ecological models and urban social science linkages are now providing dramatically improved abilities to perform comprehensive predictions. However, the lack of high-resolution tree data integrated with such powerful simulation tools, particularly in under-resourced communities, remains a significant barrier to transformative impacts of urban canopy estimations and predictions. This CI will use a spatio-temporal generative artificial intelligence approach capable of determining highly accurate locations and species of urban trees in cities. Further, the team will address non-trivial challenges in data management and CI interoperability to enable research workflows that seamlessly integrate big remote sensing data, desktop simulation tools, HPC-optimized simulation models, and web-based interactive dashboards to provide a set of diverse tools capable of performing mitigation simulations and visualizations. Building on team members' prior experience and a prior NSF Elements project, current tools will be extended to automatically link urban tree-related data to the community’s urban parameterization models. The project will impact a broad community represented by committed partners of WRF/NCAR, National DOE Urban Integrated Field Labs, WUDAPT, NSF LTERs, NSF Accelnet: GLASSNET, i-Tree, CRTI, KIB/KAB, and ArbNet. The uTREE CI will be extensible, portable, and scalable to serve a large and multi-disciplinary community of 60,000 researchers and 500,000 urban practitioners and at the outset will impact an initial 3.6M urban citizens and 9M trees (Chicago and Indianapolis). Next-generation urban computational and ecological researchers and managers will gain critical exposure to data science concepts in urban tree research and computational data analytics and visualization. Nearly one-third of the Earth's land surface is covered by forests, which host the majority of terrestrial biodiversity. Accurate mapping and monitoring of forests across large regions and over time is critical for mitigating climate and natural hazards, managing natural resources and protection of vital ecosystems. While traditional ground-based measurements of plant species and size provide the most accurate data on forest structure and above ground biomass, these methods become impractical when covering large areas with high-frequency repeat cycles. Airborne and Space-based remote sensing techniques provide a timely and cost-effective way to assess forest structure and biomass on regional to global scales. Satellite missions from NASA and ESA have sensors that gather data with significantly more frequent repeat cycles compared to in situ measurements or aerial surveys. While these satellite missions offer global coverage, some provide only sparse data on forest structure and need to be combined with other data sources for producing comprehensive and accurate wall-to-wall maps. There is a lack of efficient frameworks that utilize multi-source remote sensing data to produce wall-to-wall forest structure or above ground biomass at temporal and spatial scales necessary for effective forest management or use in hazard mitigation and monitoring applications. Without significant improvements to existing methodologies and looking beyond traditional data sources, efficient and accurate monitoring of forest structure and above ground biomass will remain limited. OpenForest4D will allow a wide range of users to generate on-demand and up-to-date research-grade forest structure and above ground biomass estimates across a range of timescales. This will be achieved by applying novel statistical models and artificial intelligence methodologies on a fusion of multi-source remote sensing data from ground, airborne and spaceborne platforms. Providing these cyberinfrastructure services through easily accessible interactive web-based interfaces, along with educational resources focused on the underlying domain science, will facilitate transformative research in forest sciences and ecology and encourage broad community participation. OpenForest4D's web-based educational resources, published curriculum materials, and live webinars will help develop a diverse, globally competitive STEM workforce. This award by the Office of Advanced Cyberinfrastructure is jointly supported by NSF's National Discovery Cloud for Climate initiative. 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.
