Regents of the University of Michigan - Ann Arbor

NSF Awards · FY 2024 · 2024-06

Many everyday tasks require overcoming automatic tendencies and impulses to act in accordance with current goals. Imagine getting into the car to drive to a new office. If one is not careful, one might drive the familiar route to the old office instead. Getting to the new workplace requires focusing attention to resolve the conflict between the older automatic responses (e.g., turn right at the first traffic light) and the new, more appropriate goal-directed response (turn left instead). The ability to resolve this conflict has been a focus of many researchers for decades, as it is an ability that is fundamental to human cognition and its dysfunction has been associated with many brain disorders (e.g., ADHD, dementia, depression). To further the understanding of conflict resolution, this project proposes a novel combination of behavioral methods, computational modeling, neuroimaging, and non-invasive brain stimulation. Rather than allowing participants to respond at any point in time, the planned method forces participants to respond at varying times during conflict resolution to interrogate the time course of how conflict is resolved. The project also plans to include undergraduates in research, including those from historically underrepresented groups. The study of conflict resolution has historically relied on analyses of participants’ free response times (RT) in classic cognitive control tasks such as the Stroop, Simon, and Flanker tasks. Although this approach has yielded valuable insights, researchers have recently identified critical limitations with using free RT that have led to an impasse in the understanding of the nature of conflict resolution and its underlying neural mechanisms. The project seeks to address these limitations by combining a forced-response method that generates speed-accuracy tradeoff functions for each participant with a computational model of the response preparation process underlying automatic and goal-directed responding. This project is composed of four research aims: 1) Validate the response preparation model with physiological responses at the muscle using electromyography; 2) Compare the response preparation model against prior sequential sampling models used for conflict tasks; 3) Combine computational modeling with functional neuroimaging (fMRI) and non-invasive brain stimulation (TMS) to assess the causal role of lateral prefrontal cortex in conflict resolution; 4) Assess the generality of the conflict-resolution mechanism across tasks with TMS and the response preparation model. Overall, the results from this project have the potential to deepen our understanding of the neural mechanisms underlying conflict resolution. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This project aims to serve the national interest by improving capacity in developing high-quality free, online, interactive mathematics textbooks, so that they facilitate students' learning in calculus and linear algebra. The PreTeXt-Runestone: Open Textbooks Engaging Undergraduates in STEM project advances our understanding of users' engagement with interactive features embedded in electronic textbooks by describing how such use supports teaching and learning. The project seeks answers to two interrelated questions: 1) How do groups of textbook users (students, teachers, authors, and researchers) work together to write interactive textbook questions designed to elicit multiple ways of students' thinking in classrooms? and 2) How do students and instructors use those interactive textbook questions in real classrooms? The project focuses on open-source PreTeXt textbooks hosted in the learning analytics platform, Runestone Academy. The project addresses the important need to promote student learning through continued engagement with the material they are learning and to provide tools for teachers to engage with the information gathered using those questions - not only their responses but also the various attempts to answer the questions. The goal of the project is to study the design and classroom use of three types of interactive textbook questions, Matching Exercises, Parsons Problems, and Reading Questions. The questions will be added to four widely used interactive textbooks in calculus and linear algebra. Through three design-based research cycles, the project will document the process (strategies and heuristics) through which four Student-Teacher-Author-Researcher development teams create questions that target core mathematical notions or skills tailored to each textbook and that make evident patterns of reasoning among students. Each cycle will include the development, integration, use, and evaluation of the questions in actual classrooms, with each iteration widening the scope of the project, with eight courses in the first cycle, 24 courses in the second cycle, and 40 courses in the third cycle for an estimated total of 72 instructors and at least 1200 students. WestEd will provide continued formative evaluation on all aspects of the process and will help identify processes in the development of the questions. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

