University of Maryland, College Park

NSF Awards · FY 2024 · 2024-09

The first living organisms sensed their environment (input) and responded to it (output). They computed their responses. Today, we write and execute complex programs. The fastest computers are slower, more energy-intensive, and less complex than the human brain. Harnessing neuronal computing capabilities would represent a giant leap forward in computing power, speed, and efficiency. This project will establish a three-dimensional (3D) network of neurons as a brain analog. The small clumps of neural cells, called organoids, act as the nodes of the network. This research will be organized into three distinct subjects. The first is biocomputing theory to identify functional neuronal networks. The second is to develop organoid culture and hardware interfaces to maintain stable brain organoid cultures. The instrumentation will stimulate and record neuronal activity. The third is discussion and analysis of ethical concerns identified within the research to build awareness, literacy, and reasoning capacity of the researchers and the greater scientific community. This research aims to develop a mechanistic understanding of how to train neuronal networks in organoids. The structure and physiology of neurons will be related to their role in computation and learning. It will establish concrete examples of how to map computation and learning paradigms onto 3D biocomputing networks. This project leverages and extends existing technologies for culturing neurons in patterned 3D microenvironments and for recording/stimulating neurons using high density microelectrode arrays and optogenetics. There are three primary scientific hypotheses underlying the project. First, that 3D cortical organoids can be trained to respond selectively to one pattern out of many distractor stimuli. Second, that this association can be established simultaneously for many distinct patterns. Finally, that training based on closed-loop approaches incorporating mechanistic insight will be more effective than either open-loop approaches or closed-loop approaches that do not take into consideration underlying neural structure. This project is jointly funded by the Emerging Frontiers in Research and Innovation Program (BEGIN OI) and the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Since 1975, the triennial International Congress of Invertebrate Reproduction and Development (ICIRD) has showcased high-quality research across the entire breadth of animal life. This conference is highly integrated along several dimensions, as it brings together biologists from many nations and institution types who are conducting research on many different invertebrate animal species. Further, their research examines reproduction and development at diverse biological levels, from ecological context to the roles of individual genes. This award supports the resumption of the ICIRD series, after the scheduled 15th Congress was cancelled due to the COVID-19 pandemic. More specifically, the award will support the attendance of 14 US researchers at different career stages at the Congress, to be held June 6-10, 2025 in Washington, DC. This meeting presents an excellent opportunity to support early career trainees in the field of invertebrate reproductive biology to network and learn about the exciting new advances in the field. The Broader Impacts of the conference include the support of early career researchers and the advancement of research programs. The International Congress of Invertebrate Reproduction and Development (ICIRD) is a conference that takes a unique pan-metazoan view of reproduction, with a focus on less-studied taxa. Scientific topics to be covered at the conference include, but are not limited to: regeneration/body maintenance/asexual reproduction, invertebrates as food, evo-devo of reproductive mechanisms, gametogenesis and fertilization, evolution, genomic and genetic tools for pan-metazoan reproductive biology, larval development, and metamorphosis. By bringing together diverse researchers who work on many different animal phyla at several biological levels, broad patterns will appear that are not likely to emerge in conferences focused on a single taxon or level of organization. This award will be utilized to defray the costs for early career researchers (early-career PIs, post-doctoral fellows and student trainees) to attend and participate in the Congress. 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

Non-technical summary Optically active spin qubits are the fundamental building blocks of quantum networks and distributed quantum computers. This project aims to significantly advance quantum information science by developing optically active quantum bits (qubits) in silicon, allowing us to utilize the existing vast infrastructure for silicon processing to develop scalable quantum devices. The program focuses on a color center called the T center. This optically active spin qubit can be directly engineered in silicon, enabling the convergence of quantum photonics and silicon photonics in a single material platform. Our research will develop the fundamental science and device physics necessary to generate scalable spin qubits based on T centers for large-scale quantum photonic devices. We will enhance the T center's optical properties through nanophotonic integration to achieve an efficient interface between light and spin. We will demonstrate key quantum operations including indistinguishable single photon emission, spin initialization, and coherent control of electron and nuclear spins, paving the way to entangle spins over long distances using fiber-optic channels. We will also use advanced noise spectroscopy to study the fundamental noise processes that limit spin coherence times, and establish advanced dynamical decoupling pulse sequences that can extend them by orders of magnitude. Our ultimate goal is to create repeatable arrays of Elementary Memory Units (EMUs) using T centers. EMUs combine quantum memory with photonics, electrical tuning, and microwave control to form a self-contained building block for future quantum networks and distributed quantum processors. Using these EMUs, the project will explore methods to generate and distribute entanglement between T centers across different locations, including within the MARQI network, developed by the PI to connect quantum nodes at the University of Maryland campus, neighboring research labs, and quantum startups. The program will have a wide-ranging impact on multiple fields including integrated photonics, quantum optics, and spin-based information processing. It will also contribute significantly to education and workforce development in quantum technology by supporting graduate and undergraduate students, as well as developing educational modules for the Quantum Atlas, an interactive resource aimed at enhancing public understanding of quantum science. Furthermore, the project will offer research opportunities to students through the UMD GRAD-MAP program. Technical summary Optically active spin qubits are the fundamental building blocks of quantum networks and distributed quantum computers. Integrating these qubits into silicon, a highly scalable integrated photonics platform, would constitute a significant breakthrough in quantum information processing. This program will develop a new class of optically active spin qubits in silicon that exhibit exceptional optical properties combined with long spin coherence times. Our research will concentrate on the T center, a silicon color center that can exhibit efficient radiative emission within the telecom band along with the capability to store quantum information in both electron and nuclear spin. We will combine T center qubits with nanophotonic engineering and microwave excitation to demonstrate the core operations required for quantum applications: indistinguishable single photon emission, spin initialization, coherent electron and nuclear spin control, and single-shot readout. We will leverage these capabilities to create repeatable arrays of Elementary Memory Units (EMUs) based on T centers that can serve as the building blocks of distributed quantum information processors. Furthermore, we will elucidate the fundamental properties of the T center spin qubit through noise spectroscopy and implement optimized dynamical decoupling pulse sequences to increase spin coherence times by orders of magnitude. Finally, we will develop new methods to generate spin-photon entanglement optimized for T centers that leverage frequency and time-bin photonic qubit. 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

