Massachusetts Institute Of Technology

NSF Awards · FY 2024 · 2024-08

This project focuses on symplectic manifolds, which are crucial objects in understanding the mathematics behind many physical phenomena, such as the movement of planets and the behavior of particles. The main goal is to find different ways to identify and count surfaces within these spaces. Understanding these two-dimensional objects can help us comprehend the more abstract, high-dimensional spaces they exist in. By analyzing this detailed geometric information, the project aims to tackle theoretical mathematical problems inspired by physics, such as those seen in Hamiltonian mechanics and string theory. Solving these theoretical problems can enhance our understanding of complex systems, potentially resulting in advancements in technology, healthcare, and general knowledge of nature. In addition, this project will provide research training opportunities for students. The counts of pseudo-holomorphic maps into symplectic manifolds are usually rational-valued due to the presence of nontrivial automorphisms. The project aims to answer questions in Hamiltonian dynamics and mathematical physics by developing curve counts with coefficients beyond rational numbers, including integers, complex K-theory, and complex cobordism. New curve-counting invariants inspired by cohomological operations and homotopy-theoretic enhancement of Floer theory will be developed along the way. The research topics include global Kuranishi charts for operations in the integral Hamiltonian Floer theory, Adams operations in enumerative geometry, Floer homotopy types over complex cobordism, and the study of periodic points of Hamiltonian diffeomorphisms. 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 2024 · 2024-08

PROJECT SUMMARY The ultimate goal of this project is to create synthetic genetic circuits that accurately control the level of cell fate- specific transcription factors (TFs) autonomously in response to cell state changes. The underlying hypothesis is that the level and timing of expression of critical TFs dictates the efficiency of cell conversion protocols and the quality of produced cells. Here, we focus on the differentiation of human induced pluripotent stem cells (hiPSCs) into hemogenic endothelial cells (HECs) from which all hematopoietic stem and progenitor cells (HSC/HPCs) arise. Unfortunately, cur- rent methods to derive definite HECs (dHECs), which have the potential to produce adult-type lymphoid cells and HSCs, remain not only inefficient but are also difficult to execute and scale, and, as a consequence, exhibit high degrees of variability in outcomes between different labs, hiPSC lines, and even between replicate experiments.These problems hamper analysis of the underlying developmental processes and pose formidable obstacles to clinical translation of hiPSC-derived blood cell products since ensuring the safety and cost-effectiveness of the product necessitates high differentiation efficiency and consistency. Prior work has demonstrated that SCL (S), LMO2 (L), GATA2 (G), and ETV2 (E) TFs together are sufficient to convert hiPSCs-derived mesoderm to dHECs and that efficient forward programming requires discovery and subsequent implementation of both optimal expression levels and timing for each TF. Yet, con- ventional methods for TF-mediated cell fate programming rely on indiscriminate overexpression without any control on cellular TF levels. This is largely due to our inability to precisely control TF levels at user-defined values during cell fate programming, and this limitation has prevented discovering optimal trajectories and subsequently enforcing them. Here, we propose synthetic genetic controller circuits that overcome this hurdle. Specifically, in Aim 1, we create ge- netic circuit designs that set TF levels and use them in an efficient in vitro differentiation protocol to discover the optimal combination of S, L, G, E levels and timing. In Aim 2, we develop a new circuit architecture, based on TET1-enabled positive feedback, to prevent epigenetic silencing of our genetic circuits once we deliver them to hiPSCs. In Aim 3, we make our genetic controller circuits enforce autonomously the optimal SLGE TF levels found in Aim 1 in response to the hiPSC-to-mesoderm transition. We achieve this by a new autocatalytic ADAR-based RNA sense-and-respond system, which senses the mesoderm marker Brachyury (TBXT) and enforces user-defined TF levels in response to it. We expect that this process, by being autonomous as opposed to manual and by enforcing optimal TF trajectories, will result in a more efficient, repeatable, and robust hiPSCs to dHECs conversion protocol, thereby helping fill the gap to clinical translation. Although in this project we tailor the genetic circuit designs to controlling SLGE TFs after sensing mesoderm-specific transcripts, the designs can be readily modified to express different TFs in response to any other cell type- or state-specific transcript. Therefore, we believe that the synthetic biology technology that we will establish will have broad impact on any other cell fate programming as well as any cell-or gene-therapy projects where expression levels and timing, as well as resistance to silencing, are important.

NSF Awards · FY 2024 · 2024-08

This award will support and extend ongoing research on the dynamics of binary systems in general relativity, and into characterizing the gravitational waves that such binaries produce. The goal of this project is to improve our understanding of how black holes in binary systems interact with one another, and how their interactions affect the gravitational waves they produce. This work will thus provide a foundation for further observational probes of the dynamics of black holes and ultra-strong gravity. This award will also support “sonifications” of gravitational wave signals and visualizations of the sources that produce these waves, using audio to illustrate how gravitational waves encode source information, as well as online tools for studying the behavior of orbits of spinning black holes. The core work supported by this award will use black hole perturbation theory (BHPT), an approach that works well modeling binaries in the small mass ratio limit when a binary can be regarded as a small object orbiting a large black hole. One planned project will continue investigating the endpoint of binary black hole coalescence in this limit. Past work studying this problem for circular orbit geometries significantly clarified how the spectrum of final mode excitation depends on coalescence geometry. The team's current work examines the role eccentricity plays in this process, examining how well a system’s eccentricity as it enters the final merger can be discerned from its late gravitational-wave spectrum. Other projects include continuing investigations of gravitational waves produced by spinning bodies orbiting black holes, the continuation of work to develop surrogate binary black hole models based on the small mass-ratio limit, as well as continuing ongoing development of open-source tools for producing fast waveforms in the small mass-ratio limit. These projects grew from work that constituted major portions of the Ph.D. theses of graduate students supported by a prior NSF award. 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 · 2024-08