Reproductive Healthcare Deserts and Maternal and Infant Health$150,741
NIH Research Projects · FY 2025 · 2024-09
Modified Specific Aims Section Access to reproductive healthcare in the U.S. has become increasingly constrained. A growing body of research has focused on identifying the national- or state-level policies that affect access to care.1–6 Less attention, however, has been paid to documenting access to reproductive healthcare at the local level. This is important because a primary way that policies affect access is through the expansion or contraction of health facilities. Understanding how individuals and local communities experience changes in access to care necessitates a finer-grained measure than the state level, although no data currently exist that would allow for such an approach. Local access to reproductive healthcare, including contraception, maternity, and abortion services, can impact reproductive autonomy and maternal and infant health. “Contraceptive deserts,” or localities with limited or no access to contraception, are becoming more prevalent across the U.S. and threaten women’s ability to time, space, and limit their births.7,8 Access to maternity care is also declining, particularly in rural areas, leading to “maternity care deserts.”9,10 Poor access to maternity services creates barriers to prenatal care and safe delivery, both of which enhance maternal and infant health. Maternity care deserts also complicate postpartum contraceptive use as women often seek information about the best timing for their next pregnancy and contraceptive use after delivery.11 Finally, access to abortion services provides a third pathway to avoid unintended births and their health impacts.12 And yet, living in a county without an abortion provider (“abortion desert”) has become increasingly common.13 Research on contraceptive, maternity, and abortion services is highly siloed, reducing our understanding of whether these care deserts overlap and of the effects of living in a locality lacking services across all three domains, or what is called a “reproductive healthcare desert.” These deserts are likely to overlap geographically and contribute to variations in health outcomes.8,14,15 Building on our team’s expertise in sexual and reproductive health, data integration and management, geospatial science, and robust longitudinal analyses, the aims of the proposed research are to: Aim 1: Create the Reproductive Healthcare Deserts (RHD) longitudinal dataset by combining indicators of access to contraceptive, maternity, and abortion care between 2009-2021 at the U.S. county level. To create this unique dataset, we will integrate data sources on (a) access to publicly funded contraceptive care at Title X clinics from the Office of Population Affairs and at Federally Qualified Health Centers from the Centers for Medicare & Medicaid Services; (b) access to maternity care from the Health Resources and Service Administration; and (c) access to abortion care from the Myers Abortion Facility Database. Aim 2: Use the RHD dataset to document access to reproductive healthcare at the county level between 2009-2021. Working in collaboration with a geospatial data scientist, we will create an interactive dashboard that will map the RHD dataset and include time-varying county-level characteristics obtained from supplementary datasets (e.g., American Community Survey). The dashboard will guide our descriptive research into the spatial patterns and scale of changes to reproductive healthcare access over the study period. Aim 3: Evaluate the relationship between variation in access to reproductive healthcare and individual-level indicators of reproductive autonomy and maternal and infant health. We will merge the RHD dataset with restricted-use data from the National Vital Statistics System and the National Survey of Family Growth to develop spatially clustered multilevel models analyzing the relationship between county-level reproductive healthcare access and individual-level measures of maternal morbidity, infant health, interbirth intervals, contraceptive use, and unintended birth. Aim 3a: Assess whether access mediates individual-level differences in reproductive autonomy and maternal and infant health. Taken together, this project will provide a new comprehensive measure of reproductive healthcare, a novel dataset and interactive dashboard that will track changes over a 13-year period, and robust evidence of how reproductive healthcare deserts are related to reproductive autonomy and maternal and infant health. Our study is in response to NOT-HD-23-003 “Research to Improve Pre-pregnancy Care and Enhance Healthy Birth Intervals.” The first iteration of the RHD dataset will predate the current era of rapid change to reproductive healthcare access but will provide a proof of concept of the construct of reproductive healthcare deserts and a model for examining how policy changes translate to local access to reproductive healthcare moving forward.
eMB: Explainable and Physics-Informed Machine Learning for Cell Typing via a Modern Optimization Lens$376,162
NSF Awards · FY 2024 · 2024-09