The objective of this Pathway to Enable Open-Source Ecosystems (POSE) Phase II project is to further develop the ecosystem for the Open-Source Leg prosthesis, which gives researchers access to a fully capable, standardized hardware and software platform. The intent of the Open-Source Leg is to lower the barrier to studying the challenges of controlling robotic prosthetic legs, which is among the greatest obstacles hindering their widespread use and clinical impact. The Open-Source Leg enables researchers to more easily study and compare different control strategies without the prohibitive cost of developing a robotic leg from scratch. The overarching goal of this research is to accelerate the impact of the Open-Source Leg through the development of open-access materials and community interaction. To realize this vision, the aims of this project are threefold. First, the team will foster community engagement through purpose-built scaffolding mechanisms, including continuous communication with users, development of educational resources, and community events. Secondly, the team will further enhance its open-source infrastructure, leveraging web-based development tools for continuous integration and deployment. Finally, the team will assess and act upon the effectiveness and sustainability of the Open-Source Leg ecosystem to ensure its success and establish a foundation for long-term sustainability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This project will develop new algorithms for simulating on the computer a class of mathematical models that describe the evolution in time of a network of surfaces. These models play a prominent role in many applications. A very important example that will receive particular attention is the evolution of the internal structure (microstructure) of polycrystalline materials, such as most metals and ceramics, during manufacturing processes such as heat treatment (annealing). Polycrystalline materials are very common. They are composed of tiny crystallites, known as grains, stuck together. During annealing, the boundaries between the grains, described by a network of surfaces, start to move as some grains get larger, while others shrink and disappear. The shapes and sizes of the grains making up these materials are known to have profound implications for their physical properties, such as their strength and conductivity. Materials scientists have long had mathematical models that describe the motion of the network of grains; what has been lacking is accurate, efficient, reliable, and flexible numerical methods that would allow them to compare large scale simulations of their models against experimental measurements. In recent years, as experimental measurements of time evolution of the three dimensional internal structure of materials have become available, the need for algorithms to simulate the relevant models have become increasingly acute. The project will take steps to address this need. Resulting algorithms will be implemented in software, which will be made available to the broader scientific community. The project will also support the training and research of one graduate student working towards a Ph.D. in mathematics. The project will take a new approach to designing level set methods for multiphase geometric motions such as motion by mean curvature of networks of surfaces. It will exploit a precise, mathematical connection between a particular discretization of the level set formulation of motion by mean curvature, known as the median filter scheme, and another class of algorithms known as threshold dynamics. This will allow extending advantages of one method to the other. The advantage of threshold dynamics is its generality and highly developed theory of stability and convergence. In particular, recent advances in our theoretical understanding of threshold dynamics enabled its extension to the more elaborate microstructure evolution models of interest to materials scientists. Via its precise connection to median filter schemes, elements of this theory will be carried over to level set methods. The new level set methods will allow arbitrary, normal dependent (anisotropic) surface tensions and mobilities to be assigned to any interface in the network of surfaces — a level of generality that cannot even be attempted by most existing techniques. They will also allow subgrid accuracy in locating the interface even when implemented on uniform grids — a distinct advantage of the level set method over threshold dynamics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

A groundbreaking broadband probe-based RF and microwave measurement system is introduced, representing a significant advancement in electromagnetic measurement technology. The proposed RF probe acts as point transmitter and has a very small form factor (cross section area less than one square millimeter) fed by an optical fiber and thus is electromagnetically non-invasive. This pioneering technology enables the comprehensive assessment of the electromagnetic response of any receiving system to highly localized applied electric fields, marking the inaugural demonstration of a method capable of experimentally measuring the electromagnetic Green's function of receiver systems. The ability to experimentally measure a system's Green's function would represent a fundamental shift in electromagnetic measurements and characterization, unlocking a multitude of possibilities that were previously cumbersome or unattainable. The proposed probe heralds a new era of experimentation, facilitating endeavors such as: i) Precision measurement of antennas' receiving