Abstract Vocalization is used in many species for social and emotional communications. Interestingly, mice can emit two types of vocalizations: ultrasonic vocalizations during social and courtship interactions (USVs) and audible squeaks in response to stress and pain. While previous studies, including our owns, have uncovered higher centers regulating vocalization, such as the gating of USV production by neurons in the periaqueductal gray (PAG), the precise neuronal cell types and the mechanisms for producing the actual sounds via vocal cord adduction and vocal-breathing coordination remain poorly understood. The circuit elements that selectively required for eliciting audible squeaks in mice also remain unknown. The objective of this research proposal is to solve these two fundamental questions of vocal control. In preliminary studies, we have used activity-dependent method that identified excitatory vocal premotor neurons located in the retroambiguus nucleus (RAmVOC) as sufficient for driving vocal-cord closure and elicit USVs. RAmVOC neurons are also absolutely necessary for both USVs and squeaks Here, we will (1) transcriptomically define the cell types of all laryngeal premotor (pre-MNlary) and as well as all RAmVOC presynaptic (pre- RAmVOC) neurons; (2) determine the activity and function of preBotCInh – RAmVOC pathways in vocal- respiratory coupling; and (3) determine the roles of different populations of periaqueductal gray (PAG), Kölliker-Fuse (KF), nucleus solitary tract (NTS) neurons in driving audible emotional vocalizations. We expect to not only identify the hitherto unknown circuits that trigger emotional cries but also reveal precise cells and mechanisms underlying vocal-respiration coordination and lay molecular foundations for future dissection of other aspects of vocal control (pitch and shape of syllables), for finding potential treatment targets for dysphonia and other disorders related to motor speech productions, as well as for revealing how other physiological needs control vocal cord movements in health and diseases.

NSF Awards · FY 2024 · 2024-08

During the ice ages of the past few million years, ice sheets repeatedly grew southward from the Arctic. The ice sheet in North America advanced as far south as New York City during the most recent ice age, but little is known about its size before then. This project will determine if the ice sheet also reached New York during each of the past five ice ages using cave formations (speleothems) in the area. The findings will show how consistently (or not) ice sheets respond to drivers of climate change and improve estimates of how high sea levels rose during warm periods between ice ages. Given that ongoing ice sheet melt is projected to accelerate in our warming world, the real-world data generated in this research will provide timely information on how ice sheets and climate are linked. The most compelling findings will be delivered to the public through tours at a popular show cave and shared with the caving community through National Speleological Society print and online media. The scientists will participate in the NSS’s “Request a Speleoguest” program linking K-12 educators with cave experts, as well as develop an educational module, including caving trips, for a New York City charter school that serves primarily low-income students. This project will apply a well-known but overlooked approach to constraining the Laurentide Ice Sheet’s (LIS) southern limit during glacial maxima of the past 500 kyr: developing a speleothem growth chronology within the ice sheet’s footprint. 150 uranium-thorium ages will be measured on a large collection of speleothems from 17 caves in east-central New York State, only 100-200 km inboard of its Last Glacial Maximum (LGM) margin. This chronology can provide a binary proxy of ice cover through time – speleothem growth typically occurs when ice-free conditions permit liquid water charged with soil CO2 to percolate into a well-ventilated cave, whereas growth usually halts when an area is glacier covered. Stable isotope profiles will also be measured along speleothems to develop a long climate reconstruction. The results will help address four longstanding problems in paleoclimate. (1) Did the LIS advance to near its LGM extent every glaciation? (2) How are interglacial sea level markers affected by the size and timing of prior ice loading? (3) Were abrupt glacial climate events related to rerouting of freshwater runoff by an oscillating LIS margin in eastern North America? (4) How were orbital and millennial-scale climate change expressed in the mid latitudes, near the LIS and upwind of the North Atlantic? 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

Cardiovascular disease is the leading cause of morbidity and mortality worldwide and is responsible for 20% of deaths in the United States alone. Among both disabling and fatal events, cardiac arrhythmias play a central role. In this context, electrophysiological studies of cardiac models of arrhythmias in vitro are essential to provide a fundamental understanding of the underlying mechanisms and develop novel therapeutics. In particular, monitoring cardiomyocyte action potential propagation at the cell network level with subcellular resolution and over extended periods is of fundamental importance to comprehend the role of gap junction distribution and sodium channel clustering, at the microscopic scale, in the activation wavefront propagation in cardiac tissue. Alas, current electrophysiological techniques suffer from severe technical limitations which prevent performing such experiments and, therefore, hinder progress in cardiovascular research. In this proposal, we introduce the concept of multi-electroplasmonic nanoantenna arrays (MENAs) to enable wireless transmembrane-level electrophysiology in networks of cells with subcellular resolution and over extended periods. MENAs are composed of protruding nanomushroom-like electroplasmonic nanoantennas designed to strengthen coupling at the cell- nanoantenna interface and provide large seal resistances with the cells cultured atop. Under such conditions, local electroporation provides direct intracellular access to the structures and permits each nanoantenna to scatter light with an intensity proportional to the cell transmembrane potential. Compared to the patch clamp technique, MENAs are not limited to a single cell at a time but permit the study of cellular networks with up to a million recording sites simultaneously. Due to their wireless nature, MENAs do not need conductive traces or integrated amplifiers, which limit the lateral resolution of traditional multielectrode arrays to ~20 µm and, consequently, can achieve sub-micrometer resolution. Furthermore, MENAs do not suffer from photobleaching and permit long-term stability much greater than fluorescence-based reporters, such as voltage-sensitive dyes (up to several days versus a few minutes, respectively). Compared to recent electro-optic approaches limited to single-site extracellular recordings, MENAs provide intracellular access and permit multi-site transmembrane-level electrophysiology. The following three aims will be achieved to demonstrate the concept of MENAs: Aim 1 – Nanofabrication and electro-optic characterization of MENAs: A scalable nanofabrication process will be developed to manufacture MENAs. Electro-optic performances of the resulting nanotransducers will be characterized. Aim 2 – Characterization of MENA electrophysiological recording properties with cardiomyocyte monolayers: MENAs recordings will be studied in terms of amplitude, duration, and yield and validated with patch clamp experiments. Aim 3 – Proof-of-concept study of the activation wavefront propagation in spatially organized cardiomyocyte cultures modeling cardiac arrhythmia: MENA electrophysiological capabilities will be illustrated on arrhythmia models. The proposed technology is a breakthrough innovation that will prospectively revolutionize the understanding of cardiovascular systems by enabling fundamental and pharmacological studies at an unprecedented level. In the long term, MENAs will contribute to relieving the growing social and economic burden of cardiovascular diseases on society