Human-induced pluripotent stem cells (hiPSCs) represent a groundbreaking advancement in stem cell research. Derived from skin or blood cells, hiPSCs are reprogrammed to an embryonic-like state, enabling them to differentiate into any cell type, such as blood, immune, heart, and neuron cells. This Nobel Prize-winning technology circumvents the ethical issues associated with human embryonic stem cells and provides valuable models for studying human development, disease, drug testing, and potential cell-based therapies. However, to leverage hiPSCs in clinical settings and large-scale manufacturing, there are significant challenges to overcome. One major challenge is accurately identifying cell types at different stages of differentiation, which is crucial for ensuring the cells perform their intended functions. Traditional experimental methods for cell identification can be costly, time-consuming, and limited in robustness. This research aims to address these challenges by developing explainable and physics-informed machine learning models. These models will enhance the accuracy and reliability of cell type identification, ensuring that hiPSC technology can be widely adopted in clinical and industrial applications, ultimately benefiting society through improved healthcare solutions and advancing our understanding of human biology. The project will involve both graduate and undergraduate students, with graduate students focusing on core theory and method development while undergraduates investigate applications. The PIs will work with Purdue’s Research Experience for Undergraduates (REU), and Summer Vertically Integrated Projects (VIP) program to mentor additional underrepresented minority students each summer to work on interdisciplinary research in stem cell engineering and machine learning. Outreach activities will include developing hands-on K-12 activities, partnering with local organizations, organizing lab tours, and presenting research at the "Mending Broken Hearts" gallery exhibit, aiming to increase STEM participation among underrepresented groups. This research project addresses critical challenges in the adoption and scalability of human-induced pluripotent stem cells (hiPSCs) by developing novel machine learning methodologies. The specific problems targeted include the need for high-accuracy, cost-effective cell type identification during differentiation and the incorporation of prior biological knowledge into explaining machine learning models. The PIs intend to create explainable machine learning algorithms that leverage single-cell RNA sequencing (scRNA-seq) and imaging data to provide counterfactual explanations, highlighting key genes or image features critical for cell typing. These models will utilize mixed-integer programming to solve counterfactual explanations to generate interpretable predictions, addressing the limitations of current black-box approaches. Additionally, the aim is to overcome data scarcity by integrating biological knowledge into the machine learning frameworks, employing novel physics-informed machine learning algorithms. This research will develop and benchmark these innovative methods, applying them to the study of Tumor Associated Neutrophils (TANs) for cancer therapy. By enhancing explainability in cell typing predictions, this work will significantly advance the field of stem cell research and its applications in regenerative medicine and oncology. This project is jointly funded by the Mathematical Biology Program in the Division of Mathematical Sciences, the Infrastructure Innovation for Biological Research in the Division of Biological Infrastructure (BIO/DBI), and Office of Strategic Initiatives. 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.
Proteomic Characterization of EV RNA-Binding Proteome Landscape in Cell-Cell Communication$280,000
NSF Awards · FY 2024 · 2024-09
With support from the Chemical Measurement and Imaging Program and the Chemistry of Life Processes Program in the Division of Chemistry, Professor W. Andy Tao's group at Purdue University is developing a new mass spectrometry-based strategy to enable the analysis of RNA-binding proteins that play important roles when cells communicate with each other. Extracellular vesicles (EVs) are nano- or micro-size particles released by all kinds of cells. Their importance has been increasingly realized through the discovery of transferring biological molecules such as proteins and RNAs from one cell to another by EVs. The goal of this research is to develop chemical tools that introduce a tag on RNAs in EVs so that when RNAs are transferred from one to another by EVs, proteins interacting with RNAs from EVs can be isolated using the tag and then analyzed by mass spectrometry. This interdisciplinary project incorporates elements of chemical biology, instrumentation, and bioinformatics. Education-outreach activities are integrated with the research effort and are designed to directly impact undergraduate students through their educational and research experience. The research will engage early year students in a research project that emphasizes the use of modern analytical methods for the isolation and analysis of biological molecules and encourage undergraduate students enrolled in the new chemical biology major at Purdue University, to think of science in a broad, discovery-based manner. EVs have emerged as important messengers in cell-cell communication by transferring biological molecules such as nucleic acids, proteins, and metabolites to recipient cells, affecting the function and activity of recipient cells. EV RNAs have been discovered to play key roles in intercellular communications by regulating gene expression and other cellular processes in recipient cells. A systematic analysis of EV RNA-binding proteomes in recipient cells could provide dynamic insights into the molecular mechanisms that are responsible for EV functions. This NSF project will introduce a systemic strategy based on chemical proteomics to profile the landscape of EV RNA interactomes in recipient cells. RNAs will be labeled using non-nature uridine (U) and transferred from cells to EVs, which allows for the full characterization of RNA-binding proteome in cells and in EVs, and most importantly, EV RNA-binding proteome in recipient cells. The novel chemical proteomic strategy will be fully examined with HEK 293 cells Jurket T cells, and CD8+ T cells. 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.