radiation patterns under any arbitrary incident field, whether in the near- or far-field regions; ii) Identification and localization of manufacturing defects and malfunctioning elements within extensive phased arrays and massive multiple-input multiple-output (MIMO) communication systems, streamlining diagnostics and maintenance; iii) Thorough characterization of electromagnetic interference and compatibility (EMI/EMC) in microwave and millimeter-wave integrated circuits (MMICs) and systems-on-chip (SOCs) with micron-level resolution, enhancing overall system reliability and performance; and iv) Investigation into the effects of RF and microwave radiation on biological tissues, offering high selectivity for applications such as cancer treatment, RF ablation, microwave hyperthermia, and RF dosimetry, thereby advancing medical science and therapy techniques. These examples underscore the broad spectrum of applications and the transformative potential of the proposed probe technology, paving the way for unprecedented advancements in electromagnetic research, engineering, and healthcare. By strategically maneuvering the localized transmitting probe across the target receiving system and recording receiver output at each excitation point, the system's response to any desired applied electric field can be fully characterized. Comprising three integral subsystems, the proposed measurement probe encompasses an optical modulator and optical fiber to convert the applied RF signal into an intensity-modulated laser light source. At the probe's tip, a series of photo-voltaic cells generate a high reverse bias voltage when illuminated by the optical signal, while an RF avalanche photodiode, also housed within the tip, demodulates the optical signal under the reverse bias from the photovoltaic cells. This process generates a highly localized, robust RF electric field across a miniaturized metallic dipole. Fabricated on a standard 65-nm bulk CMOS process, the probe confines the electric field to an area of 100 µm × 100 µm, achieving field intensities on the order of 10 kV/m. Remarkably versatile, a single probe is capable of measuring system responses from zero to 12 GHz with arbitrary polarization, enhancing its utility across a spectrum of applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This project aims to serve the national interest by enhancing Peer+, a free tool that supports Peer Instruction. When using Peer Instruction an instructor displays a question that students answer individually. The students then discuss the question with nearby peers and refine their answers. Peer+ will add two new ways for students to discuss the question with their peers through (1) a text-chat for answering questions during lecture, and (2) a pseudo text-chat for answering questions after lecture. While there is substantial evidence for the effectiveness of Peer Instruction, preliminary research at the University of Michigan has found that using text-chat during lectures improved learning. Peer Instruction is known to improve retention in STEM classes, especially for students from minoritized groups. Providing new types of peer discussion could further improve retention and thus increase the number and diversity of students who succeed in STEM classes. The research associated with this project will increase knowledge about effective STEM education and approaches that attempt to reduce barriers to adoption of Peer Instruction. Since it can be hard for instructors to find the time to adopt new teaching methods, a summer instructor workshop will be offered, and follow-up support will be provided. The project will investigate (1) the effect of three different modes for peer discussion on learning and student satisfaction at four institutions and in a variety of courses, (2) the effect of the Peer+ tool on student retention, and (3) how instructor attitudes towards and knowledge of Peer Instruction change due to a workshop, follow-up support, and use of Peer+. A design-based research approach will be used, based on theory, and the system will be evaluated in real educational settings. The research will be evaluated using both qualitative and quantitative measures. The NSF IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This research project will advance our fundamental understanding of the molecular players that coordinate gene expression in the cell. Through studying their structure and function, further knowledge will be gained on some of the essential processes that they are involved in such as regulation of the cell cycle and DNA repair. In addition, this project will provide research opportunities for a number of underrepresented students at Eastern Michigan University, a diverse, primarily undergraduate institution that values student-centered and inclusive learning. Over a dozen undergraduate students will be closely trained in research by a strong, collaborative duo of PIs with extensive backgrounds in mentoring future scientists and professionals. An additional 24 students will also receive the benefits of research through the continuation of a CURE-grant proposal Biochemistry Lab writing-intensive course (CHEM 453W). This project will also expand and strengthen an existing High School Research Experience at EMU program, which provides research training during the summer to students in grades 9-12 from the local Greater Detroit community. UHRF1 and UHRF2 are