University Of California, Merced

NSF Awards · FY 2025 · 2025-07

This Engineering Research Initiation (ERI) award supports research that will contribute new knowledge in robotic removal of wood waste from forests, thereby promoting the progress of science, advancing the national health, prosperity and welfare. Forest fires cause severe damage to communities by destroying property and livelihoods. To mitigate wildfire risk and severity, several clearing methods are utilized, ranging from mechanical fuel reduction, to prescribed burning and to grazing by animals. For mechanical reduction, limbing - the removal of lower branches from trees — is one of the most critical methods for preventing crown fires, as these lower limbs act as "ladders" that help surface fires reach the canopy. However, limbing remains largely reliant on manual, hazardous and labor-intensive activities performed by forestry workers. This project look to provide needed knowledge for the development of robotic teams capable of autonomously cutting and catching lower tree limbs. The new robotic system looks to alleviate the workload of forestry workers in some of their most physically demanding activities, improving occupational health. It also intends to accelerate removal of hazardous fuels from the forest, contributing to wildfire prevention in the US. Results from this research intend to help protect US communities located near or within forests, as well as forest-based industries such as logging and tourism. The project will provide undergraduate and graduate students with essential knowledge in robotic wood waste removal and positively impact engineering education. This research aims to make fundamental contributions to establish a systematic framework for the design and control of cutting and catching robots for fuel reduction activities in forests. The research looks to achieve this goal by designing and optimizing novel rotary end effectors to perform robotic limbing on structurally integrated tree branches. Additionally, the research seeks to involve development of hierarchical control algorithms to plan, execute, and synchronize the motion of the cutting and catching robots. At the higher level, a centralized spiral-based planner looks to compute optimal trajectories for the cutter and catcher robots, considering tree geometry, kinematic and actuation constraints, and maximizing branch collection. At the lower level, each robot looks to operate under decentralized control, utilizing control barrier functions to closely follow the planned trajectory, while ensuring real-time adaptability to dynamic obstacles and environmental uncertainties. 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 2025 · 2025-07

Efficient data movement is a critical challenge in high-performance computing (HPC) and artificial intelligence (AI) cyberinfrastructures due to the massive volumes of data generated by modern data-intensive applications. Existing methods often struggle with performance bottlenecks, particularly when transferring data across parallel and distributed computing environments. To address these limitations, this project -- the Open DPU-Offloading data Transfer Architecture (OpenDOTA) -- provides a framework that leverages Data Processing Units (DPUs) to accelerate data movement. By enhancing efficiency in DPU-powered systems, OpenDOTA aims to advance scientific simulations, drive AI advancements, and strengthen computational research infrastructure. The project fosters collaboration and contributes to the evolution of state-of-the-art data movement technologies, benefiting a wide range of users in academia and industry. This project focuses on designing OpenDOTA as a high-performance, scalable framework for DPU-offloaded data movement in HPC and AI cyberinfrastructures. The research is structured around three key thrusts: (1) Adaptive point-to-point data movement, which employs diverse offloading strategies to optimize data transfer over DPUs; (2) Accelerating collective communication by leveraging advanced DPU offloading techniques to improve scalability; and (3) Deep reinforcement learning (DRL)-based optimization, which dynamically adapts data movement strategies for maximum performance. By integrating these approaches, OpenDOTA offers a comprehensive solution to existing data movement challenges, paving the way for scalable, high-performance applications across HPC and AI domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Graphics Processing Units (GPUs) are the go-to choice for deep learning due to their exceptional computational power and massive parallelism. However, maximizing GPU performance for model development and inference remains notoriously challenging as models grow increasingly complex, spanning multiple abstraction layers: the upstream Python layer, the midstream C/C++ layer, and the downstream GPU kernel layer. While this layered complexity meets diverse application needs, it also embeds inefficiencies that are difficult to detect due to intricate cross-layer interactions. The project addresses these inefficiencies through a comprehensive, cross-layer performance analysis of deep learning models. The project’s novelties are advancing state-of-the-art profiling techniques to enable systemic performance tuning across all layers. The project's broader significance and importance are deepening the understanding of systemic performance issues in deep learning, thus strengthening foundations in code analysis and advancing progress in fields increasingly reliant on deep learning, such as image processing. With interest from industry leaders like Meta, the project shows strong potential for translating academic insights into practical applications. Additionally, the project contributes to educational and outreach goals by integrating its findings into computer science curricula and K-12 programs to cultivate a workforce skilled in performance analysis and optimization. Three innovative analysis techniques structure the project. (1) Unified binary code analysis: It consolidates all layers of deep learning models into a shared binary abstraction, enabling the identification of cross-layer inefficiencies in code segments and data objects. (2) Incremental analysis: It incrementally narrows the scope of monitored performance metrics to pinpoint the root causes of inefficient code segments identified in the unified binary analysis. (3) Data object analysis: It addresses inefficient data objects identified in the unified binary analysis to diagnose their root causes. Together, these techniques form a comprehensive approach to performance tuning, addressing inefficiencies from a systemic perspective and maximizing GPU capabilities in deep learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Deep Learning (DL) has improved scientific applications across various scientific domains, including high-energy physics, meteorology, agriculture, and material science. This project introduces DLToolkit, a performance profiling infrastructure tailored for domain scientists to analyze and optimize science-driven DL applications. This project also contributes to education and supports broader usage; the outcomes of this project will be integrated into the Computer Science (CS) curriculum, and both George Mason University and the University of California - Merced are minority-serving institutions, offering opportunities for delivering knowledge about cutting-edge techniques to underrepresented students. Together with industry and national laboratory partners, the project will also provide research training, symposia, and internship opportunities for students, aiming to foster a cohort of performance engineers. The overarching objective of this project is to improve scientific DL applications. The intellectual merits include three novel profiling capabilities: (a) synergistic tool-framework profiling to streamline extensive domain-specific knowledge from existing DL frameworks to DLToolkit, significantly lowering the barrier for domain scientists to use DLToolkit; (b) just-in-time (JIT)-aware profiling to ensure precise yet lightweight attribution of performance events to complex JIT-compiled DL operators; and (c) tensor-centric profiling to provide a holistic view of tensor operations’ impact on model performance. By uniting these capabilities within DLToolkit, this project will create a cohesive infrastructure for domain-specific performance profiling to empower scientists with critical insights to optimize their DL applications, accelerating scientific research and innovation. 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 2025 · 2025-06