NSF Awards · FY 2024 · 2024-08

With the support of the Macromolecular, Supramolecular and Nanochemistry program in the Division of Chemistry, Professor Robert J. Gilliard of the Massachusetts Institute of Technology is developing novel synthetic protocols for the design and synthesis of boron-doped nanographenes. Graphene is an allotrope of carbon consisting of a single sheet of atoms arranged in a hexagonal lattice nanostructure resembling that of a honeycomb. Graphene can become conductive when external voltage is applied (like in transistors) or light shines on them (like in photovoltaic cells). As such, it is a very useful material for potential use in solar cells, LED screens and other applications that utilize the conversion of electricity to light and vice versa. In this project, smaller aromatic compounds containing the chemical element boron will first be designed and synthesized. These compounds will then be utilized as templates for the preparation of boron-doped nanographenes. A strong emphasis will be placed on the characterization and studies of supramolecular assemblies in these unique materials. The research efforts will provide valuable training to undergraduate and graduate students, particularly underrepresented minorities and first-generation students. As a part of the broader impact component, Professor Gilliard and his team will build ChemRise program at the Massachusetts Institute of Technology, a program that will seek to recruit, retain, and provide professional development opportunities to scholars from groups that are traditionally underrepresented in chemistry. This project will focus on the design, synthesis, and characterization of boron-doped nanographenes, providing advances in knowledge concerning the strategic placement of electron-deficient dopants in complex pi-extended conjugated networks. The overall strategy centers on two research thrusts: (1) using boron-doped acenes as templates for the synthesis of boron-doped nanographenes and 2) the employment of coordination chemistry, self-assembly, and surface chemistry to control the properties of boron-doped nanographenes. Synthetic methodology will focus on using electron-rich moieties to promote the Scholl reaction or other oxidative cyclodehydrogenation methods in boron-containing polyaromatic hydrocarbon precursors. This research has the potential to provide valuable information for the development of the next generation of boron-doped heterocycles with applications in spintronics and optoelectronics. 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 POSE Phase II project will establish an open-source ecosystem (OSE) for OpenCilk, called Fastcode. OpenCilk is a state-of-the-art, completely open, task-parallel programming platform. Fastcode will encompass research and education in software performance engineering: making software run fast or otherwise consume few resources, such as time, storage, energy, network bandwidth, etc. The FastCode OSE for OpenCilk will lead to a significant increase in computer science and engineering graduates with experience in software performance engineering (SPE). Expanding the SPE workforce will, in turn, lead to significant efficiency gains in academic research, industry, and government across many domains. Fastcode will provide conduits for research in parallel programming and software performance engineering to find its way to application developers, impacting various domains of society, including business, science, technology, etc. By providing software infrastructure to support novel research and teaching in software performance engineering, Fastcode will enable the much larger community of applications researchers to leverage the performance of modern computers. The benefits of computing, including faster and better scientific discoveries, will be magnified, and the environmental impact of computing will be mitigated due to reduced energy consumption (if you can compute something in half the time with the same resources, it saves about half the energy). For application developers to cope with the end of Moore's Law, they must embrace software performance engineering (SPE) and all its constituent technologies: parallel programming, vectorization, caching, algorithms, compiler optimization, etc. The OpenCilk task-parallel platform simplifies parallel programming, arguably the most difficult of these technologies and the one with the greatest potential. But without a more general knowledge of SPE, programmers cannot effectively exploit the full capabilities of modern multicore computers. The Fastcode OSE for OpenCilk will enable researchers to advance their understanding of SPE and parallel programming, providing the next generation of researchers and software developers with principled and scientific foundations for obtaining application performance in the post-Moore era. 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

Emerging quantum technologies require us to control large many-body systems and to maintain their quantum coherence during the dynamics. This would enable achieving quantum advantage in computation and simulation, opening the possibility to tackle complex computational problems of broad practical importance that are out of reach today. Despite rapid progress, there are still limits to the size and coherence time of quantum systems. A fundamental question with broad practical implications is then how to find robust control protocols that can protect the quantum systems, preserving its coherence and entanglement that enable quantum advantage, while also implementing desired dynamics for quantum computation and simulation. Understanding these limits also requires a precise characterization of many-body quantum systems, which is itself a challenging task. This project aims to tackle these two intertwined challenges by developing and studying experimentally novel control and learning protocols. This team will exploit Floquet Hamiltonian engineering to develop novel protocols both analytically and numerically and introduce robust error correction in Hamiltonian engineering. They will further exploit their large-scale, solid-state nuclear spin platforms to experimentally assess the methods and the quantum simulation performance, beyond the regime where classical numerical computation can validate quantum simulations. To evaluate both the control performance and the engineered Hamiltonian, the team will devise experimentally accessible metrics that can characterize the many-body dynamics and the properties of out-of-equilibrium many-body quantum states. In particular, the team will combine novel ideas in quantum system learning with the team's recently demonstrated single-spin correlation measurements and out-of-time ordered commutators to efficiently extract information from the quantum many-body system, even in the presence of a limited number of observables. The results are expected provide novel insight into thermalization and information scrambling – or their absence due to localization or prethermalization, a key question in the quest to exploit many-body systems for quantum applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Van der Waals (2D) materials are a family of two-dimensional crystals with diverse physical properties. These ultra-clean materials can be assembled in any desired order to create devices that exhibit various functionalities. Among them, 2D superconductor systems possess significant potential to enhance our understanding of superconductivity at a fundamental level and to develop new types of superconducting quantum devices for quantum technologies. This project aims to study the fundamental characteristics of Van der Waals (2D) superconductors relevant to quantum information science and technology. To explore Van der Waals materials the team will build hybrid superconducting circuits incorporating these 2D superconductors and leverage our team's expertise in circuit quantum electrodynamics (cQED), quantum transport, and time-domain operation of quantum systems to enable this research. The team will explore the following directions: 1.) Kinetic inductance and pairing symmetries in moiré superconductors; 2.) Quasiparticle dynamics in 2D superconductors; 3.) Quasiparticle blocking using gap-tunable 2D superconductors; 4.) Microwave spectroscopy of Andreev levels and quasiparticle trapping characterization in graphene Josephson junctions. The proposed projects are strategically located at the intersection of condensed matter physics, 2D materials, and quantum information sciences. The results from the proposed works are expected to advance our knowledge of:; 1.) Superconductivity in interacting, highly-correlated systems, potentially leading to a better understanding of high-TC superconductors, such as the cuprates; 2.) Superconductivity in low-dimensional systems; 3.) Quasiparticle population and dynamics in crystalline 2D superconductors; 4.) Decoherence mechanisms and mitigation of quasiparticle poisoning of superconducting qubits; 5.) Interplay between mesoscopic superconductivity, Andreev physics, and coherence of superconducting qubits. 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

