SBU-BNL Seed Grants for Collaborative Research

SBU-BNL Seed Grants for Collaborative Research is sponsored by Stony Brook University (SBU) and Brookhaven National Laboratory (BNL). This program supports joint initiatives between scientists from Stony Brook University and Brookhaven National Laboratory to foster collaboration.

Seed Grants | BNL | Stony Brook University Office of Brookhaven National Laboratory Affairs Brookhaven Science Associates Brookhaven National Laboratory (BNL) Brookhaven Science Associates (BSA) United States Department of Energy (DOE) SBU-BNL Seed Grants for Collaborative Research The Office of Brookhaven National Laboratory Affairs sponsors the annual Seed Grant program to support joint initiatives between scientists from Stony Brook University and Brookhaven National Laboratory.

Funding for the program is provided by the Office of the President. An email outlining proposal requirements is sent to the campus community several months in advance of the proposal submission deadline. About the Seed Grant Program: The Seed Grant Program began more than 25 years ago as a mechanism to foster collaboration between the Stony Brook and Brookhaven National Lab science communities.

Scientists from both institutions work in conjunction with colleagues bringing their ideas to life. These collaborations are a key element for developing synergistic activities Please contact the Office of Brookhaven Affairs ( ann. ozelis@stonybrook.

edu ) for more information. Universal Scaling Behavior Originated from Fermi Surface Geometry and Topology in Quantum Altermagnetic Metals SBU, Physics and Astronomy BNL, Condensed Matter Physics and Material Science Division This project aims to establish the mechanisms and universal transport laws of altermagnet that exhibit spin-polarized transport despite zero net magnetization to advance energy-efficient spintronics.

Such universal scaling in metals is governed primarily by Fermi-surface geometry and topology rather than microscopic detail, and we leverage this perspective to classify behaviors across diverse materials. On the theory side, we show how altermagnetism can emerge from a Fermi liquid via a d-wave Pomeranchuk instability that drives spin-resolved quadrupolar distortions without producing net magnetization.

A central objective is to promote this instability by engineering Fermi-surface anisotropy through lattice potential, strain, or pressure to create hotspots, inflection points, and proximity to van Hove singularities.

We treat generic interactions using a functional renormalization-group framework that tracks their flow, yielding realistic routes to stabilize altermagnetism and concrete predictions for dynamical susceptibilities and real-space spin textures.

A second objective quantifies the competition between altermagnetism and superconductivity by extending the functional RG to finite temperature, identifying regimes where the altermagnetic metal is favored, estimating the critical temperature that suppresses BCS pairing, and characterizing potential unconventional superconductivity on non-circular Moreover, Ab initio calculations will supply electronic-structure inputs and connect theory with experimental measurement.

To close the loop with experiment, we will deliver testable predictions for elastic and inelastic neutron scattering, topological Hall and anisotropic magnetoresistance measurements, and spin-polarized STM, applied to canonical and proposed altermagnets (MnTe, CuMnAs, EuAuSb) with particular focus on RuO2, expected to display low-temperature (~150 K) Fermi-liquid behavior.

Overall, this 18-month program will define universality classes, distill key design principles, and speed the discovery of altermagnets for scalable, low-dissipation A Phased-Array Bistatic Radar Network for Measuring Atmospheric Winds BNL, Instrumentation Division Bistatic radar systems differ from monostatic configurations in that the transmitter and receiver are located at separate positions.

This flexible geometry offers significant advantages in both security and atmospheric sensing applications. By measuring signal strength (scattering) and phase (Doppler) from different angles, bistatic systems provide richer, more comprehensive insights into weather phenomena.

The Radar Science Group at Stony Brook University (SBU) has developed a bistatic radar receiver designed to operate in conjunction with the SBU SKYLER-2 mobile X-band phased array radar (PAR). The 2025 Seed Grant funding will support both the evaluation and data analysis of this prototype passive radar node.

The technical validation of the system—particularly its time and phase synchronization performance—will be led by the team at Brookhaven National Laboratory (BNL). In addition, the funding will support the collection of bistatic radar observations under two distinct atmospheric conditions: i) Clear-air environments and ii) severe weather conditions.

These observations have the potential to yield breakthrough insights into horizontal and vertical wind estimation in both cloud-free and cloudy conditions. Finally, initial hardware and software testing will be used by the BNL team for the design and development of a more sophisticated, versatile, and accurate time/phase synchronization system.

This next-generation synchronization capability will be integrated with a newly acquired metamaterials-based antenna radar system, further enhancing the scientific capabilities of the SBU radar science group.