This project advances artificial intelligence (AI) by developing more reliable and trustworthy question-answering systems that combine large language models with structured knowledge graphs. Current AI systems, while powerful in generating human-like responses, often produce answers that are plausible but factually incorrect, which can have severe consequences in critical domains like healthcare and legal services. By integrating the strengths of large language models with the structured, verifiable information from knowledge graphs, this research creates AI systems that people can trust for high-stakes decisions. The project develops novel computational methods that ensure AI outputs are grounded in verified knowledge, reducing errors and improving reliability. The research outcomes will benefit society by enabling more dependable AI assistants for healthcare diagnosis support, legal consultation, and other domains where accuracy is paramount. The project also contributes to computer science education through new courses on knowledge graphs and advanced language models at the University of California, Merced, helping prepare the next generation of AI researchers and practitioners. Open-source software tools and datasets developed through this research will be made freely available to the broader research community, accelerating progress in trustworthy AI development. The project develops innovative approaches for combining large language models with knowledge graphs through four key technical advances. First, it creates new methods for synergistic knowledge representation that effectively capture information from both unstructured text and structured knowledge sources. Second, it designs AI agents that automatically explore knowledge paths to augment language model inputs with relevant verified information. Third, it develops constrained decoding techniques that ensure language model outputs remain consistent with knowledge graph facts. Fourth, it enables bi-directional reasoning between language models and knowledge graphs through carefully designed learning algorithms. The research methodology includes rigorous evaluation on diverse benchmarks across general domain question answering, healthcare services, and legal consultation. The project employs an efficient knowledge path exploration algorithm using intelligent pruning, which significantly reduces computational overhead while preserving model performance. The developed methods will be made available through comprehensive open-source implementations, accompanied by detailed documentation of computational requirements and efficiency metrics to facilitate adoption by other researchers and practitioners. 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 2025 · 2025-06

The goals of the CREST Postdoctoral Research Program (PRP) are to advance STEM knowledge, improve the nation's research competitiveness, and strengthen the preparation of the STEM workforce. This project aligns with those goals through the support of an early career scholar in the study of one of the most common pathogens affecting human health. Microbes rarely live in isolation; instead, they typically interact with one another to form complex microbial communities that can profoundly influence both natural and industrial ecosystems. One common and highly effective way these microorganisms cooperate is through the formation of biofilms, structured, multicellular communities that offer protection and enhance the community’s ability to survive environmental stresses and function collectively. In recent work, it was observed that the fungus Candida albicans is capable of interacting with strict anaerobic bacterial species, such as Clostridium perfringens, to form free-floating microbial aggregates called “mini- biofilms.” Remarkably, these mini-biofilms form under aerobic conditions, and provide a protective niche for the anaerobic bacteria. With support from the CREST-PRP, this project will study these processes to gain a mechanistic understanding of how mini-biofilms are formed and regulated to provide valuable insight into the ways microbial communities coordinate, adapt, and persist in diverse environments. This information could inform new strategies for controlling microbial populations in environmental, industrial, and clinical contexts, thus contributing to national security and global competitiveness. The award will provide support for a postdoctoral scholar who will perform the research and engage in professional development experiences to strengthen the PI's preparation as a scientist, thereby addressing agency priorities to develop the STEM workforce. Biological phenomena such as biofilm formation are often controlled by transcriptional networks, where transcription factors (TFs) regulate subsets of genes involved in complex biological processes. To uncover the transcriptional network governing mini-biofilm formation, the genome-wide binding profiles of 52 TFs that are identified to be putatively involved in mini-biofilm formation will be mapped. Integrating this binding data with existing transcriptional profiling data will reveal the complete transcriptional network governing mini-biofilm formation. Next, the top 30 downstream target genes of the core TFs will be studied to understand which genes play key functional roles in mini-biofilm formation. 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.

NIH Research Projects · FY 2025 · 2025-06

PROJECT SUMMARY/ABSTRACT Pattern recognition receptors are membrane and cytosolic proteins that alert the organism’s defense mechanisms by detecting pathogen-associated and danger-associated molecular patterns. The recognition of pathogenic DNA from bacteria and viruses by DNA sensors triggers an inflammatory response indicating the danger of infection. Absent in melanoma 2 (AIM2) and interferon-inducible protein 16 (IFI16) are intracellular pattern recognition receptors that detect pathogenic DNA. Upon DNA binding, AIM2 and IFI16 oligomerize and recruit other proteins leading to inflammasome assembly. Inflammasomes are multiprotein complexes that trigger inflammation by activating cytokines in response to danger signals from pathogens or cell dysfunction. Although inflammation is a fundamental defense mechanism, its dysregulation leads to the onset of several diseases, including cancer, diabetes, and cardiovascular disorders. The functions of AIM2 and IFI16 and the signaling pathways they elicit are implicated in autoimmune disorders and drug resistance of certain types of cancer. Thus, AIM2 and IFI16 are desired targets in anti-inflammatory and anticancer therapies. AIM2 and IFI16 belong to the PYHIN family of multidomain proteins bearing HIN domains for DNA binding and PYD domains for oligomerization. Evidence suggests that the HIN domains participate in DNA recognition to different extents by contributing to receptor oligomerization at various levels and engaging in heterotypic HIN-HIN interactions that could be involved in receptor inhibition. Our laboratory has reported key mechanistic information on the assembly of AIM2 on DNA at the single-molecule level using optical traps combined with confocal fluorescence microscopy. We showed that the binding of AIM2 to DNA promotes AIM2 self-association into oligomers of different sizes. Our real-time data on AIM2-DNA binding allowed us to establish that single AIM2 molecules and oligomers do not significantly diffuse along the DNA. We are extending these studies to IFI16 HIN domains, revealing they also oligomerize on DNA. In contrast to AIM2, IFI16 HIN domains demonstrate significant mobility. We have corroborated these results with biochemical methods showing that IFI16-HIN oligomerization on double- and single-stranded DNA depends on DNA length. In this project, we will demonstrate that individual HIN domains play different roles in molecular pattern recognition based on their different diffusivity and oligomerization on DNA and their capability to engage in heterotypic HIN-HIN interactions. We will use optical traps and confocal fluorescence microscopy to establish relationships between mobility, oligomerization, and HIN-DNA complex survival time. In addition, we will determine the stoichiometry of HIN-DNA complexes as a function of DNA length and identify heterotypic HIN-HIN interactions by biochemical methods. The successful outcome of this project will define the impact of oligomerization and binding dynamics on the survival of HIN- DNA complexes, which is key for subsequent signaling steps of innate immunity and inflammatory processes.