IsoDAR will be the first high-intensity, proton-driven source to operate underground next to a multi-kiloton-scale detector. The experiment will run at Yemilab in South Korea, where it has received preliminary approval and caverns have been constructed. The novel source will produce unprecedented levels of neutrinos, neutrons, and photons for precision physics measurements of inverse beta decay, antineutrino elastic scattering and searches for rare particle searches. The heart of both IsoDAR which will delivers x10 more current than commercial machines. This project focuses on final steps in R&D for the cyclotron. Furthermore, with respect to broader impacts, the IsoDAR design makes feasible acceleration of deuterons, which is applicable for medical isotope production, especially Ac-225 that is in short supply and fusion materials testing. This grant supports students and a postdoc to participate in studies of a full-scale central region prototype of the cyclotron. Four projects will be pursued: 1) development of the beam diagnostics, training students on instrumentation; 2) establishment of machine-learning-feedback for tuning, training students in cutting edge coding; 3) establishing a new physics case from using beam timing, training students in analysis and phenomenology; and 4) modification to produce a D+ design, training the postdoc for a career that will include future developments in isotope production. Original work by the team that invented the IsoDAR cyclotron provides a firm foundation for this plan. This proposal maximizes the use of existing state-of-the-art equipment, most of which has been funded by previous NSF grants. 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 Standard Model of particle physics describes all known fundamental forces except gravity. It has proven capable of describing physical phenomenon to remarkable precision. However, it fails to describe all aspects of our universe, indicating there must be other forces beyond those that we currently know about. The LHCb experiment is one of the four main experiments at the Large Hadron Collider (LHC), CERN. It specializes in detecting differences between matter and anti-matter, using a heavy type of quark called a beauty quark. Billions of quarks are produced every second in collisions at LHC, which in turn decay to other particles, producing a so-called event in our detector. These events result in a data rate of around 40 Tb/s under current data-taking conditions. Not all this data can be saved, and the rate must be reduced in real-time by more than a factor ten. We call the algorithms used in real-time selection of events the "trigger". The LHCb Upgrade II experiment will run in the 2030s and will deal with a data rate of 200 Tb/s, the largest at the LHC. This rate has to be reduced by four orders of magnitude before being written out. To achieve this, the electronics present on or near the sub-detectors, called Field Programmable Gate Arrays (FPGAs), must be leveraged. Ideally machine learning (ML) algorithms would be employed on the FPGAs as these algorithms give improved performance. This is currently difficult for the LHCb experiment as the community-standard package that interfaces trained ML models to FPGA backends, hls4ml, has limited functionality for the two most prolific FPGA types used in LHCb. This project extends the functionality of hls4ml, significantly expanding the user-base. It also develops the first machine-learning based lossy compression algorithms to run on the electronics infrastructure within LHCb, with a speed on the order of tens of nano seconds, along with other algorithms to run in the LHCb Upgrade II trigger on both FPGAs and GPU architectures. The LHCb Upgrade II experiment will run at the Large Hadron Collider at CERN from 2033-2041 and will supersede the current LHCb Upgrade I (2022-2029). The current LHCb trigger system is unique in particle physics in that the full 40Tb/s of data produced by the experiment is fed into software, where it is processed using a GPU-based architecture which reduces the rate using a real-time event filter (trigger). The data rate of the LHCb Upgrade II experiment will be five times higher than the current rate, at 200 Tb/s. The data rate must be reduced by around four orders of magnitude before being sent to storage, and the large rates mean that data reduction must occur even before the computing farm. Without sophisticated trigger algorithms before the computing farm, many of the detector read outs will simply be thrown away, resulting in a significant loss of physics information. In recent years there has been rapid progress in fast machine learning (ML) algorithms run on FPGA accelerators present in the electronics that sit on or close to the detector readout. Despite the obvious relevance of using ML algorithms on FPGAs to overcome LHCb's 200 Tb/s problem, the collaboration has been slow to profit from this progress. This is in part because the community-standard package that interfaces trained ML models to FPGA backends, hls4ml, has limited functionality for the two most prolific FPGA types used in LHCb, Intel and Microchip. One of the sub-projects on this proposal will extend the scope of hls4ml to include these backends, significantly expanding the user-base of hls4ml. This project will then develop the first low-latency ML-based lossy compression algorithms to run on the electronics infrastructure within LHCb. This will allow the collaboration to assess the validity of fast ML on FPGAs as a solution to the 200 Tb/s problem. This project will also build on the work of the previous CSSI grant, OAC-2004645, and develop and implement reconstruction algorithms to run in the Upgrade II trigger which are tested and developed for both GPU and FPGA backends, allowing LHCb to have the flexibility to choose whichever architecture offers the most cost-effective solution in 2033. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Physics in the Mathematics and Physical Sciences 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 · 2024-08