As mature speakers of a language, humans can produce and understand an indefinite number of sentences. This ability comes from a powerful cognitive system - syntax - whose properties reveal the types of computations that human minds can engage in. One core property is the capacity to encode abstract grammatical dependencies that can hold at a distance. When and how this property emerges in development is an important, outstanding theoretical question. This project examines infants’ representations of non-local syntactic dependencies before their second birthdays, even before they regularly produce full sentences of their own. This investigation illuminates the active syntactic development that occurs during the second year of life. In doing so, this project provides an important step for understanding the origins of the core computational capacities that syntax relies on. This project supports education by providing training opportunities in language sciences research. In addition, the project benefits society because it includes outreach about issues in language development to local families and high school students from under-represented backgrounds. The project focuses on the acquisition of the types of non-local syntactic dependencies in wh-questions, in which a fronted wh-phrase can act as the argument of a verb at a distance (e.g., What did the chef burn). The investigators examine when infants know that an object wh-phrase and a local object of a verb cannot co-occur, because they both express the same argument relation (e.g., *What did the chef burn the pizza). Recent work finds that 18-month-olds, but not younger infants, are aware of this complementary distribution pattern, suggesting awareness of the non-local grammatical dependency between the wh-phrase and the verb. This project provides a set of behavioral experiments that identify more precisely how 18-month-olds represent these dependencies, and the development that occurs prior to this age. Through this case study, this research establishes a firm foundation for the initial steps of syntactic dependency learning in infancy. This empirical foundation provides boundary conditions on theories of when and how initial grammatical knowledge is acquired, illuminating how learning from experience interacts with children’s early capacities for representing the speech they hear. 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

Potential theory has its roots in classical physics and the realization that gravity and the electrostatic force can be described using so-called potential functions, both of which satisfy Poisson's equation. In more recent decades, potential theory has been recognized as ubiquitous in many different areas of mathematics and physics. This project seeks to understand the role of potential theory in complex geometry. Broadly speaking, complex geometry aims to find ideal geometric models that are simple enough to model the physical universe, describing interactions between small particles, or even colliding galaxies. The PI will also train graduate students in the subject area, and introduce undergraduates to this field through summer research programs, in each case focusing on providing opportunities to traditionally underrepresented members of academia. On the outreach side, the PI will continue to make educational videos disseminated online, as well as give popular science lectures in local middle schools. Complex geometry is the discipline at the intersection of differential and algebraic geometry. As advocated by Demailly, Siu, and others, it is possible to study this subject using potential-theoretic methods. Along these lines, the PI plans to make significant progress on a cluster of interconnected questions and conjectures in complex geometry by employing a combination of methods from potential theory, infinite-dimensional geometry, and more traditional methods of geometric analysis. There is a vast literature on Kähler quantization for smooth metrics. Instead, the PI will study quantization in the context of finite energy potential theory that accommodates degenerate Kähler metrics. Potential applications range from the quantization of Radon measures to Yau-Tian-Donaldson type theorems. The PI will also study the fascinating connections between convex and complex geometry in the transcendental context. This includes establishing a link between the Hausdorff geometry of convex bodies and the geometry of singularity types of quasiplurisubharmonic functions, and better understanding the complex Brunn-Minkowski inequality of Berndtsson, leading to uniqueness theorems for degenerate canonical Kähler metrics. 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 goal of this EArly-concept Grant for Exploratory Research (EAGER) grant is to enhance pilot training by developing methods to optimize sensory-motor interactions, thereby reducing training duration through accelerated learning. Traditional pilot training mainly relies on dominant sensory cues like vision and equilibrium. This research explores how secondary sensory cues, such as the senses of touch and audition, can be used to improve training efficiency. Additionally, the project seeks to leverage insights into pilot neurophysiology to better integrate these sensory cues in real time. By applying principles from flight dynamics, control theory, human-machine interaction, and neurophysiology, this project aims to create a comprehensive framework for optimizing sensory-motor interactions. This research supports the national interest by advancing science and promoting public welfare through improved training methods that enhance safety and efficiency in aviation. This study is particularly relevant as it supports the strategic transition towards Single Pilot Operations, which are projected for future passenger airplanes and next-generation rotorcraft aimed at Urban Air Mobility, commonly referred to as air taxis. Broader impacts include developing digital assistant systems that adapt to the cognitive workload of operators, reducing training costs, and improving safety in complex environments. The specific objective of this research project is to develop methods for optimizing sensory-motor interaction strategies to minimize pilot training duration. This involves creating a pilot training platform that uses multiple synthetic actors for neuroadaptive multimodal cueing. In this setup, a human pilot collaborates with an intelligent agent to control a simulated vehicle, functioning as a symbiotic organism. The vehicle can be any machine capable of moving across regions of physical space such as airplanes, helicopters, or drones. The pilot receives multimodal cues through five synthetic actors: virtual and augmented reality goggles for visual cues, a motion-base platform for proprioceptive cues, spatial audio headphones for auditory cues, full-body haptic suits for haptic feedback, and active control inceptors for additional haptic cues. Real-time neuroadaptation enables the intelligent agent to adjust these cues and its control authority based on the pilot’s cognitive and physiological states and the performance of the pilot-vehicle system. This approach integrates tools from flight dynamics, control theory, human-machine interaction, and neurophysiology to develop a framework for optimal sensory-motor interaction. The project introduces several novel aspects, including the simultaneous use of multiple synthetic actors in sensory-motor interaction, the application of secondary sensory cues, and the adaptive modification of multimodal cues based on real-time data. The anticipated outcomes include advancements in digital assistant systems, improved training methods, and enhanced operational safety. 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