NIH Research Projects · FY 2026 · 2025-05

Project Summary/Abstract Studies of microorganisms have largely been carried out in free-floating (planktonic) cultures; however, the medical, industrial, and environmental impacts of most microorganisms depend on their abilities to form resilient surface-associated microbial communities called biofilms. Biofilms are the predominant growth state of microorganisms on biotic and abiotic surfaces. Biofilms are notorious for forming on any surface that is routinely exposed to moisture, including mucosal surfaces, and implanted medical devices, such as catheters, pacemakers, contact lenses, and prosthetic joints, which all provide a surface and sanctuary for biofilm growth. The human health consequences of biofilm infections are thus significant and can be life-threatening. The focus of my laboratory’s research program is to understand the molecular and mechanistic bases of biofilm microbial communities. We are interested in investigating how transcriptional networks underlie the regulation of gene expression during biofilm development. Our overarching goals are to understand how these communities are regulated, how they are built, how their specialized properties are elaborated and maintained, and how these types of behaviors have evolved. We take systems biology approaches to investigate these fundamental biological questions and explore biofilms at both the single-species as well as multi-species levels. The types of microorganisms we study include fungi, bacteria, and archaea, with a focus on those that are commensals and/or pathogens of humans. Much of our work to date has focused on single-species biofilms formed by the fungal pathogen Candida albicans as well as dual-species biofilms formed between C. albicans and interacting bacterial/archaeal partners that are predominant members of the human microbiota. Our work will provide new insights into ways of detecting and treating biofilms in medical and industrial settings and, perhaps most importantly, in preventing them from forming in the first place. Specifically, in terms of human health, this work has the potential to lead to new prevention, diagnostic, and therapeutic strategies to combat biofilm infections. In the long-term, the information gained from these studies could lead to significant changes in the ways we treat infections and dysbiosis of the microbiota. More broadly, these studies could also shed new light on the evolution of multicellularity in eukaryotes by increasing our understanding of how social behaviors evolve in single-celled microorganisms.

NSF Awards · FY 2025 · 2025-04

This I-Corps project is based on the translation of engineered proteins that act like tiny sensors, lighting up when they detect specific changes inside cells. These proteins can be customized to track important biological signals, such as pH levels, harmful pollutants, or disease-related molecules. Currently, industries like healthcare, waste management, and food production rely on slower methods to monitor biological changes and these slower technologies leads to delayed disease detection, inefficient processing of waste, and missed opportunities to improve product safety. By translating this research from discovery to real-world application, this project may benefit society and the U.S. economy by creating more high-quality jobs in biotechnology and supporting healthier communities nationwide. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a genetically encodable biosensor. This biosensor uses a conformational rheostat design with fluorescent signals to create proteins that act like tiny molecular switches that light up or change color when they detect molecular changes. This solution consists of proteins engineered to change shape and glow when they detect changes, using a flexible system that measures tiny signals at the single molecule level. This novel technology is in contrast to existing sensors that only give simple on-or-off results. The engineered proteins respond faster than current testing methods, which often require expensive equipment and take hours or even days to produce results. The benefits of this approach include instant, precise data to track molecular changes in medical tests, industrial processes, or environmental systems, improving efficiency and outcomes while offering a customizable tool that adapts to different biosensing needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The 30th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), sponsored by the Association for Computing Machinery (ACM), will be held in Rotterdam, The Netherlands, March 30 - April 3, 2025. ASPLOS is a leading forum for multidisciplinary systems research spanning computer architecture and hardware, programming languages and compilers, and operating systems. It involves participants (researchers, developers, students, and practitioners) from academia, industry, laboratories, and commerce coming together to discuss recent advances and trends. ASPLOS seeks to increase student participation in the conference and the field. The funding will support the travel of eligible US students. Recipients will be able to attend the main conference, workshops, and tutorials. Travel grants will encourage the research interests and the involvement of students in the field who are not well funded and those who are just beginning their participation in the field or are interested in entering it. A special effort will be made to reach out to students from underrepresented groups. The award will cover up to 15 NSF-sponsored student travel grants for a total budget of $25,000. 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.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY Neurodevelopmental disorders (NDDs), especially autism spectrum disorders (ASD), are alarmingly prevalent among our children. Recent genome-wide studies suggest a strong correlation between ASD and loss-of-function mutations in genes encoding subunits of a particular chromatin remodeling complex, called the Brahma Associated Factor (BAF; alias, mSWI/SNF). However, despite such strong correlation, the precise role(s) of the neuronal BAF (nBAF) complex in brain development-related gene transcription remains largely unclear. In response to the NIH funding opportunity titled, ‘Cellular and Molecular Biology of Complex Brain Disorders’ (PAR-24-025), this project is designed to elucidate neurodevelopmental functions of SMARCC2 (fundamental biology), a high confidence ASD candidate gene product of the BAF complex. In working with SMARCC2, our project will organically extend to other subunits of the BAF complex, many of which are also implicated in NDDs. SMARCC2 and several other members of the BAF complex feature unstructured Intrinsically Disordered Regions (IDR), which will be the focus of this study. Our central hypothesis is: ‘In maturing neurons, the nBAF complex drives RNA Pol2 productive elongation (molecular role), and thereby synapse maturation (cellular role) via IDR-mediated multivalent interactions’. This hypothesis will be tested via two specific aims: 1) determine if nBAF and its core subunit SMARCC2 mediates RNA Pol2 elongation via IDR-dependent multivalent interactions and liquid-liquid phase separation (LLPS), and 2) determine if the nBAF complex, via transcription of synapse-related genes, regulates synapse maturation. We will use pharmacological nBAF complex inhibitors and degraders, RNAi of key BAF subunits, and genetically encoded expression of mutated BAF subunits to perform studies both in vitro and in vivo. Taken together, this study will generate valuable insights into nBAF complex functions and epigenetic mechanisms driving normal and altered ASD-related neurodevelopment, which will fill in the knowledge gap between BAF mutations and their outcomes, such as ASD. Our findings will also have broader implications in other fields, such as cancer, where BAF subunit mutations are quite common.