This project lies at the intersection of probability theory, combinatorics, and mathematical physics. Its primary objective is to uncover novel connections between two currently active research domains that have developed independently until recently: random permutations and random geometry. The emerging interplay between permutons (limits of random permutations) and random geometric objects arising in quantum physics and statistical mechanics (such as Schramm–Loewner evolution curves and Liouville quantum gravity surfaces) will play a fundamental role in generating significant advancements in both fields. This will involve formulating novel theories for universal random permutons and random directed metrics, expanding existing ones, and effectively resolving long-standing problems on meanders and meandric systems. The three main objectives of this research project are, first, to investigate the problem of the longest increasing subsequence of random permutations from a novel angle, which involves linking it to directed metrics in planar maps. The goal is to construct a 'quantum version' of the universal Kardar-Parisi-Zhang geometry, i.e., the directed landscape. Second, to study the geometry of random meanders and broader statistical physics models involving crossing fully packed loops on planar maps. The objective is to tackle the long-standing open problem of determining the scaling limit of random uniform meanders and meandric permutations. Third to establish connections between the limits of d-dimensional permutations and new scale-invariant d-dimensional random geometries introduced in the physical literature. The aim is to begin developing a novel d-dimensional theory of random geometries and permutons. The project offers opportunities for education and outreach to high school and undergraduate students, as well as mentoring of undergraduate and Ph.D. 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 project aims to use randomized algorithms to address the fundamental combinatorial problem of constructing interesting objects, counting such objects, and studying their (typical) structure. Beginning in the mid-twentieth century, the use of randomness and the probabilistic method revolutionized the combinatorialist's toolbox. More recently, the application of randomized processes has facilitated many breakthroughs and landmark results. The study and use of these processes has deep connections to functional analysis, entropy theory, and discrete probability. By nature, the use of these processes also opens the door to computer experimentation. As such, many aspects of this project are suitable for collaboration with students of all levels. One focus of the project is the study of graphs and hypergraphs where, in the standard random models, different constraints compete at the same scale. For instance, consider Latin squares, that is, n x n matrices in which every row and column is a permutation of the symbols {1,2,...,n}. It is straightforward to verify that if one chooses each symbol independently and uniformly at random then in each row and column, a constant fraction of the symbols will be repeated. For this reason (and others), probabilistic constructions of Latin squares proved challenging. Nevertheless, recent advances allowed the use of sophisticated randomized algorithms in constructing Latin squares, which are but one example of the rich family of combinatorial designs. Despite this progress, many mysteries remain regarding even the most basic properties of random Latin squares (and related objects). For example, how many 2x2 Latin subsquares does a typical random order-n Latin square have? A second focus of the project is the study of threshold phenomena in random hypergraphs. Of particular interest is characterizing at which densities random binomial graphs contain certain spanning structures, such as combinatorial designs. A recently proved connection between "fractional expectation-thresholds" and thresholds introduces an exciting avenue for using randomized constructions to prove threshold results. This project will develop and extend techniques based on randomized processes to answer these constructive, enumerative, and structural questions. 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

Neutrino physics continues to provide a wealth of opportunities for discovery and a deeper understanding of the underpinnings of the Standard Model of particle physics. This work tackles two fundamental questions related to neutrino physics: the neutrino mass and how neutrinos coherently interact with matter. The answers to both these questions remain incomplete despite almost seventy years of the neutrino being discovered. To this day, the absolute mass scale remains experimentally unknown. A discovery of the neutrino mass scale would profoundly impact the field of nuclear physics. Likewise, how neutrinos interact coherently with matter at low momentum transfers remains unexplored and could reveal new clues about physics beyond the Standard Model. The Neutrino and Dark Matter Group at MIT strives to explore the answers to both of these questions using high-precision techniques aimed at measuring the fundamental properties of neutrinos. For measuring the neutrino mass scale, we will continue to lead the Project 8 neutrino experiment, which uses the technique of cyclotron radiation emission spectroscopy, or CRES, to measure the endpoint spectrum of tritium beta decay. Project 8 aims to construct a next-generation tritium beta decay experiment with a final neutrino mass sensitivity of 40 meV/c2, and over the next few years, will construct the next prototype experiment (Phase III) to demonstrate the scalability of the technique. For the second question, the Neutrino and Dark Matter group will spearhead the Ricochet neutrino experiment. Ricochet uses a combination of Germanium and metallic superconductors to measure coherent elastic neutrino-nucleus scattering (CEvNS) from neutrinos created by fission. The use of metallic superconducting bolometers for recoil detection also has a broad reach, having potential applications in nuclear reactor monitoring and direct dark matter detection. Both of these experiments (Ricochet and Project 8) utilize high-precision techniques that operate near the quantum limit of sensitivity. Therefore, a portion of this research will also be devoted to developing high-temperature traveling wave parametric amplifiers, which we plan to use for neutrino physics and other future cryogenic sensor arrays. 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 proposal addresses two of the most fundamental questions in nuclear and particle physics: the nature of dark matter and the origin of the universe's matter-antimatter asymmetry - why does it appear the universe is matter dominated? Axions are a well-motivated candidate for dark matter, while possible Majorana neutrinos are potential sources of matter-antimatter asymmetry. The project will conduct specialized experiments using precision measurement techniques to search for axion dark matter through the DMRadio program and for Majorana neutrinos through the CUPID neutrinoless double-beta decay program. The technical goal is to develop robust quantum-enhanced superconducting readout systems for both initiatives. Additionally, the PI and her team will leverage data from these experiments to create advanced algorithms, including AI and machine learning techniques, to reduce noise. For the DMRadio program, the project will develop real-time SQUID optimization software for frequency scans. For the CUPID program, the project will create a robust multiplexed readout system for TES sensors based on technology developed by NIST. These projects provide critical training for the quantum workforce. The inclusion of undergraduate researchers in all aspects of the project is a key broader impact of the work. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-08