The funding will maintain the SBU radar science group at the forefront of technological advantages in atmospheric remote sensing and the proposed atmospheric experiments can lead to significant advantages in the measurements of winds.

In addition, the funding will allow the Instrumentation Department (IO) at BNL to demonstrate cost-efficient methods for synchronizing arrays over km length scales, and this is expected to support IO's efforts towards establishing expertise in the techniques of high-precision timing AI-FUSE: AI-enabled Fusion of Uneven Spatio-temporal Evidence BNL, Environmental Science and Tech Building on the latest advancements of Artificial Intelligence, particularly Foundation Models, this project will develop a new technique to construct uniform 4D data cubes from field measurements.

AI-FUSE (AI-enabled Fusion of Uneven Spatio-temporal Evidence) will address a very common challenge hampering (geo)science, which is that different sensors have distinct advantages and limitations with some sensors collecting data at high spatial resolutions while others have high temporal resolution.

The resulting data gaps and misalignment in the time stamp of the measurements leave gaps in our understanding of the geosciences and limit our ability to use AI for deeper analysis.

AI-FUSE will advance existing approach by (i) filling in missing data in space, (ii) converting one sensing modality into another, and (iii) harmonizing data across asynchronous To demonstrate its scientific value, AI-FUSE is envisioned to be applied to air temperature measurements collected by a multi-sensor network in the Encanto neighborhood of Phoenix during the U.S. Department of Energy’s Southwest Urban Integrated Field Laboratory (SWIFL) field experiment.

The 4D air temperature cube derived by AI-FUSE is expected to reveal new insights into land surface–atmosphere interactions in cities during extreme heat events. A particular focus will be on quantifying the localized cooling benefits of urban parks.

These insights will support more effective planning and investment in infrastructure, while also informing energy demand management in one of the hottest urban regions in the U.S. 2513 Mendez-Mendez and Yu SBU, Electrical and Computer Engin BNL, Computing and Data Center The project "Continual Learning of Agentic AI for Scientific Applications" aims to transform how artificial intelligence (AI) supports scientific discovery by developing adaptive, self-improving AI agents.

Current AI systems for science are largely static: they rely on fixed tools and workflows that cannot evolve as new data or challenges emerge. This limitation hinders their ability to generalize to complex and changing This seed project will design an AI agent that learns from its own experiences—much like a junior scientist refining their skills over time.

The research will integrate specialized and general-purpose computational tools within a modular framework, enabling the agent to select, combine, and update methods dynamically.

Key innovations include (1) self-evaluation mechanisms that allow the agent to assess its own performance without ground-truth labels, (2) advanced in-context learning strategies that refine decision-making by drawing on prior successes and failures, and (3) continual learning techniques that allow individual tools to improve with limited data.

The team will demonstrate these advances through applications such as segmenting cell organelles in electron microscopy images, a challenging task requiring coordination of diverse models and methods.

By creating agents that continually adapt and improve, this project lays the foundation for AI systems that act as long-term collaborators Over its 18-month duration, the project will produce proof-of-concept results to position the investigators for larger-scale federal funding. Ultimately, this research aims to establish a new generation of self-improving AI agents capable of accelerating discovery across scientific domains.

SBU, Physics and Astronomy The Electron-Ion Collider (EIC), currently under construction at Brookhaven National Laboratory, will be the flagship U.S.-based collider facility, delivering unprecedented high-statistics datasets. In addition to its research program in nuclear physics, the EIC’s high luminosity, beam polarization, and precision make it a compelling discovery machine for physics beyond the Standard Model.

In particular, it can probe a unique parameter space of light, weakly coupled particles motivated by various dark matter models. Such scenarios may be inaccessible to searches at other collider facilities, such as the Large Hadron Collider (LHC) at CERN, making the EIC an important bridge between nuclear and high-energy physics.

Conventional search strategies, which rely on specific model hypotheses and predefined observables, are generally not optimized to identify anomalous events when signals are weak or exhibit exotic signatures. These limitations motivate the development of artificial intelligence (AI)-driven methods that exploit full event-by-event information to unlock the EIC’s discovery potential.

Within this research program, we will develop modern AI techniques for model-independent searches, establish benchmark datasets, and provide tools to guide future theoretical and experimental developments. High-Density 2.

5D Integrated Point-of-Load Converters for Extreme Environments Abstract SBU, Electrical and Computer Engin BNL, Instrumentation Division This project will conduct a systematic study of power conversion circuit design and packaging for extreme environments, especially under cryogenic temperatures and high radiation environments.

The proposed power converters will address fundamental issues in Microelectronics for extreme environments to target multiple applications in Quantum Computing, Nuclear and High Energy Physics, Artificial Intelligence Science, and Accelerator Science.