NSF Awards · FY 2024 · 2024-12

The broader impact/commercial potential of this I-Corps project is based on the development of a wearable muscle-sensing solution to directly measure fascial thickening when muscles are strained over long periods of time. This project prioritizes affordability, wearability, and in-home continuous monitoring, making this novel technology widely accessible to the general public. The data collected through this platform solution can be shared with physical therapists and ergonomists to give them a holistic view of patient muscle data, thus helping them better serve patients. Muscles are highly individual, and this solution could ensure that data collection thoroughly includes underrepresented groups to mitigate bias. Furthermore, the affordability of the proposed solution will make muscle health solutions widely available to people often subject to healthcare inequities. By helping users take the initiative to prevent chronic musculoskeletal pain, this solution could save users the $32B spent annually on pain management procedures and medications. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of the proposed technology. It is based on the prior development of an artificial intelligence (AI) powered wearable sensor system that utilizes haptic feedback to directly measure fascial thickening when muscles are strained over long periods of time. Current muscle stiffness sensor solutions such as electromyography, shear wave elastography, and magnetic resonance elastography are either largely focused on research applications or are bulky and expensive, which limits their usage in clinicians’ offices. A preliminary model of the device and AI algorithm have demonstrated the feasibility of this approach, and further research is being conducted to generalize the algorithm across different physiologies and develop form factors that optimize comfort. If successful, this solution could be used to build a more comprehensive understanding of the properties of the fascia and the relationship between fascial thickening and musculoskeletal chronic pain. 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.

NIH Research Projects · FY 2026 · 2024-11

PROJECT SUMMARY Toxoplasma gondii is a widespread intracellular parasite of warm-blooded animals and causative agent of human toxoplasmosis. Mus musculus is an important intermediate host that is possibly entangled in an evolutionary arms race with this apicomplexan parasite. Our current work using forward genetics to understand mechanisms of resistance to T. gondii suggests that susceptibility to primary infection is genetically determined. In this grant submission, we propose to leverage a high diversity recombinant inbred mouse panel, the Collaborative Cross (CC), as well as parasite genetics to dissect mechanisms that determine disease outcome following primary infection. Our preliminary data predicts that novel alleles segregating within the CC panel will determine host susceptibility to previously described `less virulent' strains of T. gondii. However, virulence is relative and determined by the combined genetics of parasite and host, but which genes and host responses drive disease severity are not fully understood. We hypothesize that novel host-parasite interactions govern disease outcome following Toxoplasma gondii infection in Mus musculus castaneus and musculus murine subspecies. Approaches from genetics, molecular parasitology and immunology will be utilized to evaluate these hypotheses. In Aim 1, we propose to use the entire CC panel to genetically map and identify host susceptibility genes to parasite infection with a `low virulent' strain of T. gondii. In Aim 2, we will leverage our unpublished data to interrogate a parasite locus associated with host morbidity following primary infection in non-laboratory mice. Whether a genetic conflict occurs between host and parasite genes identified in this proposal will be examined. With the overarching goal of preventing human toxoplasmosis, insights gained from this proposal will guide future research on host-parasite relationships and the genetic basis of parasitic disease.

NSF Awards · FY 2024 · 2024-10

An award is made to University of California (UC), Merced to acquire a timsTOF Pro 2 Mass Spectrometer coupled to a liquid chromatography system to enable biomolecular research into host-microbe interactions. UC Merced is a Hispanic-Serving Institution by design located in the heart of California’s San Joaquin Valley (SJV). The student population reflects that of the local community where students are primarily Hispanic or from other underrepresented groups (URM) in STEM and are largely first-generation undergraduates (>75%). As such, this instrument enhances the training and education of UC Merced students by granting them access to state-of-the-art technology that enables the analysis of complex biological samples. To facilitate the training mission, the instrument and collected data is used in undergraduate and graduate classes. The instrument is also available to researchers at other institutions located across the SJV as it represents the only high-resolution mass spectrometry (HRMS) of its type in the region. The instrument and supported projects will enhance local workshops and research exchanges between partner institutions to train users in data acquisition and analysis. HRMS instruments, like the timsTOF Pro 2, are key pieces of research equipment that allow for investigators to characterize and explore the spatiotemporal dynamics of both small molecules and proteins. As such, the timsTOF Pro 2 enhances the capacity of researchers to advance current understanding of biological processes by leveraging metabolomic and proteomic approaches. For example, research using this instrument is advancing current work investigating how microbes interact with their hosts in future climates, developing biomarkers for disease, and helping inform local restoration efforts across marine and terrestrial ecosystems. The instrument represents a significant advancement in mass spectrometry analysis (resolution and speed) that will enable the separation, detection, and identification of proteins and metabolites quantitatively and qualitatively across host-microbe systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to serve the national interest by integrating the study of software performance throughout the CS curriculum. There is a growing recognition of the importance of software performance, however, learning how to code efficiently remains a critical missing piece in CS curricula, as for decades the primary focus has been on achieving functional correctness (and this trend continues). This project intends to develop EduPerf, an education-centric software platform, to integrate software performance analysis into different levels of CS courses to help students and instructors. The project has the potential to cultivate a highly skilled cohort of software engineers adept in performance analysis and proficient in writing efficient code. The project team plans to develop interventions and novel measurement-based techniques to enable instructors to assess student learning effectively. The team plans to use formative and summative assessment methods to measure the student learning outcomes and answer several research questions. The project results will be disseminated at several major CS education conferences. The team will provide detailed user and developer guides to EduPerf in PDF, HTML, video, and other comparable presentation formats. The project outcomes will not only benefit the technology industry in Silicon Valley in California, the automotive industry in Michigan, and Research Triangle Park in North Carolina but also advance the nationwide scientific endeavor. 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 project is also supported by the NSF IUSE: HSI program, which has the goals of enhancing the quality of undergraduate STEM education, and increasing the recruitment, retention, and graduation rates of students pursuing associate’s or baccalaureate degrees in STEM. 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 · 2024-10