PROJECT ABSTRACT Enzymes catalyze selective small-molecule transformations with activity and selectivity that is seldom matched by non-biological catalysts. Metalloenzymes, such as non-heme iron enzymes (NHIEs), catalyze a diverse array of reactions involving C–H activation that are relevant to natural product biosynthesis and would be of great benefit if harnessed for medicinal chemistry. Understanding the role of the secondary sphere in catalysis is essential for describing how these enzymes work and for designing mimic catalysts capable of similar transformations under a wider range of conditions (i.e., pH and temperature) amenable to industrial catalysis. Computational modeling provides insight into the sources of enzymatic rate enhancement, the dynamics of substrates in the active site, and in the mechanism of biomimetic catalysts, all of which are challenging to determine experimentally. However, for difficult cases such as NHIEs, existing modeling techniques provide limited mechanistic insight because they are either too costly, too inaccurate, or require too much existing knowledge and user intervention. The overall vision for the PI's research program is to develop systematic methods and novel workflows to overcome cost–accuracy trade-offs in computational modeling for the discovery of new catalysts and understanding of enzymes. The PI has advanced the first machine learning (ML) models to discover new transition metal catalysts from millions of candidates, identifying opportunities to overcome trade- offs in catalyst performance. She has developed novel strategies for unveiling noncovalent interactions in NHIEs that determine their reaction selectivity and validated her predictions with experimental collaborators. The PI has advanced methods for making QM/MM systematic and robust and applied them to identifying contributions of rate enhancement in enzymes to determine where biomimetic counterparts fail. She has developed ML models to identify and avoid errors in first-principles modeling. The central hypothesis of the proposed research is that development of novel low-cost methods that enable the generation of larger datasets will reveal structure– property relationships in enzymatic and biomimetic C–H activation. The rationale is that dynamic effects and interactions with second-sphere residues that distinguish enzymes that catalyze different reactions cannot be understood without sufficient sampling and a broad comparison of behavior across the enzyme family. Over the next five years, the PI will 1) develop models for the prediction of regioselectivity in non-heme iron enzymes using neural network potentials to enable sampling, 2) systematically determine environmental contributions to catalysis, and 3) discover bioinspired homogeneous catalysts. The proposed research will produce a framework for predictive modeling in biological and bioinspired catalysis. The goals build upon methods the PI's lab has developed for modeling enzymes and non-biological systems and the recent results they have produced related to ML-accelerated catalyst screening and non-heme iron enzyme modeling.

NIH Research Projects · FY 2026 · 2024-07

Top-down regulation of cortical processing is critical for learning, attention, figure-ground separation, multisensory integration, contextual-modulation and many other processes associated with cognition. However, the biological mechanisms supporting top-down computation remain elusive. A popular and compelling hypothesis is that top-down computation is implemented via the engagement of apical tuft dendrites in layer 1. This hypothesis is indirectly supported by convergent anatomical and functional evidence, including observations in patients with disorders associated with disrupted top-down processing, but more direct evidence linking dendritic integration to top-down mechanisms is limited. To address this gap, we developed a novel imaging approach that allows simultaneous recording of somas and dendrites in large populations of neurons during learning, without signal contamination. We will combine this approach with two complementary behavioral tasks: a highly controlled Brain-Computer Interface (BCI) paradigm and a comparatively naturalistic virtual navigation task. Through the set of proposed experiments, we aim to test the overarching hypothesis that top-down computation is implemented via the engagement of apical tuft dendrites in layer 1, and that these signals are responsible for guiding learning in networks of neurons. We will do this by: (1) Establishing the relationship between single-neuron dendritic integration and circuit dynamics and studying the principles governing the changes in this relationship over the course of learning; (2) Interrogating the relationship between dendritic activity and behavioral variables, how this relationship is modified over the course of learning, and how dendrites instruct changes in their corresponding somas; and (3) Testing the hypothesis that dendrites receive vectorized error signals consistent with an efficient solution to the credit assignment problem. Results from these experiments will catalyze a new ways of thinking about cortical computation and learning principles in biological systems, propelling the field into new directions with impactful scientific and translational potential.