This project focuses on improving silicon photonic integrated circuits (PICs), which have important applications in telecommunications, quantum information, and artificial intelligence. Current methods to adjust or tune these circuits are inefficient, especially in cryogenic environments required for many future applications. Our approach introduces a new type of energy-efficient tuning element that doesn’t require continuous adjustment, inspired by techniques used in electronic memory chips. This innovation will enable more compact and efficient circuits, overcoming significant barriers in the field. The project will also foster educational growth and diversity by involving graduate students and making the designs freely available to the scientific community. In silicon photonic circuits that employ microresonators, unavoidable fabrication variations mean the resonant wavelength often needs adjustment, typically through dissipative thermal tuning. In cryogenic settings, this becomes impractical. The goal of this project is to develop a new class of switchable, digital, nonvolatile micromechanical tuning elements for photonic circuits that eliminate the need for persistent, resonator-specific tuning. This approach harnesses the bistability in micromechanical beams, achieved through geometric engineering or intrinsic film stress with controlled release, enabling predictable beam deflections for controlled phase shifts in microresonators. This digital tuning approach allows for precise and stepwise adjustment of resonant wavelengths in large-scale photonic integrated circuits, without the need for continuous active tuning. This approach could enable high component density, operational energy efficiency, and compatibility with standard foundry processes. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Research and Innovation (UKRI), where NSF funds the U.S. investigator and EPSRC funds the partners in the United Kingdom. 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

This grant provides support for broadening student participation at the 2024 Modeling, Estimation, and Control Conference (MECC 2024) in Chicago, Illinois, 27-30 October 2024. Held annually since 2021, MECC focuses on the intertwined research problems of building mathematical models of dynamic systems, fitting these models to measured data, and using the models for control algorithm design. The scope of the conference encompasses theoretical research as well as a broad range of applications, including the automatic control of (ground, air, marine, and space) vehicles, transportation networks, power/energy systems, robots, and biomedical systems, to name some examples. MECC is predominantly an academic research conference, with most attendees either seeking or already having doctoral degrees. The overarching goal of this grant is to broaden this audience significantly by recruiting and engaging K-12, undergraduate, and early-career graduate students. This recruitment effort will focus predominantly on students who have never attended a control conference before, with the goal of making the conference more accessible to female and underrepresented minority students. Three education and outreach events will be created as part of this grant. These events will focus on introducing student participants to the automatic control discipline in general, its application to scaled autonomous vehicles, and its interplay with embedded computing. The speakers at these events will include a mix of researchers and practitioners from both academia and industry. Students will be recruited to these events from universities nationwide, as well as from a broad range of K-12 schools in the Chicago area. A panel of judges will select at least 40 of these students for support through this grant, with an emphasis on expanding conference participation and accessibility to a broad range of student participants. Funding will be used for supporting the students’ travel to the conference, providing them with access to take-home kits for their continued learning, and supporting the participation of a diverse range of speakers at the above three events. A key goal of these efforts will be to transform MECC itself, from an academic research conference to an event that brings research and teaching much closer together. Such a transformation will benefit all conference participants. It will broaden access to the conference by engaging a more diverse student population. It will also give the traditional conference attendees more of a chance to discuss both the intellectual merit of their research as well as the broader impact of this research, particularly given the broader and more diverse audience. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Language is a fundamental aspect of human experience, influencing how humans communicate, understand, and shape the world. This workshop, held in conjunction with an annual interdisciplinary conference, highlights societal impacts of psycholinguistic research. This workshop bridges the gap between academic research in psycholinguistics and the broader community, including educators, mental health professionals, government stakeholders, and industry colleagues, highlighting why diverse outreach efforts are essential for the field of language sciences. Other benefits to society include providing education on innovative tools and strategies for amplifying the impact of language sciences research, including lessons learned and best practices. This workshop explores how language science can address real-world challenges, how translational applications can inform language science, and how to foster productive collaborations between researchers and the broader community. Focusing on examples of successful partnerships with museums, health professionals, government agencies, and the tech industry, the workshop aims to encourage language scientists to work alongside community partners to understand and address real-world problems. Through interactive discussions and presentations, attendees explore how language science can contribute to more effective and equitable workplace policies, diagnostic tools, interventions, and frameworks for informal science education. The workshop also showcases successful research partnerships that have influenced quality of life in various ways, illustrating the practical applications of language processing studies beyond the lab. 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

Explosive objects in the distant universe can now be studied by simultaneously combining information from multiple messengers - gravitational waves, particles, and light. The investigators will develop software to deliver new discoveries and physical constraints concerning the nature of explosive objects. The investigators will provide students opportunities for cross-institutional internships and collaborations with amateur astronomers and citizen scientists. The research, methods, and visualizations will be directly included in developing courses at multiple institutions. The work will provide training for students in critical areas for astrophysics and beyond, including robust application of machine learning. The team will partner with the LIGO Science Education Center and The Baton Rouge: Bringing Youth Technology, Education and Success programs to utilize multimessenger astronomy to inspire K-12 students in the state of Louisiana. A 4-year research program led by investigators at the Louisiana State University, Harvard University, University of Minnesota-Twin Cities, and University of Maryland, College Park will improve our understanding of explosive transients. The exotic zoo of explosive transients is still being explored, and the overlap of signals seen at different wavelengths is key to their taxonomy. Explosive transients occur at the extremes of physics, beyond the reach of terrestrial laboratories. Multiwavelength and multimessenger observations of these transients enable advances in areas including gravity, fundamental physics, dense matter, cosmology, and the origin of the elements. The proposed work will enable new discoveries through the power of the Vera Rubin Telescope with concurrent observations provided by high energy and gravitational-wave observatories. The research team will combine observations of compact objects with the Vera C. Rubin Observatory’s Legacy Survey of Space and Time with space-based gamma-ray burst monitors and ground-based gravitational-wave interferometers. Focusing on gamma-ray bursts and supernovae, the team will construct new optical transient classifiers, develop the formalism to associate distinct signals across wavelengths and messengers from the same event, characterize these events through dedicated follow-up, and enable global discovery via public alerts. The result will be an end-to-end multiwavelength and multimessenger discovery machine. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The two-meter-long genetic material in human cells is tightly packed into the nucleus that is a million times smaller. To accomplish this packing, DNA, the primary genetic material, is tightly wound around proteins called histones which are then hierarchically organized as chromatin. In a process called transcription, specialized proteins read out the information contained in the DNA sequences to make new proteins that then carry out all cellular functions. Chromatin, rather than being an inert template, is a dynamic polymer, which undergoes further reorganization when cells undergo changes in their state, such as during development. How this information is read out from the tightly packed, dynamic chromatin is poorly understood as are the physical rules of spatial organization of chromatin. Cells of the adaptive immune system, which help the body fight infection offer a tractable system to study the interplay between gene transcription and dynamic chromatin. When exposed to foreign proteins, these cells undergo extensive changes in chromatin organization as they fight the infection and subsequently develop a memory for it. In this award the investigators will use quantitative experimental approaches and theories from physics to address long standing gaps in our knowledge of critical processes in development, immune response and disease states such as cancer. This project will also provide novel educational and training opportunities for undergraduate and graduate students in advanced optical microscopy techniques, image processing, and data analysis as well as polymer physics and biophysics. Genomic techniques have established the basic structural rules of hierarchical organization of chromatin but they offer only a static, cell-averaged snapshot. Transcriptional readout of the genetic information contained in chromatin does not occur on a static template. Rather, chromatin exhibits dynamics at multiple time scales, ranging from the sub-second scale thermal motion of the chromatin polymer to minutes and hours long reorganization in response to developmental programs and external chemical and mechanical stimuli. When cells undergo differentiation, the genome undergoes extensive structural reorganization of chromatin leading to functionally appropriate gene expression. How sequence specific proteins that bind DNA and regulate transcription, known as transcription factors (TF), interact with dynamic chromatin to regulate gene expression remains poorly understood. This award will use CD8+ T cells (also called “killer T cells”) as a model system to study this question. Upon antigen exposure, T cells rapidly differentiate into effector cells and memory cells. Effector T cells ultimately kill infected target cells, while memory T cells circulate in the body in a “naive-like” state and rapidly recall re-exposure to antigen and transform into effector cells. Genomic studies have revealed that T cell activation and differentiation is accompanied by extensive reorganization of the genome, but how these changes affect chromatin dynamics and interactions with transcription factors is not well understood. The Principal Investigator’s laboratory have developed a suite of techniques – from single molecule tracking to whole genome imaging to study the dynamics of chromatin and TFs over transcriptionally relevant timescales. In this award they propose to elucidate the driving forces for chromatin patterning, its dynamical evolution and the resulting impact on transcriptional dynamics with T cell activation and differentiation as a model. More generally, this work will have implications in understanding the fundamental principles underlying chromatin dynamics and TF/chromatin interactions during cell differentiation. This research, which lies at the nexus of physics and biology, will provide interdisciplinary training opportunities for graduate and undergraduate students. The project will also provide STEM research experiences for undergraduate students and high school students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award supports research on the interplay between three different research areas: polylogarithms, cluster algebras, and hyperbolic geometry. Polylogarithms generalize the natural logarithm and have been studied since the 18th century. Cluster algebras, invented in the early 21st century, are purely combinatorial objects which are widely studied and broadly applicable. Hyperbolic geometry is a geometry with constant negative curvature, where Euclid's fifth postulate fails. Recent advances have revealed surprising links between these areas. For example, formulas for scattering amplitudes in high energy physics frequently involve polylogarithms evaluated at cluster algebra coordinates. Also, the volume of a certain hyperbolic polyhedron known as an orthoscheme, where successive faces form right angles, is given by a polylogarithm formula. The proposal will investigate key conjectures, find new examples of hyperbolic manifolds, and compute invariants using cluster coordinates. The PI will involve both graduate and undergraduate students in this project and continue his outreach to local schools. The proposal will explore the relationship between polylogarithms and cluster algebras focusing on several key conjectures in the field. These include the Matveiakin-Rudenko conjecture, that all polylogarithm relations arise from the cluster polylogarithm relations of type A_n; Zagier's polylogarithm conjecture, that the zeta function of a number field at integers is expressed by polylogarithms; and Goncharov's depth conjecture, that a polylogarithm is a classical polylogarithm if an only if its truncated coproduct vanishes. The proposal will explore special cases of these conjectures using Matveiakin and Rudenko's notion of cluster polylogarithms as well as new tools developed by the PI and his collaborators. In addition, the proposal will study Rudenko's polylogarithm formula for a hyperbolic orthoscheme, find new examples of hyperbolic manifolds that don't arise from Coxeter groups (and therefore have dihedral angles that are not a submultiple of pi), and generalize formulas for Cheeger-Chern-Simons invariants from dimension 3 to dimension 5. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Low Dimensional Topology is a branch of mathematics that studies shapes of three and four-dimensional objects. This project explores such objects using tools inspired by modern physics. One such tool is the Yang-Mills equation, used in quantum field theory to describe electro-weak interactions. Solutions to the Yang-Mills equation on a manifold can reveal deep insights into the underlying topology. The construction of configuration spaces on manifolds is inspired by Feynman diagrams; it has recently been used to answer long-standing open problems in low-dimensional topology. These ideas also interact closely with many other areas of mathematics, such as non-linear partial differential equations, algebraic topology, and algebraic geometry. The PI will use the existing tools in gauge theory and configuration space theory to study questions in three- and four-dimensional topology and develop new tools in this field by working on fundamental analytical questions about gauge-theoretic equations. During the project, the PI will train graduate and undergraduate students, organize high school educational activities, and participate in outreach programs to attract more students to mathematics. The research activities of this project will focus on the following three major directions. The first direction studies the higher algebraic structures in instanton Floer homology and its relations with gluings of 3-manifolds. The second direction studies the analytic properties of generalized Seiberg–Witten equations. The third direction explores the applications of Kontsevich configuration space integrals in 4-dimensional topology and uses it to study smooth mapping class groups in dimension 4. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Technical Abstract: The quantum science and engineering community is focused on developing protocols and devices that leverage the unique features of quantum mechanics to achieve classically infeasible tasks. Realizing this potential requires highly trained scientists and engineers who are fluent in quantum concepts. More must be done to attract, train, and place emerging talent to meet the needs of a growing market in industry, government, and academia. Populating the growing quantum workforce requires training in science communication, exposure to career pathways, and access to internship and job opportunities. The Institute for Robust Quantum Simulation has developed the Quantum Leap Career Nexus workshop to advance workforce development through internship and job placement. The goal is to facilitate more such placements in the local and regional area, where a multi-university quantum-focused career workshop does not yet exist. Non-Technical Abstract: This workshop is a direct result of feedback from and was designed by young researchers, who are interested in learning more about career pathways and are eager for opportunities to expand their professional networks, engaging more fully in the quantum industry. The program brings together undergraduates, graduate