Solid materials inevitably have defects such as missing atoms and impurities of a different kind of atom. These defects can have discrete energy levels like an individual atom, providing quantum states which can be used for applications in quantum computing, quantum sensing, and quantum communications. This project uses computational theory to understand the fundamental properties of such defects and identify promising candidates to be studied in the laboratory and considered for future technologies. The research team is developing a new methodology and applying it to a novel class of defects to assess their suitability. The methods developed are being implemented in open-source software, available for the research community at large, and made accessible via tutorials and workshops. The postdoctoral researchers and students are being trained in best practices for code development. The research benefits society and economic development by building the fundamental scientific knowledge for the advancement of quantum technologies. This project brings together faculty members from the University of California, Merced (UC Merced), Florida Polytechnic University, and the University of California, Santa Barbara (UC Santa Barbara) which has a well-established research program including the NSF-funded Quantum Foundry. The research team is adapting course modules from UC Santa Barbara on Materials for Quantum Information Science for classes at UC Merced and Florida Polytechnic and developing new hands-on web-based simulation tools and exercises about defects. Undergraduates at UC Merced and Florida Polytechnic will take part in the research. UC Merced students will join Quantum Foundry professional development activities, including student-led seminars and engagement with industry through industry internships and the annual Quantum Industry Showcase. An additional element is public outreach with the California Minerals and Mining Museum about how defects create colors in gemstones. The continued development of quantum information science depends on identifying suitable qubits, single-photon emitters, and quantum memories. Point defects or impurities in semiconductors and insulators form an attractive platform for implementing these technologies. Theory can play an important role in identifying suitable new candidates for experimental investigation. However, accurate calculations of defect properties are quite challenging for standard electronic-structure methods. The research team’s recently developed spin-flip Bethe-Salpeter equation approach to this problem has shown success on small molecules and on the NV− and SiV0 centers in diamond. This project is further developing the spin-flip Bethe-Salpeter equation method, to make it more accurate and flexible. The method is then being applied to transition-metal impurities in wide-band-gap semiconductors or insulators, which are a primary target of interest in the field, in order to calculate optical spectra and radiative and non-radiative recombination. The research team brings together expertise specifically in the spin-flip Bethe-Salpeter method from junior investigators at UC Merced and Florida Polytechnic, with expertise in first-principles calculations of defects and recombination processes from a more senior investigator at UC Santa Barbara, to develop research and education in quantum information science at these two emerging institutions. This work advances the spin-flip Bethe-Salpeter methodology and code, providing a new tool for the quantum information science research community to use in first-principles studies of point defects, which overcomes the limitations of some existing methods. The research expands knowledge of particular defects in wide-gap semiconductors, helping in the search for new candidates for quantum technologies. This award is co-funded by the Advancing Informal STEM Learning program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The University of California, Merced (UC Merced), a minority-majority campus, has rapidly become a productive research university with a promising future. UC Merced educates a student body, with 58% Hispanic students, 74% first-generation college students, and 63% Pell grant recipients. The University of Washington (UW) is a renowned public research-intensive university with extensive infrastructure for nanofabrication, characterization, and hybrid research/training labs. Through the Venture for Innovation in Self-assembly and Integration of Optoelectronic Nanostructures (VISION) Partnership for Research and Education in Materials (PREM) between UC Merced and the UW-led NSF Center for Integration of Modern Optoelectronic Materials on Demand (IMOD), an NSF Science and Technology Center (STC) involving 12 universities, UC Merced students gain access to facilities, expertise, and mentorship nationwide. VISION aims to cultivate student potential in STEM through enriched research opportunities, scientific collaboration, pedagogical development, and mentoring; it seeks to broaden nano- and quantum materials literacy at all educational levels; and in its seed phase, initiate and develop opportunities for K-14, undergraduate, and graduate students. VISION strives to create broader educational pathways for UC Merced’s student body and workforce development and engagement opportunities for IMOD institutions, via reciprocal visits and regular interactions. Additionally, VISION intends to design educational and outreach activities to inspire K-14 students and equip their teachers to incorporate modern materials science and engineering into their curricula. VISION aims to create a synergistic environment for research, education, and professional development, guided by best practices for workforce development and transitioning students into successful careers. By leveraging complementary expertise and infrastructure between IMOD and UC Merced, this PREM Seed project seeks to advance the understanding of material science and engineering approaches to translate established quantum opto-electronic principles into semiconductor nano-architectures with wide operational range. To this end, in its seed phase VISION focuses on precisely organized solid state quantum emitters, such as colloidal quantum dots arranged into one-, two-, and three-dimensional assemblies via DNA templating. To validate functionality and robustness of quantum properties, these structures will be tailored and assessed for quantum light generation and light manipulation. The knowledge gained with such nanostructures that cross materials classes will contribute to the development of economically feasible quantum materials-based devices with the potential for integration with, or applications in monitoring biomedical, life and environmental systems. In regard to education and professional development activities, VISION will 1) promote culturally responsive mentoring to enhance retention and degree completion among students 2) develop curriculum that integrates research and pedagogy, producing authentic contextualized materials for K-14; and 3) develop a bachelor’s/master’s pathway in quantum materials, leveraging IMOD-developed quantum course materials and IMOD expertise in experiential learning methodologies known to workforce development in PhD programs, and boosting undergraduate retention. Research and educational findings will be jointly disseminated to the public. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Machine learning (ML) augmented algorithms have emerged as a powerful framework that incorporates machine-learned predictions into algorithm design. This framework has demonstrated that traditional algorithms can be significantly enhanced using machine learning while retaining some worst-case performance guarantees. This project aims to study ML-augmented algorithms that only have access to weak and sparse predictions. Such scenarios can arise when obtaining abundant and accurate data for training is challenging, or when running heavy predictors that can otherwise incur a non-trivial overhead in time-critical or resource-constrained systems. By understanding the power and limitations of using weak and sparse predictions, the project seeks to make ML augmented algorithms more applicable. Additionally, the project will prepare the students to tackle challenges in this rapidly expanding field, particularly by reaching out to and supporting community college students in their educational journey toward four-year colleges and beyond. This project also aims to further unlock the potential of machine learning-augmented algorithms by exploring their capabilities when operating on sparse or weak predictions. Sparse predictions, which may occur when training data is limited, arise when the machine learning model produces few predictions for certain inputs. Weak predictions arise, for example, when predictions are given as ranges of values rather than specific values. Both scenarios pose unique challenges for algorithm design. This project will investigate the design considerations for machine learning-augmented algorithms in these contexts, seeking to understand the tradeoff between prediction quality and resulting performance guarantees, ultimately advancing the understanding and applicability of machine learning-augmented algorithms in real-world scenarios. 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.