NSF Awards · FY 2024 · 2024-07

This award supports a study of how the nuclei of chemical elements are formed in astrophysical environments by using high energy lasers in a laboratory. Nuclear reactions play key roles in the dynamics and evolution of our universe. They are responsible for forming the basic elements that make up everything we see around us – including the simplest hydrogen atoms, the oxygen we breathe, metals such as iron and heavy elements such as uranium - through nucleosynthesis processes in stars and during the Big Bang. However, nuclear reactions cannot be measured directly in space. To solve outstanding questions about abundances of elements in the universe, we have to know how these reactions happen under different conditions. Historically, experiments to test the likelihood of different reactions have been done using accelerators. However, accelerators use solid or gaseous targets. In stars, matter is found in a plasma state, where the atoms are split into their charged constituents, ions and electrons. Scientists have reasons to believe that reaction probabilities will be different in plasmas compared to solid or gaseous targets. The present work uses large lasers, such as the National Ignition Facility (NIF), to create plasmas where the reactions can be studied in an environment comparable to that in stars. The project will support an international collaboration with Imperial College London and Lawrence Livermore National Laboratory (LLNL), training of graduate students and a postdoc, and may advance the development of fusion energy and contribute to national security. This effort is expected to improve the understanding of how elements are formed in the universe by answering questions about plasma effects on nuclear reaction probabilities that have never before been addressed experimentally. In doing so, it will contribute to the goals of NSF's "Windows on the Universe: The Era of Multi-Messenger Astrophysics" meta-program. The project focuses on the use of high-energy-density (HED) plasmas for basic nuclear science experiments relevant to nuclear astrophysics. High energy laser facilities such as OMEGA at the University of Rochester and the NIF at LLNL can create the stellar-like HED experimental conditions in a laboratory setting. Unlike the more traditional accelerator-based approached, the laser-driven HED conditions more closely mimic astrophysical environments in several ways, including thermal distributions of the reacting ions as opposed to monoenergetic ion beams; stellar-relevant plasma temperatures and densities; and, in the case of NIF, neutron flux densities not found anywhere else on earth. This represents a unique opportunity for understanding plasma effects on nuclear reactions. Fully exploiting this platform for nucleosynthesis experiments is anticipated to be a broad, decades-long effort. The goal of the current effort is to advance the frontier through new experimental projects. In particular, four areas will be addressed: (1) using new diagnostic capabilities to uniquely study six-nucleon systems with three particles in the final state, such as those relevant to the solar proton-proton cycles; (2) development of a platform for studying reactions involving mid-Z ions relevant to the stellar carbon-nitrogen-oxygen (CNO) catalyzed proton-burning cycles; (3) making strides toward the first terrestrial measurements of plasma screening of nuclear reactions; and (4) development of a platform for use of high-performing NIF implosions to study neutron capture on nuclei in excited states. 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 2024 Talbot Workshop, titled “Topological Cyclic Homology of Ring Spectra,” will be held at Tall Timbers Retreat in Nacogdoches, TX, from August 11-17th, 2024. The Talbot Workshop is an annual one-week academic retreat, providing an immersive opportunity for early career participants to become acquainted with current research in algebraic topology and related fields. It follows a model that has been developed and refined over many years. Each year, participants gather to study a topic of current research interest under the guidance of two senior experts. The mentors for the 2024 Talbot Workshop will be Jeremy Hahn (MIT) and Allen Yuan (Northwestern University), who both contributed essentially to the recent spectacular resolution of the fifty-year-old “Telescope conjecture” of Doug Ravenel. This exciting development, reported on in Quanta Magazine, connects two long-standing areas of mathematical research of wide international interest -- algebraic K-theory and homotopy theory -- in a completely new and revolutionary way. The focus on a single research topic, the collective nature — mentors and participants sharing meals, housing, and activities for a week — and graduate student organization make the Talbot Workshops unique among mathematics events. Topological cyclic homology is a rapidly developing subject sitting between homotopy theory and (via the work of Bhatt–Morrow–Scholze) p-adic arithmetic geometry. There are now many excellent introductions to topological cyclic homology that focus on discrete commutative rings and spherical group rings. We aim to give a computationally focused introduction to the topological cyclic homology of finite height ring spectra. The topic is also closely connected to (MU-based) synthetic spectra, and may help familiarize students with their use in computations. The workshop will proceed by discussing THH and its concomitant structures: the motivic filtration, the circle action, and the Frobenius map. We will follow the discussion of each structure with computations in the basic examples of THH of finite fields, the integers, and ku, with the ultimate goal of understanding syntomic cohomology and Lichtenbaum–Quillen theorems. We will conclude with connections to the algebraic K-theory of ring spectra, chromatic redshift, the telescope conjecture, and prismatization. Conference website: https://sites.google.com/view/talbotworkshop/current-talbot 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 Partnerships for Innovation - Technology Translation (PFI-TT) project is the development of a high-energy, long-duration primary (non-rechargeable) battery with higher storage capacity than other currently available batteries. Lithium (Li) primary batteries have the highest energy of all batteries, making them indispensable for applications where recharge is nonessential, but a premium is placed on long lifetime and reliability. Lead leading examples of the use of lithium primary batteries including medical implants, defense, and Internet-of Things (IoT) sensors. In those applications, the lifetime of the device is constrained by battery life, and there is high value associated with increasing the gravimetric/volumetric energy to enable new functions, longer-duration operation, and/or smaller/lighter devices for the same delivered energy. Unfortunately, the leading primary battery chemistries were developed several decades ago and have since matured with little/no new competitors entering the market. This team has developed a new primary battery chemistry that could boost the energy density of the current market-leading system by 50%, with good safety characteristics, and little or no increase in cost. The technology will address the unmet needs in the aforementioned industries and enable societal impacts such as improved patient quality of life, enhanced military mission flexibility, and new energy-intensive IoT use cases. The project is based on the development of a class of energy-dense, safe, and cost-effective catholyte (electrochemically active electrolyte) based on newly developed liquid fluorinated reactants (LFRs). Due to the excellent voltage alignment of LFR with a leading solid cathode material, the catholyte can be readily integrated into the current market-leading primary battery technology and replace the typically inactive electrolyte, substantially reducing the inactive weight of the cell. Such integration yields a hybrid solid-liquid cell design that significantly boosts gravimetric/volumetric energy by 20–30% (to date). Efforts so far have demonstrated significant performance improvements compared to the incumbent technology at low discharge rates over a wide temperature range (25–50 °C), but performances decline under high-rate conditions, especially approaching ambient temperature conditions. To address this hurdle, the objectives of this project are to: (1) advance cathode architecture design and anode stabilization strategies to overcome current performance limitations related to rate and operating temperature; and (2) improve cell design to demonstrate similar gains in practical-scale, high-capacity, pouch cells. This project will also conduct extensive cathode/anode modifications and cell-level structure design; iterative cell testing and co-optimization of cathode/catholyte; and prototype development and testing catering to energy/power needs in target markets. 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

Automatic Differentiation has become an important enabling technology for scientific computing and machine learning. Simply put, machine learning is a parameter fitting problem, and the computation of derivatives enables the fitting of parameters. Historically, programmers were required to spend significant time and effort developing these derivative codes by hand, making the use of machine learning and simulation on existing applications a tedious and difficult task. In recent years tools have been developed to generate code to compute these properties. However, they have been limited to specialized domains and specific programming languages. In contrast, the Enzyme project aims to generate derivatives of arbitrary programs in any LLVM-based language (e.g. C/C++, Fortran, Julia, Rust, Swift, Python, MLIR, etc), without restriction on scientific domain. The project's impacts are that scientists and engineers in all fields will be able to apply modern algorithms like neural networks to their domains without extensive rewriting of their entire application. This project will both develop the existing research prototype Enzyme implementation into a production-quality ecosystem, and establish an open-source community that will allow Enzyme to be maintained by the open-source community. This involves extensive user documentation and examples, documentation for new and existing Enzyme developers, integration into vendor compilers, organizing an annual Enzyme conference and weekly developer meetings, providing tutorials for Enzyme at relevant meetings, creating an Enzyme advisory board, and coordinating community satisfactions as well as development priorities. 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 abstract The discovery of two-dimensional (2D) magnets opens the door to the exploration of magnetic behavior in these ultra-thin materials. However, it is a challenge to study these thin materials with tools designed for macroscopically large samples, making it hard to create accurate models of their magnetic properties. To tackle this challenge, the research team uses special X-ray and optical techniques that are sensitive to very thin samples, along with theoretical models to understand the properties of 2D magnets. The PI and team aim to focus on two main areas: (1) studying the nanoscale magnetic patterns in 2D magnets; and (2) exploring the low-energy dynamics of 2D magnets. The research work aims to develop electronic and magnetic devices for applications in low-power storage and computing, which are important for creating energy-efficient big data platforms in the age of artificial intelligence. The project includes a program to give research opportunities to college students from schools with fewer science resources, helping to reduce academic inequality and bring more diversity to STEM graduate programs. Technical abstract The study and characterization of the microscopic spin physics in two-dimensional (2D) magnets is a timely topic at the frontier of materials research. The discovery of atomically thin magnets adds a new building block and creates opportunities to realize novel spin phenomena in 2D quantum nanomaterials. However, the microscopic spin physics of this new class of materials in the ultrathin limit is inaccessible to several probes of bulk materials, hindering the development of quantitative models of emergent 2D magnetism. The research team approaches this important challenge using advanced X-ray and optical spectroscopy and scattering probes together with theoretical modeling of the microscopic interactions and magnetic phase diagram using ab initio (DFT) and Monte Carlo methods. The PI and team seek to pursue two main research directions: (1) the study of ground state magnetic textures in 2D magnets and heterostructures; and (2) the study of low-energy spin dynamics in 2D magnets. These activities are poised to significantly advance our understanding of spin phenomena in low-dimensional systems and serve to guide new applications in nanoengineered systems and devices. Additionally, the project includes a pilot program offering longitudinal research experiences to undergraduates from institutions with limited STEM opportunities, aiming to reduce academic inequality and diversify graduate applicant pools, alongside outreach activities for middle school students focused on materials 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.