students, postdoctoral scholars, and early-career professionals to build career skills, establish mentorships, widen networks, learn about emerging career pathways, create new connections between talent and employers in quantum science and engineering, and showcase research and work experience to secure promising placements. It provides career-development training to enhance job-seekers’ marketability through stronger resumes, expanded digital footprints, and improved communication to a non-technical audience. A significant portion of the workshop is dedicated to networking and recruitment activities with potential employers, to help with internship and career placements in quantum science and technology. The location in Prince George’s County, Maryland provides a unique opportunity to leverage the mid-Atlantic region’s diverse population of students and professionals and its abundance of employers across the quantum industry, academia, and government. In addition to inviting attendees from local, regional, and national partners such as the Institute’s partner universities, other Quantum Leap Challenge Institutes, and other quantum centers, this workshop invites attendees from historically Black colleges and universities and minority-serving institutions. To broaden access to career opportunities in quantum science and technology for students of all backgrounds, the workshop covers travel expenses for at least fifty attendees. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

During manufacturing of current semiconductor and related industry products, atomic precision in materials etching is required. Plasma-based atomic layer etching (ALE) provides a solution to achieve precise target dimensions and material selectivity by using ion-bombardment. Damage due to ion-induced mixing and atomic displacement has become an important concern for devices with near-atomistic critical dimensions. This Ecosystem for Leading Innovation in Plasma Science and Engineering (ECLIPSE) Grant Opportunity for Academic Liaison with Industry (GOALI) award supports fundamental research to obtain the required knowledge for the development of the electron beam activated atomic layer etching (EB-ALE) process which avoids ion induced atomic displacement and damage. A plasma source that is spatially distant from the substrate chemically modifies the material surface. This is followed by electron beam irradiation induced etching restricted to the modified surface. The novel EB-ALE approach enables layer-by-layer etching for critical applications involving complex multi-element materials that are extremely sensitive to process damage. The application of EB-ALE for processing of advanced materials and structures benefits the US economy and society by wide-ranging impacts on advanced manufacturing for microelectronics, quantum technology, renewable energy, electrical storage applications, and others. The academic and industrial collaboration provides unique educational and training opportunities for the students, faculty and researchers involved, including women and underrepresented minorities, and accelerates technology transfer. Electron-beam induced atomic layer etching (EB-ALE) of materials is based on surface functionalization using a remote plasma source, and subsequent electron beam-irradiation of the modified surface. Electron bombardment induces bond breaking processes and achieves self-limited etching while avoiding ion-induced displacement damage connected with direct plasma based atomic layer etching methods. This project aims to make fundamental contributions to the knowledge gap in understanding surface chemical and physical aspects of EB-ALE mechanisms by combining surface processing experiments using selected precursors/substrate materials and in-situ diagnostics. Maintaining stoichiometry is vital for advanced materials, such as, Ge2Sb2Te5 phase change memory alloys. Dissociation of precursor gases in the remote plasma source and transport to the material surface are studied for different operating conditions, including remote plasma power, gas flow rates and mixing, and correlated with the measured surface coverage. Etching behavior is established as a function of electron beam current density and energy, surface chemical modification and coverage and device structure. The relative importance of electrical charging is examined by evaluating insulators and conductors for different electron beam irradiation conditions. The nature of surfaces and defects of model complex materials after EB-ALE is established using an array of sensitive analytical and electrical probes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project aims to understand complex physical processes like pollution transport, virus spread, and wildfire evolution by integrating physics and artificial intelligence. These movements of interest within each of these processes are influenced by uncertain background flow velocities; that is, how fast the pollution or virus moves, making identification of the source of the problem challenging. The proposed research combines equations from physics that govern physical movement with generative machine learning, specifically focusing on stable diffusion models. By incorporating uncertain flow velocity information, we aim to enable more accurate source identification from limited observations. This innovative approach promises to enhance our ability to manage environmental and societal disasters, leading to improved pollution control, risk assessment, and disaster response strategies. The project develops physics-constrained generative stable diffusion models to reverse advection-diffusion processes. It addresses uncertainties in background flow velocities by developing a stable-diffusion formulation to gradually remove the stochasticity in the backward process, and adopt appropriate diffusivity learned through the training data. This approach integrates physical governing equations as guidance, allowing for reliable modeling that can be conditioned on the limited information of background flow fields. The research aims to quantify how these uncertainties affect source identification accuracy, providing a transformative solution in environmental monitoring. By retrospectively interpreting observations, we aim to unravel causal relationships leading to current states. The project also aims to advance interdisciplinary education and support diverse student participation in engineering and environmental science research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

High energy neutrinos are unique messengers to the distant universe. Neutrinos are extremely small and lightweight particles that are created in high energy interactions. They are particularly powerful probes because they are weakly interacting – they can travel vast distances without being stopped, and are not bent by magnetic fields. This makes them complementary probes to the other high energy particles we know of – cosmic rays and gamma rays. This proposal will use the IceCube Neutrino Observatory to search for the highest energy neutrinos possible – events above 1 PeV. IceCube is a neutrino detector at the South Pole deployed over 2km deep within the glacier in Antarctica. It uses photosensors to measure the blue flashes of light generated by neutrino interactions in the ice. By searching for these extremely high energy (EHE) events, we will study the origin and nature of the highest energy accelerators in the universe. Because neutrinos are expected to be produced in the same locations as cosmic rays and gamma rays, the observations of neutrinos provide definitive evidence of charged particle acceleration. And because they can travel far distances unimpeded, they track how the accelerators are distributed throughout space and time across the history of the universe. Additionally, these EHE neutrinos are hundreds of times more energetic than any particles which can be produced on Earth, allowing us to study physics at new energy scales. The goal of this project is to enhance IceCube’s search for EHE neutrino events. We plan several technical advancements to make this possible. Searching for EHE neutrinos with IceCube is challenging because of the need to reject the overwhelming flux of atmospheric muons that arise from cosmic ray interactions in the atmosphere. We plan to develop new methods for separating these backgrounds from neutrino signals through use of new variables, such as stochasticity, and new techniques, such as machine learning algorithms. We also plan to improve both the energy and directional reconstruction algorithms used in the detector to account for detector effects such as