NIH Research Projects · FY 2025 · 2024-09

Project Summary / Abstract A single acute ethanol exposure causes lasting changes in behavior and brain function. Acute changes may form a basis for progressive changes with subsequent ethanol exposures, incrementally increasing the risk of alcohol use disorder. Studies from humans to Drosophila have uncovered strong behavioral and neural links between the development of ethanol tolerance, an early form of ethanol-induced plasticity, and changes in sleep. We recently discovered in Drosophila that a small group of circadian clock neurons that regulate sleep also encode rapid ethanol tolerance, the form of tolerance that is closely associated with proximal changes in sleep. Whereas prior work in Drosophila implicated clock pacemaker genes and a potential relationship between sleep and rapid tolerance, our new data provides an anatomical locus and a focus on the specific aspects of the tolerance/sleep interaction. The long-term goal is to determine how ethanol causes changes in sleep. The expected long-term impact is a better understanding of the relation between addiction and sleep. Because rapid ethanol tolerance appears to map to some but not all sleep regulatory neurons, we hypothesize that ethanol may ‘misuse’ sleep circuitry. Circadian rhythms and sleep are extensively studied in Drosophila. While our understanding of rapid ethanol tolerance is less complete, it is easy to study and intriguing underlying mechanisms are beginning to emerge. The density of data on sleep mechanisms in Drosophila and mammals creates an opportunity to better understand tolerance and its relation to longer term effects of ethanol. The plan for this proposal is to determine the extent of co-encoding of rapid ethanol tolerance and sleep in Drosophila. To do this, we propose three experiments. First, we plan to map rapid ethanol tolerance in sleep circuitry. Second, we discovered that rapid ethanol tolerance is composed of a labile and a consolidated memory-like states. We plan to map these and also evolutionarily conserved tolerance genes in the sleep circuitry. Third, We plan to survey the function of the approximately 50 known sleep genes in rapid ethanol tolerance. The majority of these genes have not yet been tested for their role in tolerance. The expected outcome of the proposed research is a genetic and circuit map of rapid tolerance with respect to sleep.