CNS Core: Small: NSF-MeitY: A Unified Framework for Video Analytics Optimization and Adaptation$496,895
NSF Awards · FY 2024 · 2024-09
Edge-cloud video analytics systems, also known as video analytics pipelines (VAPs), are being deployed in major cities around the world to support diverse applications, spanning public safety, transportation, healthcare, retail, and more. Unfortunately, the status quo of developing and deploying a VAP for a new application is largely manual and labor-intensive. (1) A VAP developer must implement end-to-end pipelines on heterogeneous hardware by writing low-level software for each component. (2) The developer must pick and choose the right set of machine learning models and their placements pre-deployment to minimize the cloud computing bill while providing acceptable latency and accuracy. (3) The developer must adapt the pipeline post-deployment in response to changes in the environment (e.g., network bandwidth, light conditions, traffic density). Each step is challenging to perform, presenting significant hurdles to the development and deployment of new VAP applications. This project aims to develop a unified framework to simplify and automate video analytics pipeline development, optimization, and adaptation by streamlining all three steps in developing and deploying a new VAP application. Under such a framework, application domain experts specify high-level analytics tasks (logical operators) to be performed on the camera frames and all candidate physical implementations for each logical operator (physical operators). Pipeline authors describe the pipelines via graphs, and the framework will automatically generate an optimal physical implementation for initial deployment and deploy an adaptation engine that monitors changes in environmental conditions and automates adaptation to new physical plans that satisfy application latency and accuracy constraints. The project will have direct, practical implications to the video analytics industry and is poised for substantial societal impacts. (1) Impact on industry: The proposed VAP framework will advance the state-of-the-art by providing a much-needed solution that significantly eases the development effort of VAP vendors and shortens the time-to-deployment of new and increasingly diverse VAP applications. (2) Impact on society: The technologies developed for enabling the framework will foster wide adoption of important societal VAP applications, spanning transportation, healthcare, retail, public safety, and more. (3) Impact on other research fields: The work will have far-reaching impacts outside the area of video analytics systems by developing general query optimization techniques, which will also be applicable to traditional database management. The technologies developed in the project will be disseminated and transferred to the broader research community and the IT industry. 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.
Probing Novel Out-of-Equilibrium Quantum Dynamics in Homogeneous Atomic Quantum Gases$430,309
NSF Awards · FY 2024 · 2024-09
Dynamical systems with intrinsic instability can amplify small initial perturbations to produce fascinating features we experience daily, from cloud and pattern formation in weather and geography, freak waves in oceanography, to structure formation in the universe. Such dynamics in quantum many-body systems present interesting but challenging problems because of the need to consider quantum correlations between the constituent particles. An instability could amplify initial quantum fluctuations, noise that exists even without external or thermal perturbations, into an observable pattern that could show distinct behaviors compared with those found in classical systems. Understanding quantum instability dynamics could lead to a better understanding of microscopic to mesoscopic quantum phenomena and new applications such as quantum-enhanced amplifiers and sensors. In this project, the research team will use ultracold atomic gases to form a uniform superfluid and study its out-of-equilibrium behavior and instability-induced quantum dynamics. The first project goal aims at exploring the dynamics of ultracold atoms brought to an attractive interaction so that the system becomes unstable against density perturbations. The team will study how these perturbations evolve due to a pattern forming instability and detect quantum correlations within the system. In the second project goal, the team will implement a new scheme to make a superfluid flow faster than the speed of sound and study the instability of the supersonic flow. The aim is to examine how energy flow is dissipated in the system, which may find connections with important topics in condensed matter and plasma physics, and astronomy. This project will provide training for multiple PhD students and undergraduate research assistants, preparing them for future physics careers. The project will also support the PI’s continuing participation in outreach activities at Purdue University, including an instrumentation development program with the active involvement of service-learning undergraduate students and local high school students. The PI and the team will use ultracold cesium atoms trapped in an optical box to study novel nonequilibrium physics and instability-induced quantum dynamics. Ultracold atoms present an ideal testbed for studying out-of-equilibrium quantum dynamics because of the well-developed control toolbox for accessing instability physics with precise timing and for probing them with many details. The tools include precision tuning of atomic interactions through a Feshbach resonance and arbitrary potential-shaping that could induce dynamics with high spatiotemporal resolution. High-resolution in-situ imaging, time-of-flight measurements, and interferometry will provide complementary information on the density distribution, momentum state population, and long-range phase coherence. The project goals include probing quantum many-body dynamics in an attractive Bose gas and supersonic turbulence in a superfluid. The first goal aims at studying the dynamics of quantum many-body breathers, distinct from mean-field breathers, exploring a novel quantum phase transition in a one-dimensional Bose gas that occurs even at zero temperature, and detecting the non-local correlations and quantum entanglement in a novel state of a Bose condensate following an interaction quench. The second goal aims at using engineered dissipation to induce supersonic flows in a superfluid and to observe the instability of the flow. This technique allows the study of supersonic turbulence in a compressible, zero-viscosity quantum fluid. The engineered supersonic flow also promises a new way of generating sonic black holes. Success in this project could lead to a better understanding of instabilities and entanglement generation in quantum many-body systems, quantum thermalization problems, dynamical quantum phase transitions, and quantum turbulence in supersonic flows. 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.