PMT saturation. With these enhanced capabilities, we will conduct the most sensitive search for neutrinos above 100 PeV, with the potential to observe O(5) EeV scale neutrinos. The search could also yield important insights into the shape of the astrophysical flux in the 1-100 PeV regime. These two measurements combined will indicate whether the PeV neutrino sky is fundamentally different from the TeV neutrino sky, or if nature transitions to new and more powerful accelerators. We will also use these methods to enhance IceCube's realtime follow-up program, boosting its contribution to multimessenger science through improved purity and angular resolution. We will additionally contribute to a program of outreach and education by mentoring an undergraduate scholar through the UMD GRAD-MAP program and other events like “Maryland Day.” The award is aligned with the NSF Big Idea of Windows on the Universe: the Era of Multi-messenger Astrophysics as it coordinates the use of multi-messengers observations utilizing cosmic neutrinos and providing alerts to the larger community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The plant hormone ethylene regulates numerous physiological responses of agronomic importance. Plants produce ethylene from a molecule called 1-aminocyclopropane-1-carboxylic acid (ACC). Separate from ACC’s central role in ethylene biosynthesis, there is growing evidence that ACC itself can trigger its own responses in plants, and there is preliminary evidence that the signaling pathways for ACC involve metabolite signaling, which is a relatively unexplored topic in plant biology. This project seeks to define the ethylene-independent roles of ACC and to elucidate the potentially novel molecular mechanisms underlying the process of ACC signaling. The project uses a combination of approaches, including metabolomics, transcriptomics, and molecular genetics, and employs two different plant model systems, a flowering plant and a non-flowering plant (a liverwort). The results of the project are expected to elucidate the mechanisms of ACC signaling, enhance our understanding of ACC and ethylene hormone biosynthesis, provide an evolutionary perspective on the mechanisms of ACC action, and lay the foundation for incorporating ACC and metabolite signaling into revised mechanistic models of plant growth and development. The Broader Impacts of this project include the intrinsic merit of the research as all flowering plants use ACC/ethylene to control critical agronomic traits such as fruit ripening. Additional work will provide science education for students, teachers, and the general public, with an emphasis on the engagement of students from all backgrounds. These experiential learning activities take place in high school classrooms, college classrooms, research laboratories, and public science centers. Growing evidence indicates that the biological precursor of the plant hormone ethylene, ACC, is a novel signal capable of inducing a variety of responses independent of its central role in ethylene biosynthesis. Preliminary data indicate that responses to ACC likely involve complex mechanisms, given the detection of significant metabolomic changes when ACC biosynthesis is perturbed. The project goals are to define the ethylene-independent roles of ACC and elucidate the underlying mechanisms of ACC-induced responses. The researchers will investigate the hypothesis that ACC acts at least in part through metabolic changes detected by mass spectrometry in Arabidopsis thaliana as well as the bryophyte Marchantia polymorpha, which does not use ACC as an ethylene precursor. They also analyze the biological and metabolic consequences when A. thaliana plants lack the ability to synthesize ACC. These studies will provide insight into the evolutionary history of ACC function and advance the relatively unexplored topic of metabolite signaling in plants. The project also examines the molecular genetic basis of a specific A. thaliana response to ACC (lateral root formation) in A. thaliana, and similarly explores ACC responses in M. polymorpha starting from transcriptomic data. Taking a broader approach, the researchers genetically dissect the underlying mechanisms of ACC responses by cloning and analyzing the genes/gene products corresponding to ACC-insensitive mutants in both A. thaliana and M. polymorpha. The project’s findings should provide new perspectives on ACC and ethylene biosynthesis and lay the foundations for incorporating ACC and metabolite signaling into mechanistic models of plant growth and development. 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

Water scarcity presents a significant global challenge. Nearly two-thirds of the global population struggle with limited access to fresh water. While the Earth is covered in water, only a small portion is freshwater suitable for human consumption. This makes it difficult to provide clean water to communities. The goal of this project is to explore sustainable technology to capture and utilize water present in the atmosphere. Specifically, hygroscopic salts and biomass-based polymers will be combined to develop efficient atmospheric water harvesting (AWH) systems. Wide adoption of these AWH systems would enhance access to clean water, especially in arid regions experiencing water scarcity. The project includes several outreach activities to the broader community that will benefit society through increased public knowledge of science. These include webinars on sustainable materials innovations for global challenges, a collaboration with the Maryland Science Center, and community engagement to the Washington DC metropolitan area through Maryland Day. The project will advance the science and technology of AWH by exploring novel materials and system designs to efficiently extract drinking water from the air, addressing the pressing issue of water scarcity in human society. Building on promising preliminary studies, the project will investigate the synergistic effects of water vapor sorbents, superabsorbent polymers (SAPs), and photothermal materials on AWH performance. Specific research objectives include: (1) Selecting water vapor sorbents with high water capture capacity and speed through a comprehensive study integrating density functional theory calculations and experimental validation; (2) Exploring polysaccharide-based SAPs to enhance water uptake capacity and resolve salt leakage issues by functionalizing biomass-based foam surfaces; and (3) Investigating strategies for designing appropriate photothermal materials for the AWH system. These include carbonized porous materials based on biomass, hybrid biomass-carbon materials, and surface-treated biomass materials. Through detailed characterization and performance evaluation, the project will identify fundamental relationships between material composition, porous structure, and water harvesting efficiency. Developing standardized measurement protocols and AWH system prototypes will facilitate comparative analysis and practical implementation of the designed materials. 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 broader impact of this I-Corps project is the development of a new class of therapeutics based on bacterial extracellular vesicles for treatment of numerous diseases. Bacterial extracellular vesicles can potentially be produced at low cost and have numerous biotechnological applications including vaccines, therapeutics for inflammatory diseases, and drug delivery vehicles. In particular, most inflammatory diseases lack desirable treatments or cures, with an unmet need existing between first line therapies and more aggressive antibody-based therapies that require costly and laborious administration via intravenous infusions. This unmet need is particularly noteworthy for inflammatory gastrointestinal diseases (e.g., inflammatory bowel disease) and inflammatory skin conditions (e.g., dermatitis). Bacterial extracellular vesicles offer the safety and convenience of first line therapies, which are typically oral small molecule drugs with favorable safety profiles, and with the potential for improved efficacy like later line therapies. Clinical translation of bacterial extracellular vesicles has been hindered by low production rates from cells and lack of tools to further improve efficacy. These challenges present untenable manufacturing costs and biologic risk when translating results from mice to humans. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of vectors for oral delivery of immunotherapies for treatment of gastrointestinal diseases. Most therapeutics for inflammatory bowel diseases lack desirable delivery. Drugs unstable in the gastrointestinal tract require injection, and drugs with poor biodistribution and pharmacokinetics have limited efficacy and potential for systemic toxicity. The technology addresses these limitations in gastrointestinal disease treatment by leveraging bacterial extracellular vesicles - cell-secreted biologic nanoparticles naturally used by the healthy gut microbiome to communicate with human cells. The technology exploits this phenomenon by solving the most immediate bottlenecks facing bacterial extracellular vesicles therapeutics: potency and scalable biomanufacturing. The technology enables mass production of probiotic bacterial extracellular vesicles with high potency. Preclinical data in small animal models has demonstrated the technology function. 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