NSF Awards · FY 2024 · 2024-09

Faculty of color are vastly underrepresented in senior STEM faculty positions, raising questions about equity in faculty advancement processes. The project team’s prior work (NSF #1409928 and #2100034), through a consortium study of ten universities, has found that for underrepresented minority (URM) faculty, promotion and tenure (P&T) processes function as a de facto gatekeeping mechanism preventing the equitable career progression of URM faculty. Specifically, URM faculty receive, on average, 7% more negative votes and are 44% less likely to receive a unanimous vote at the college-level, even when accounting for institution, discipline, scholarly output, and more. This grant aims to examine how, when, and why promotion and tenure decision-making processes disadvantage URM faculty and to create interventions to address systemic inequities that currently impact promotion and tenure decision-making for URM faculty. The current project intends to work, through a diverse PI team and through partnerships with URM faculty, to identify mechanisms driving systemic biases in academia. Ultimately this project, grounded in quantitative social science research, plans to 1) identify the mechanisms of bias in current decision-making processes within P&T committees that result in racial inequity, and 2) develop evidence-based interventions and policy recommendations that ultimately form resources for advancing racial equity in promotion and tenure decision making. Using a consortium approach - led by three Hispanic-serving institutions (HSIs, also designated as Asian American and Native American Pacific Islander-serving institutions [AANAPISI]) and one historically Black college or university (HBCU) - the Center for Equity in Faculty Advancement (CEFA) aims to address systemic bias and racial inequity in the P&T process. The intention is to accomplish aims through two phases. First, CEFA plans to determine the theoretical mechanisms leading to the higher likelihood of negative P&T outcomes for URM faculty in STEM disciplines, particularly URM women. Within this phase, the team intends to collect P&T data from five institutions, including voting outcomes, external review letter (ERL) data, and demographic make-up of P&T committees. Supplementing the P&T data, the team plans to distill other metrics related to scholarly output (e.g., citation and paper counts) from online repositories. Research questions include examining the role of diversity of P&T committees and departments, choice architecture, informal/formal P&T policies, P&T portfolios, COVID tenure clock extensions, and ERLs. The design is for the second phase to be guided by (a) insights from the first phase on the mechanisms perpetuating structural biases and (b) authentic partnerships - including collaborative workshops - with URM faculty within the consortium universities and national networks of URM faculty. Specifically, phase two of the project aims to develop evidence-based interventions, including trainings and policy recommendations for each of the four key groups in the P&T process: 1) P&T Candidates, 2) P&T Committee Members, 3) External Review Letter Writers, and 4) University Policy Makers. Nationally available toolkits for targeting the full breadth of P&T stakeholders to reduce and eliminate structural biases and advance equity in P&T processes are anticipated project outputs. This collaborative project is funded by the EDU Racial Equity in STEM Education activity, which is supported by the Directorate for STEM Education (EDU). This activity supports research and practice projects that investigate how considerations of racial equity factor into the improvement of science, technology, engineering, and mathematics (STEM) education and workforce. Awarded projects seek to center the voices, knowledge, and experiences of the individuals, communities, and institutions most impacted by systemic inequities within the STEM enterprise. Programs across EDU contribute funds to the Racial Equity activity in recognition of the alignment of its projects with the collective research and development thrusts of the four divisions of the directorate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The Society for Industrial and Applied Mathematics (SIAM) recently recognized the establishment of the Northern and Central California (SIAM-NCC) Section, whose primary goal is to provide an ongoing opportunity for mathematicians working in the sectors of academia, national laboratory, industry, and government to come together and form a strong social and professional network. The first SIAM-NCC conference scheduled to be held at the University of California, Merced campus during October 9-11, 2024 has the following aims: (1) create an opportunity for scientific researchers in the central and northern California regions to meet, network, and share the innovations and recent developments in their fields; (2) attract and energize a diverse group of students and researchers particularly those from underrepresented minority groups; (3) offer opportunities to SIAM members from various institutions in the region to present their work, who for various reasons often struggle to participate at national and international SIAM meetings; and (4) provide early career researchers to connect with others who are at similar career stages. The broader goal of this conference is to bring together a diverse group of students and researchers particularly those from underrepresented minority groups and create opportunities for sharing ideas and networking. The central and northern California regions provide rich opportunities for involving students from underrepresented and financially challenged populations majoring in science, technology, engineering, and mathematics (STEM) fields. The 2024 SIAM-NCC Conference is centered around the following five research themes of applied mathematics: (1) mathematical and numerical analysis; (2) optimization, inverse problems, and optimal experimental design; (3) scientific and high-performance computing; (4) uncertainty quantification and prediction; and (5) scientific machine learning (ML), artificial intelligence (AI), and digital twins. The conference will feature four plenary speakers from industry, academia, and national laboratory. Ten mini-symposia are planned to capture the conference themes in critical areas of research in applied mathematics. Four panels will cover a variety of topics aimed to reach undergraduate and graduate students, early career researchers, and the greater scientific community. In particular, topics include (1) career opportunities for undergraduate students, (2) transitioning from student to researcher (e.g., preparing for internships and postdoc positions), (3) industry and laboratory careers, and (4) the role of AI/ML in science and technology. Finally, to facilitate a more open and informal discussion about research and career opportunities, to accommodate broader research themes, and to offer opportunity for all attendees to present their work, two poster sessions are also scheduled. Undergraduate and graduate students, as well as postdoctoral scholars and other early career researchers, will be particularly encouraged to participate in these sessions. The conference website is: https://sites.google.com/view/siam-ncc/siam-ncc-conference-2024. 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.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Hematopoietic cell transplantation (HCT) remains an important treatment for hematologic malignancies such as multiple myeloma, lymphoma, acute leukemias, and myelodysplastic syndromes.1 A key component of HCT is the cytotoxic preconditioning required to reduce the burden of malignancy, suppress the host immune system, and enable engraftment and tolerance of the donor hematopoietic cells.2 Unfortunately, conditioning regimens invariably damage the tissues where donor cells engraft and expand (e.g., bone marrow and thymus), leading to significant morbidity and mortality.1 Recent data suggests that radio-resistant thymus vascular endothelial cells (ECs) are critical cells in endogenous thymus regeneration as they provide the conduit for progenitor cell entry into the thymus, express ligands important for early thymic seeding, and secrete regenerative associated factors (RAFs) such as BMP4.3–5 Understanding how the vascular network, and in particular ECs, respond to cytotoxic therapy may give us insights that will enable the development of improved therapies and restoration of adaptive immunity after acute injury.4,5 But how do these regimens change the microenvironment of these tissues (especially the thymus) and impact the recovery of the hematopoietic and adaptive immune systems? To address this question, we need new methods to directly analyze the live thymus in its native site.6 Our previous intravital imaging work demonstrated that cytotoxic therapies drastically change the bone marrow (BM) vascular integrity, oxygen tension (pO2), and cell behavior.7,8 In the thymus, we also observe widespread changes in vascular structure suggesting functional damage. We hypothesize (a) that the native cortical and CMJ vascular compartment is heterogenous with functionally distinct blood vessels; (b) that these distinct vessels are made up of specific sub-types of ECs; (c) that this heterogeneity diminishes as the thymus matures and/or after cytotoxic conditioning; and (d) that cytotoxic conditioning causes abnormal hemodynamics in the blood vascular system leading to spatially and temporally restricted changes in blood flow, vessel permeability, and tissue oxygenation. To test these hypotheses, we propose the following aims: (1) Utilize novel high-resolution intravital imaging and two-photon oxygen microscopy to directly image and functionally characterize the native thymic cortical and CMJ blood vascular physiology; (2) Define EC heterogeneity and subtypes using flow cytometry, single cell RNA sequencing, and immunostaining and identify age dependent functional and anatomical alterations in EC and vascular heterogeneity; and 3) Utilize high-resolution imaging and two-photon phosphorescence oxygen microscopy to characterize the thymus microenvironment over time after myeloablative (MAC) and reduced intensity conditioning (RIC) to identify physiological changes impacting the recruitment of progenitors to the thymus in the context of HCT. The studies outlined in this proposal have the potential to elucidate the microenvironmental effects of cytotoxic preconditioning on the thymus in ways that have not been previously possible through use of novel imaging techniques for direct thymus evaluation.