Collaborative Research: RUI: Resolving the effects of lithospheric foundering on orogenesis: An example from the southern Puna plateau, Argentina$118,260
NSF Awards · FY 2024 · 2024-09
Earth’s largest mountain belts and high plateaus are supported by thick, buoyant crust and an anomalous lack of dense lower crust and mantle lithosphere. This project investigates the processes by which the lower crust and mantle lithosphere are removed by detaching and sinking, known as foundering, and the imprint of such processes on the geologic record. This research focuses on the southern Puna plateau, NW Argentina, where multiple lithosphere removal events are hypothesized from geological, geophysical, and geochemical datasets. By generating new geological datasets and numerical modeling results, the project will test the geological evidence and physical feasibility of hypothesized modes of lithosphere removal. The project supports 2 PhD students and at least 7 undergraduate researchers, who will be recruited from underrepresented minority groups, as well as 2 postdoctoral researchers and 3 early-career PIs. The project builds connections between North American and Argentinian scientists via collaboration on the project, running a research field trip with multiple research groups working in the southern Puna plateau, and hosting a numerical modeling short course in Argentina. The themes of the project, including the effects of density on the evolution of mountain belts, serve as the basis for a new annual field trip and retreat to build belonging among students at Utah Tech University (UTU), which is a highly affordable, open-enrollment, undergraduate-serving institution. To test hypothesized foundering mechanisms, this study employs a combination of field and numerical modelling work focusing on the southern Puna plateau, NW Argentina, a case locality for building large mountain belts and plateaus. The Puna plateau preserves a unique sedimentary record of Cenozoic mountain-building, and Miocene lithospheric foundering has long been proposed from geophysical and geochemical datasets. Recent studies suggest foundering as an explanation for anomalous Miocene subsidence/shortening and uplift/extension of the Arizaro and Antofalla Basins, respectively, ~150 km apart. The opposite senses of deformation associated with these potential foundering events suggest that foundering may induce highly diverse modes of deformation in the overlying crust, potentially controlled by the thermal, compositional, or structural state of the crust. This multi-disciplinary project uses geologic mapping, measured stratigraphic sections, and geo- and thermochronology to better constrain the spatial-temporal distribution of deformation and subsidence in the southern Puna plateau, as well as cutting-edge numerical modeling to characterize controls on foundering style or other processes that may have driven deformation in the region. This integrated approach will allow rigorous testing of whether foundering is consistent with observations from the Antofalla and Arizaro Basins, and if so, what crustal parameters control the effects of foundering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Collaborative Research: Physics-Empowered Vision-Based Tactile Gel-Robots for Multi-Physical Perception and Ultra-Gentle Manipulation$326,308
NSF Awards · FY 2024 · 2024-09
This collaborative research project will enable new capabilities for robots to perceive and manipulate soft and fragile objects in applications such as surgery. A central innovation is a thin gel coating that changes its optical properties directly in response to external forces. These optical changes reveal the distribution of forces across the coating in colorful fringe patterns that appear on the surface, even when the applied forces are too small to significantly deform the coating shape. Specifically, this project will create tactile-based robots that integrate the color-changing gel into a force-interpreting optical system, giving the robot the capability to perceive mechanical and physical properties of soft and fragile objects and manipulate these objects without damage. This advancement will surpass existing tactile robots in areas such as medical robotics, assistive technologies, and mixed and virtual reality. In addition, this project will establish a unique platform for workforce development through educational and training activities in robotics and provide an inclusive avenue for engaging underrepresented groups in STEM disciplines. This project will integrate fatigue-resistant photoelastic gel into a stress-interpreting optical system for high-performance vision-based tactile gel-robots that can obtain multi-physical perception and execute ultra-gentle manipulation of soft and fragile objects. Specifically, this project will leverage the molecular design of fatigue-resistant photoelastic gels, the mechanical design of a stress-interpreting photometry system, and the algorithm design of physics-informed machine learning to perceive, visualize, and interpret robot-object interactions. Finally, this project will integrate material design, mechanical design, and algorithm design to build a physics-empowered, vision-based tactile gel-robot, and demonstrate robotic multi-physical perception and ultra-gentle handling capabilities previously unattainable (for example collecting fragile jellies for study in marine biology or cutting and manipulating foods like custards in assistive robotics). 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.

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