Tens of millions of Americans interact with artificial intelligence (AI) tools to find information, answer questions, or help them solve problems. One key drawback of these systems is lack of personalization: since modern AI systems do not know whom they are talking to, they can only give generic answers to user questions. But the answer to the question “why is the sky blue?” should be different if the person asking the question is a college student or a young child. This project aims to enable an AI model to provide more appropriate responses to users depending on their unique backgrounds, experiences, and needs. It will first gather a diverse dataset in order to characterize what kinds of responses are preferred by different people. The project will then use these data to develop AI systems that can tailor their answers to individual users, as well as evaluate how well the AI systems personalize responses. To achieve this personalization, the AI systems will learn to explicitly represent the kind of person they are talking to, based on their background or previous interactions, and then use this representation to generate an appropriate response. This project will result in AIs that can provide personalized, specific responses based on the person asking the question as well as resources that will help other personalize AIs. These resources will include datasets of personalized questions and answers, interfaces and visualizations to understand why AI provides specific responses over others; interviews and discussions with community members to understand their needs; and code and models that will allow others to build, train, and deploy personalized AI systems. While large language models (LLMs) trained on massive datasets have shown impressive performance on a variety of tasks, they still exhibit biases and struggle to be equally useful for everyone. While initially pre-trained on a language modeling objective, most LLMs are further fine-tuned to align their outputs with human preferences. However, existing techniques assume a “one size fits all” approach, ignoring diversity in user needs. This project will first construct probes to detect cases where models fail to adapt to the diverse needs of different users. Then, this project will develop Personalized Feedback for Diverse Populations (PFDP) to identify when models should be sensitive to the unique needs, knowledge, and background of users by examining the training trajectory of models and comparing models' answers to human preferences. PFDP will enable the development of models that can detect examples that are difficult for computers but not for humans, explain why such disparities in difficulty exist, and represent users’ needs and preferences within the model. To correct those shortcomings in the data, we focus on data curation: we propose techniques to automatically create new examples that ask questions about under-represented groups or require targeted responses to create adversarial prompt and response pairs with a human in the loop. Finally, with these new data, we develop techniques to allow modern architectures to make the most of these difficult (but few) examples. These techniques will allow for fine-tuning LLMs with a small curated subset of data that is robust to variations in prompts and will lead to the generation of acceptable answers for a diverse population of users. 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

Researchers have not been able to establish whether and how democratic governance causes better outcomes for lack of appropriate data. This award funds research that uses a unique historical data set and innovative economic methods to investigate how democratic governance improves the provision of public services, long term economic growth, and the well-being of citizens. The data involves changes in the structure of local governments that allowed some members to be appointed while others were democratically elected. These changes in the composition of local governments allows the researchers to use modern economic methods to establish causality between democratic governance and public service provision and long-term economic growth. The data collected for this research will be made available to other researchers interested in studying the effects of democratic governance on many outcomes. The results of this research will contribute to a better understanding of the effects of democratic governance on economic and social outcomes, thus helping to establish the superiority of democratic governance over other forms of governance. The results will also further help cement US as the global leader in democratic led human and social development efforts. While recent research typically finds the existence of a positive correlation between democratic governance and increased public goods provision and rapid economic growth, these studies have not establish a causal effect of democracy on these outcomes partly because of lack of appropriate data. This award funds research that will digitize a unique historical data set on democratic transitions in several local governments and use the data to draw causal inference on the effects of democratic governance on public service provision, long term economic growth, and overall well-being. Membership of these local governments consisted of appointed and democratically elected members and the formula for drawing the membership ensured that elected members differed randomly across communities. The PIs will use this quasi-random variation in democratically elected membership of these local governments causal identification. The digitized data will be made available to other researchers. Besides its contribution to economic science, the results of this research will demonstrate the superiority of democratic governance over other forms of governance in improving the lives of citizens and the need for its spread globally. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This award will support U.S.-based mathematicians – with a priority on early-career researchers – to participate in the dynamical systems conference “New Frontiers in Parabolic Dynamics and Renormalization,” to be held at the University of Bologna, Italy, July 24-28, 2024. The mathematical field of dynamical systems concerns the evolution of systems over time, including real-world systems such as the weather, traffic patterns, and planetary systems. Some of the most important examples exhibit chaotic behavior. Parabolic dynamics encompasses one class of dynamical systems with chaotic behavior, and renormalization is a powerful tool for uncovering its properties. This conference seeks to bring leading international researchers together to discuss systems that extend beyond parabolic systems and understand techniques that would expand the scope of renormalization and discover new techniques for studying such systems. Examples of parabolic dynamical systems include horocycle flows on hyperbolic surfaces and, more generally, unipotent flows in homogeneous dynamics, smooth area-preserving flows on surfaces, and nilflows on nilmanifolds. If we allow for the presence of singularities, it also encompasses the study of interval exchange transformations (IETs) and translation flows. More recently, another example that has attracted a lot of attention is the horocycle flow in the moduli space of translation surfaces. Many important examples of parabolic flows also arise from models in mathematical physics, such as the Ehrenfest model of Lorentz gases, systems of Eaton lenses, and the Novikov model, leading to flows on Fermi energy level surfaces in solid-state physics. A key technique used to study parabolic systems is renormalization, an idea that originated from physics, entered dynamical systems decades ago, and has become an increasingly powerful tool for understanding the long-term behavior of many classes of parabolic dynamical systems. This conference seeks to bring together leading researchers from around the world to share their recent advances with the community, which would include US-based graduate students and postdocs. More information about the conference can be found at https://events.unibo.it/parabolicdynamics. 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