multi-domain epigenetic proteins that play critical roles in a variety of processes such as cell cycle regulation, DNA replication, DNA damage repair, and gene regulation. Both proteins contain two histone reader domains, called TTD and PHD, which recognize the post-translational modification (PTM) status on histone H3 to regulate DNA methylation/hydroxymethylation and gene expression. While these proteins share a high degree of sequence similarity, UHRF1 and UHRF2 have distinct and important nuclear functions that are mediated via chromatin interactions. Although much is known on the detailed histone binding properties by the TTD and PHD of UHRF1, there is still limited understanding of the structure and biological function of the same domains in UHRF2. This project will test if UHRF1 and UHRF2 exhibit distinct mode of histone binding and whether they can be specifically modulated by chemical probes. Elucidating the detailed differences between UHRF1 and UHRF2 will provide a greater understanding of the molecular requirements that dictate binding selectivity by these reader proteins. In addition, novel functionalities of UHRF2 may be uncovered by discovering new histone PTM binding partners and chemical probes that target UHRF2. An in-depth fundamental understanding of their structure, function, and ability to modulate their activities is an important step toward understanding the variety of critical cellular processes that UHRF1 and UHRF2 regulate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-01

Coordinated online harassment by collections of individuals and groups is a scourge of the modern Internet. It has upended and cost lives, silenced voices, and is making our public discourse more cruel and less representative. The phenomenon creates challenges for those who seek an equitable, secure and trustworthy internet to reduce the threat of coordinated attacks and handle attacks swiftly and effectively. Using the research team's past experiences with a clinical model that has been useful in helping victims of intimate partner violence, and new understandings of how to handle coordinated harassment to reduce harms and provide active assistance to targets of harassment, this project pilots an advice clinic. To ensure that the work has practical, real world impact, the project is also developing materials and working with platforms, threat intelligence companies, and non-profit organizations that help targets of online harassment . The project uses a comprehensive set of technical and human-centered methods to advance our understanding of coordinated harassment threats and mitigation techniques. The coordination of harassment allows harassers to scale their attacks, but also provides defenders with an opportunity to monitor attackers. This project will study how threat intelligence---an emerging area of cybersecurity that has enabled the blocking, detecting, and remediation of cyberattacks---can be used to monitor channels where coordinated harassment and doxing campaigns happen, understand escalation processes, identify pain points, and prioritize courses of action for platforms, law enforcement, and targeted individuals. The multidisciplinary team and mixed methods approach will enable the project to not only build sophisticated tools, but also build scientific knowledge in multiple fields and to understand whether and how the proposed tools can contribute solutions to a complex societal problem. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-01

This Faculty Early Career Development (CAREER) grant will support fundamental research on ultrasonic metal welding (UMW). Among the advantages of UMW over conventional fusion welding techniques are the ability to join dissimilar metals, energy efficiency, short welding cycles, and environmental friendliness, making it a promising joining technology for the advanced manufacturing of electrified and lightweight vehicles. Nevertheless, UMW has a relatively narrow operating window and is very sensitive to unpredictable, uncontrollable environmental conditions. This longstanding knowledge gap in the underlying process mechanisms makes the prediction and control of joint quality difficult, which limits its use. This project will take advantage of the emergent information-centric transformation of manufacturing science by leveraging advances in process physics, microstructural analysis, and data science. By establishing dynamic, stochastic relationships between process conditions, microstructural weld attributes, online sensing data, and weld performance, the research will advance the fundamental understanding of process mechanisms in UMW. The knowledge gained will be used to establish a suite of machine learning-based decision-making tools that will ultimately enable smart UMW. This grant will also support diverse educational and outreach activities that contribute to the education of the U.S. smart manufacturing workforce. It is a widely accepted hypothesis that UMW process conditions influence the joining performance via the dynamic evolution of micro-scale weld attributes and the weld formation process generates a signature, as reflected in parameters that can be sensed online. Nonetheless, there exist no studies to date that adequately model or quantify the inherent dynamic, stochastic process-attribute-data-performance (PADP) relationship. The overarching goal of this research is to create a PADP modeling