NIH Research Projects · FY 2025 · 2024-07

ABSTRACT Sickness behaviors represent a range of behavioral changes that manifest in response to infection or inflammation. These changes include reduced physical activity, diminished social interaction, and altered feeding behavior. It is suggested that these behavioral shifts are integral to the body's immune response against pathogens, serving to conserve energy, deprive pathogens of nutrients, and contain the infection within the host's community. Nevertheless, the mechanisms behind the initiation of sickness behaviors at the level of neurons and neural circuits remain largely unexplored. This research endeavors to fill this knowledge gap by identifying a neural substrate that acts as a point of interaction between the immune and nervous systems. Cytokines, signaling molecules produced by various immune cells, play a pivotal role in mounting the body's immune response against pathogenic threats. A growing body of evidence suggests that cytokines can also function as neuromodulators by acting on receptors expressed on neurons within the central nervous system. IL-1β is one such cytokine that has been strongly associated with the generation of sickness behaviors. In support of this idea, our data has revealed enrichment of its receptor, IL-1R1, within the dorsal raphe nucleus (DRN), a brain region well-known for its involvement in regulating behaviors altered during sickness. These observations suggest that IL-1β might act directly on IL-1R1-expressing neurons of the DRN (IL-1R1DRN) to promote the expression of sickness behaviors. In this application, we will test this hypothesis using a range of molecular, genetic, and systems neuroscience methods. Specifically, in Aim 1, we will comprehensively examine IL-1R1 expression in the DRN and monitor the activity of DRN neurons in response to IL-1β and inflammation. In Aim 2, we will manipulate the activity of IL-1R1DRN neurons to investigate their role in modulating sickness behaviors. We will also explore whether IL-1 receptor expression itself in the DRN is essential for generating sickness behaviors. Finally, in Aim 3, we will identify downstream targets of IL-1R1DRN neurons that mediate sickness behaviors while simultaneously asking whether IL-1R1DRN relies on a single target to induce all sickness behaviors or if it utilizes different targets for generating distinct types of sickness behaviors. In summary, our study will identify IL-1R1DRN and its associated targets as the primary neural circuits influenced by the pro-inflammatory cytokine IL-1β in driving behavioral changes during sickness. Successful completion of this research will offer neural circuit-focused insights into how changes in the immune system under inflammatory conditions can lead to corresponding alterations in behavior.

NSF Awards · FY 2024 · 2024-07

Conventional silicon-based computing and data storage media have largely plateaued in their arc of advancement over the past decades, ushering in the ends of commonly known Moore’s and Kryder’s Laws. Radically new technological approaches are therefore needed to sustain rapid advances in computing and data storage capabilities across domains of science and technology ranging from financial modeling to bioinformatics, drug discovery, and data storage and encryption. The awarded molecular memory and computing framework based on programmable biological deoxyribonucleic (DNA) molecules offers an innovative, scalable approach to overcome fundamental limitations of current state-of-the-art computing and data storage approaches. Individual nanometer-scale DNA templates are investigated to test the limits of their data storage and computing capabilities. Unique spatial positioning of DNA data stores and compute nodes are patterned across large-scale wafers to facilitate rapid read-out, as well as enable interfacing with conventional electronic and optical data storage and computing media. Fundamental questions include the density of data that can be realized within individual DNA objects and their collections on-surface, the fidelity of data storage/read-out and computing that can be realized, and the parallelization of this storage and computing for eventual two-dimensional (2D) device applications to optical computing and data storage. This biological computing platform fabricated using DNA will engage students from disparate fields and disciplines that are typically isolated, including computer science, biological engineering, materials science, and nanotechnology. This project will therefore help train students for the next-generation workforce enabling a transition to the Bioeconomy, with sustainable materials engineering for next-generation computational and data storage devices. Whereas DNA conventionally stores genetic information biologically, it also offers nanoscale patterning and control over materials for alternative data storage and computing approaches. Molecular computing as a field has harnessed this sequence-controlled, information containing property of DNA to perform intricate algorithmic operations and efficient data storage and retrieval. However, these systems generally operate in solution and are largely incompatible with surface-based optical and electronic storage and computing media. In the present project, nanometer-scale DNA origami assemblies are patterned on 2D silicon surfaces to ‘display’ data in a chip-like format. By leveraging the functionality of DNA origami templates, fluorescent barcodes and particles are displayed on the structure to provide optical readout that can be parallelized across large micron-to-millimeter scale substrates. Lithography is used to pattern DNA origami across the substrate with spatial and orientational control, and methods to uniquely identify each origami are explored to enable their use as individual data storage and computing units. Distinct DNA origami template shapes with lithographically patterned matching shapes enable heterogeneous placement on-surface, with parallel imaging offering unique identification of origami objects on the wafer-scale. Nanoparticle placement on origami units offers encoding of unique data stores on each DNA origami template. Incorporation of molecular qubits on these structural DNA origami templates offers optically based computing using quantum gates in a parallel manner that may in principle operate at room temperature. Altogether, transformative approaches to on-surface data storage and computing are explored that offer new strategies to overcome current limitations of conventional data storage and computing media. 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.