NONTECHNICAL SUMMARY This CAREER award supports joint theoretical research and education to advance the theoretical foundations of condensed matter physics. Condensed matter physics concerns itself with systems composed of a large number of interacting constituents. Materials are a common example as they contain many atoms and many electrons. It is common to think of such complex systems not in terms of the individual constituents, but rather in terms of properties that emerge from their collective behavior. The concept of phases of matter is an important example of a collective property. Systems that show the same phase have similar properties. Ferromagnets have the collective property that the constituent atoms or electrons align in such a way that the magnetic axis of each one points in the same direction. Ferromagnets made of different materials are all ferromagnets. However, a ferromagnet is qualitatively different from an antiferromagnetic phase in which the magnetic axis of one atom points in the direction opposite that of its neighbor. So, systems that belong to the same phase have similar qualitative properties, while systems that belong to different phases have different properties. When quantum mechanics mingles with strong interactions among constituents very strange phases can emerge, such as the topological phases of the fractional quantum Hall effect; the latter occurs when electrons confined to a two-dimension plane by semiconductors are exposed to an intense magnetic field. Recently proposed fracton phases of matter are another turning point in this development. These phases have the interesting and distinct property of being hypersensitive to the geometry of the underlying material, for example the way atoms are organized on a lattice, as well as the presence of geometric distortions of the lattice. The PI will undertake a careful study and characterization of these phases, which necessitates the development new concepts and new theoretical tools. New tools will help advance understanding of the physical properties of fracton phases as well as suggest routes for experimental detection of fractions in materials. This is fundamental research; however, fractons could play an important role in developing quantum memory, and suggest new ways to think about quantum computing. Finally, it is already becoming clear that some fracton phenomena may have been discovered long ago in superfluids and liquid crystals, without realizing that these are but a page of a much bigger story. The PI will utilize the new techniques developed in the fracton context to gain new insights into the problems of vortices in superconductors, turbulence, and quantum liquid crystals. The education component of this CAREER project includes training undergraduate and graduate students. Students will explore how to use machine learning methods to gain insight into theoretical problems. The PI will participate in global efforts to increase diversity in physics through mentoring undergraduate students who are members of underrepresented groups leveraging American Physical Society initiatives. The PI will engage in outreach in local high schools by participating in career days and encouraging students to study science. PI will develop a course aimed at undergraduate and graduate students that will focus on applications of condensed matter physics ideas to deep neural networks. TECHNICAL SUMMARY This CAREER award supports joint theoretical research and education to advance the theoretical foundations of strongly correlated topological and geometric phases of matter. The project is focused on the physics of systems that support emergent fracton excitations. These excitations possess two remarkable properties: (i) they are topologically non-trivial and (ii) they cannot freely move through space. The constraints on their motion arise dynamically, while the underlying physical system is translation invariant. More concretely the research concentrated on three major efforts. (i) Fracton excitations can emerge in gapless correlated spin liquids. The PI will explore how the existence of these excitations affects observable properties of these systems. (ii) The constrained mobility of fracton excitations can be formally imposed by introducing additional symmetries. The variety of all possible mobility constraints roughly corresponds to all possible symmetries of this kind. The PI will develop a general theory of such symmetries and their manifestation in low energy properties of the physical systems constrained by these symmetries. (iii) A particular form of fracton behavior is already present in well-known systems such as superfluids, liquid crystals and quantum Hall states, where vortices, crystalline defects and composite fermions have a subtle version of constrained motion. The PI will investigate this tantalizing connection with the expectation that fracton machinery will provide a fresh look at these systems. The education component of this CAREER project includes training undergraduate and graduate students. Students will explore how to use machine learning methods to gain insight into theoretical problems. The PI will participate in global efforts to increase diversity in physics through mentoring undergraduate students who are members of underrepresented groups leveraging American Physical Society initiatives. The PI will engage in outreach in local high schools by participating in career days and encouraging students to study science. PI will develop a course aimed at undergraduate and graduate students that will focus on applications of condensed matter physics ideas to deep neural networks. 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 broaden U.S. student participation at the International Symposium on Machine Learning for Computer-aided Design (MLCAD), which has evolved significantly since 2019. The symposium is dedicated to exploring Machine Learning (ML) across all facets of Computer-aided Design (CAD) and electronic system design, jointly sponsored by the Association for Computing Machinery (ACM) and Institute of Electrical and Electronics Engineers (IEEE). Over the last six years, MLCAD has built on its successes each year, growing in attendance and the richness of discussions, making it a crucial event for both academia and industry in the domain of machine learning for electronic design automation (EDA). Funding of this proposal will not only enhance scientific discovery in areas of ML and EDA, but also broaden the impact of this interdisciplinary research field by increasing U.S. undergraduate and graduate student participation. This travel grant will provide students the opportunity to engage with leading experts in EDA and chip design by presenting their results at the conference proceedings, special sessions, and poster sessions. It will provide an exceptional opportunity for them to immerse themselves in cutting-edge research, as presentations and discussions happening in a small sized and focused group of researchers and engineers. This will be crucial for students' career development, allowing them to connect with potential mentors, collaborators, and perspective employers. MLCAD has become a new leading and focused conference event on advancing cutting-edge interdisciplinary research between Machine Learning (ML) and electronic design automation (EDA). MLCAD 2024 will be the sixth MLCAD event and will be soliciting refereed conference papers, special sessions, industrial sessions, and poster session, along with an industry sponsored networking event. The focused topics will include Large Language Model for CAD, ML approaches to logic and physical design, ML for power and thermal management, ML for verification and manufacturing test, etc. This travel award will prioritize U.S. undergraduate and graduate students who are coauthors of an accepted paper or poster at MLCAD 2024, including members of underrepresented groups, and who do not have alternative funding sources to attend MLCAD 2024. Each travel award application will be reviewed by a selection committee of at least three members, based on academic merit, relevance of their research focus to the conference, and the financial feasibility. 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.