NSF Awards · FY 2024 · 2024-08

This award supports the Neotoma Paleoecology Database. Neotoma is one of the most widely used and trusted international data resources for fossil data, growing rapidly in the volume and variety of its data holdings, functionality of its software services, and the size and scope of its user community. This award will allow Neotoma to grow and enhance systems to support higher rates of data additions, more streamlined data curation, and better support solutions for new communities seeking to use Neotoma data. This project provides access to publicly funded data and supports researchers, educators, and the public by providing a high-quality, expert-curated open data resource for paleoecological and paleoenvironmental data. Specific activities for this project include better support for rapid upload of hundreds to thousands of datasets from participating research teams through enhancements to the Data Bulk Uploader System (DataBUS), with newly added ORCID user authentication and support for the popular Linked Paleodata (LiPD) format. Embargo Manager will support early data contributions and better data management practice, in alignment with NSF Division of Earth Sciences (EAR) Data and Sample Policy. The Hierarchical Vocabulary and Taxonomy Manager (HVTM) will improve data quality and interoperability by enabling efficient viewing and curation of controlled vocabularies. Neotoma will freely upload supported data types, with priority for NSF-EAR PI data, and will help on-board major geoscience paleodata communities. Neotoma PIs will develop and provide multiple training support activities for scientists, with focused workshops for early career researchers (ECRs) and scientists from underserved regions, multi-lingual support for workshops and online resources, publicly posted training videos, and model workflows for data handling. Neotoma developers will reduce barriers to access and support artificial intelligence and machine-learning applications by deepening Neotoma’s metadata provisioning to Science-on-Schema and DataCite. Lastly, Neotoma stewards will create custom-tailored training and leadership opportunities for ECRs by designing workshops, videos, and code vignettes to address ECR-identified challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The goal of this award is to carry out a series of four events running in parallel with the 3rd Congress on Evolutionary Biology in Montreal, Canada, on July 27-30 that will bring diverse members of the evolutionary biology community together to discuss the predominant problems in the publishing ecosystem, to enunciate concerns about the selective pressures exerted by this system in the near and more-distant phases of their careers, and to identify actions that may be tractable for many and therefore effective for all. The current, predominantly for-profit, scientific publishing ecosystem has become untenable: it is broadly unaffordable, it disproportionately excludes underrepresented researchers, it relies on largely uncompensated scientific labor, and it intensifies the pressure to publish. There is little consensus about effective remedies to this system and limited unified action to effect change: researchers make private decisions about where to publish, individual editorial boards resign ad hoc, societies negotiate separately with for profit publishers, and the highly profitable publishing industry continues to take an increased share of research funding. The four events will include: (1) a pre-congress virtual workshop to census the global community, (2) an in-person pre-congress workshop focused on early career researchers, (3) a symposium at the Congress sharing with the community findings from the two workshops and perspectives from thought leaders on publishing change, and (4) a post-congress Working Group that will draft a white paper, a publication, and establish a consortium of scientific societies to effect long-term change. The activities will engage and learn from the experiences of evolutionary biologists globally and early career researchers who must be able to prosper in the current and future publishing ecosystem. Members of these groups, who are particularly at risk, will also have voices in the Symposium and Working Group to ensure that scoping and proposed action are representative and feasible. These activities will help empower future generations of scientists. 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.

NIH Research Projects · FY 2026 · 2024-02

Project Summary/Abstract Cell fate decisions occur during embryonic development, morphogenesis, and cell reprogramming. These wide- spread cellular changes include launching global gene expression programs that are controlled by pioneer transcription factors (PTFs) which serve as the master regulators of cell fate. Manipulating PTFs is a fundamental building block for regenerative medicine, while the dysregulation of PTFs is linked to the development of cancer. PTFs are special since they can recognize their cognate DNA sequence motifs on both naked DNA and the DNA wrapped into the nucleosomes that form chromatin. The ability of PTFs to bind DNA sequences within chromatin is thought to facilitate nucleosome unwrapping, either directly or by recruiting additional factors, to start the transcription programs. The detailed processes that permit PTFs to recognize cognate sequence motifs on naked DNA or nucleosomes as well as the mechanics of PTFs once bound to nucleosomes are largely unknown. Perhaps more importantly, PTFs operate on very long genomic regions containing clusters of imperfect sequence recognition motifs. Removing these clusters destabilizes cell fates in vivo. However, the function(s) of these imperfect recognition motifs remains a puzzle. We propose to examine the mechanics of PTF recognition progressions using the Engrailed and Wor1 as models for high-resolution biophysical analysis. Engrailed is present in all higher order organisms and plays an essential role in body patterning. Wor1 drives the White-to-Opaque cell switch of Candida albicans, the agent of common human invasive fungal infections. The White-to-Opaque cell switch, which affects virulence and niche selection in clinical candidiasis, is controlled by an easily manipulated genetic circuit in vivo. Our previous and preliminary studies demonstrate that the Engrailed DNA binding domain (enHD) is highly promiscuous in the recognition of cognate DNA sequences. Moreover, enHD appears to be a fast but heterogeneous DNA scanner and undergoes a significant conformational transition when it binds to a high-affinity cognate motif. It is our overarching hypothesis that imperfect-motif clusters organize regions in active DNA and chromatin where PTFs home in on via heterogeneous scanning and promiscuous recognition, and ultimately act on via binding-induced conformational transitions. We have implemented advanced single-molecule detection systems to resolve PTF-DNA dynamic interactions with µsec resolution at nm precision. These techniques will be used to address the following Specific Aims: 1) Dissect the EnHD and WOPR sequence recognition code for DNA and nucleosomes, 2) Resolve the dynamic scanning and mechanical interaction between EnHD and WOPR with imperfect-motif clustered DNA, and 3) Examine Wor1 interaction dynamics with the White-to-Opaque master regulatory element of C. albicans in vitro and in vivo. The proposed studies will detail the DNA recognition processes used by PTFs that lead to cell fate decisions involved in tissue patterning, morphogenesis, cancer, and cellular reprogramming.