framework that consists of innovative machine learning and statistical models. The framework will be completed in two steps. First, spatiotemporal models incorporating uncertainty quantification will be built to characterize the process-attribute-performance relationship. Second, a tensor-based correlation and regression analysis will be performed to investigate the attribute-data relationship. This framework will be further employed to develop a series of physics-aware, machine learning tools for process control, including process optimization, online quality monitoring, and real-time control. Finally, the project will investigate the use of a transfer learning methodology to provide a cost-effective way to build PADP models and decision-making strategies for related products or product families. This learning capability will be an essential component in the cloud intelligence that enables the smart manufacturing paradigm. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-01

Universal access to biological tissues for fundamental studies is limited, thereby constraining both the type and number of experiments that can be readily carried out. This is a particularly challenging problem for U.S. colleges and universities that do not possess the necessary infrastructure to further their tissue engineering research. This grant supports research to mitigate this challenge by extracting and storing tissue-structure information, which will be made broadly accessible to researchers, teachers, and students at any institution. The detailed information is obtained through the sequential process of imaging (reading), digitally storing, and laser-based manufacturing (writing) of the tissue architecture. Data obtained from this process will be uploaded onto an accessible data repository to facilitate broad dissemination. The project will also provide a platform to recruit students from diverse and underrepresented groups in STEM fields to learn about the emerging field of advanced biomanufacturing through strategic partnerships with local university chapters of engineering and science-based student affinity groups. Aspects of the research methods, as well as materials learned, will also be incorporated into both new and existing courses, and lecture modules developed for a new interdisciplinary online course on the freely accessible nanoHUB.org cyberinfrastructure platform. This award utilizes a convergence of disciplines to create a digital manufacturing platform, based on two-photon polymerization (TPP), that will enable cloud-based reading and writing of scaffolds with varying complexity in 3D collagen-fiber organization. Long-wavelength (near-infrared) optical pulses and long-working distance objectives will be used to enable penetration depths greater than 5x that has previously been reported, resulting in printed scaffolds volumes of 1 mm x 1 mm x 0.5 mm, which would be on the same scale as biologically relevant 3D in vitro models. The use of optical wavefront-shaping technology enables parallelization and reduction of writing artifacts, respectively. The machine-learning-based process control framework advances the fundamental understanding of TPP process variability, and facilitate high-throughput, high-fidelity biomanufacturing of scaffolds. This research contributes to the fields of statistics and machine learning by linking these disciplines to complex, unique data structures and types in biomanufacturing, as well as permit prototyping of collagen-based mechanical metamaterials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2023 · 2023-10

The United States is facing an unprecedented shortage of engineers who are skilled in artificial intelligence (AI). AI has the potential to transform all fields of engineering and technology, but this potential can only be realized if today’s engineering students choose to make AI part of their educational and career goals. This project will study how and why engineering students include or exclude AI from their educational and career goals. Results from this project will lay the groundwork for designing inclusive programs that meet tomorrow’s demands for a skilled AI workforce. This project aligns with national priorities as outlined in the National AI R&D Strategic Plan, among other federal policy documents. This project examines how undergraduate engineering students at a large, public engineering school navigate a career landscape that is being reshaped by AI. Grounded in Social Cognitive Career Theory, our qualitative study will answer the following questions: 1.) How do engineering undergraduates perceive academic and career options related to AI, and how do students describe these perceptions as influencing their academic and career plans? 2.) How are students’ outcome expectations related to AI coursework and careers similar to or different from their outcome expectations for coursework and careers in their traditional engineering major? 3.) How do the outcome expectations of students who are interested in AI careers differ from students who are interested in more conventional engineering careers? Long term, this work will help educators understand how AI can be brought into undergraduate engineering education without excluding students who initially do not have an interest or background in AI and without decreasing interest in traditional engineering careers. 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.