Accordingly, the team will investigate and compare different power conversion topologies, packaging materials, and structures, and demonstrate the integration of these technologies in high-density cryo-cooled and radiation-hardened power-of-load (POL) converters. Two exemplary applications will be pursued during the project period.

The first addresses the demand for high step-down (48-1V) high-current power conversion in AI/quantum computing applications. To address this need, the team proposes 2. 5D integrated multi-phase cryo-cooled POL gallium nitride (GaN) converters with embedded magnetic substrates that down-convert from 48 V to 1 V at load levels up to 100 A with power efficiency > 95%.

Such cryo-POL converters operate at cryogenic temperature and can be co-located with quantum computing chips and/or superconducting CPUs, thus significantly reducing power transmission losses and heat loss due to cables and feedthroughs.

The second application addresses the demand for improved monitoring and quench detection of superconducting magnets, which are a critical enabler for nuclear and high energy physics using particle accelerators [Marchevsky2021]. To address this need, the team proposes in-magnet quench detection electronics powered by isolated low-power (~100 mW) GaN POL converters that can operate down to 4K.

Common challenges for both applications include cryogenic POL packaging, power architecture/topology development for cryogenic power conversion, and integration. The team will also study radiation-hardening methods for the design and packaging to ensure that the proposed Cryo-POL power converters can operate safely in the high-radiation environments associated with particle accelerators.

Van-der-Waals ThermoTiles: Tunable 2-D Thermoelectrics for On-Chip Hot-Spot Cooling and Heat Harvesting in Advanced CMOS SBU, Physics and Astronomy BNL, Instrumentation Division Ever-denser CMOS logic and 3-D chip stacks routinely generate sub-millimeter hot spots that rise >30 C in microseconds.

Conventional airflow or even rack-level liquid loops can remove average power, but they cannot quench the sub-millisecond heat spikes that throttle AI tensor cores.

We propose to create Van-der-Waals ThermoTiles: atomically thin, electrostatic-tunable thermoelectric (TE) laminates that sit directly beneath logic layers, actively pump heat out during computing bursts (Peltier mode) and ideally scavenge residual heat during idle (generator mode).

Our proposed work is based on the hypothesis that thermoelectric 2-D topological-insulator materials (Bi₂Se₃/Sb₂Te₃), stacked into hexagonal Boron Nitride (hBN) and Fermi-level-matched with electrostatic gating, can attain a room-temperature figure of merit ZT ≥ 1.

Coupled with ultralow (<10⁻⁷ Ω cm²) Cr/Au contacts, such materials will deliver a coefficient-of-performance (COP) >2 for hot-spot shaving and to harvest tens of µW mm⁻² during idle mode, enough to power on-die sensors autonomously.

The proposed research activities include: 1) Thickness-dependent benchmarking of h-BN / Bi₂Se₃ / h-BN heterostructures; 2) electrostatic carrier tuning to exceed ZT > 1; 3) developing gate-tunable p-n micro-Peltier leg for hotspot cooling.

By combining topological-insulator physics with robust device engineering, this work will position our labs at the forefront of materials-driven thermal management and open new avenues for energy-efficient, high-power microelectronics. Such development will allow future funding applications to various federal grants.

SBU, Electrical and Computer Engin The goal of this seed proposal is to establish a unified probabilistic framework for autonomous systems (robots) that must perceive and act reliably in uncertain, dynamic environments. The work addresses two tightly linked challenges. First, we will integrate vision, tactile, and language-based inputs and estimate uncertainty in each modality.

This will enable the system to identify when information is unreliable. Second, we will develop models that use these uncertainty estimates to guide action selection, both to accomplish tasks and to acquire better information through active perception. This approach will allow the robot to reduce ambiguity through interaction rather than accept flawed inputs passively.

We will develop probabilistic methods to fuse information across vision, tactile sensing, and natural language in settings with sparse, occluded, or inconsistent observations. Such fusion is difficult when inputs arrive asynchronously, contain noise, or lack completeness, conditions common in real-world robotics.

We will also develop algorithms for task-level control and motion planning that adapt to uncertainty in perception and dynamics so that downstream actions incorporate confidence estimates to avoid failure. Many motion-planning methods assume a known state and deterministic observations, though recent work accounts for uncertainty.

Our approach will operate in a belief-space framework that integrates posterior distributions and will enable the robot to select actions that maximize task-relevant information gain, reduce uncertainty, and preserve safety margins.

Contributing personnel: Fernando Next Generation Diagnostic Tests for Radiation Tolerant AI Hardware SBU, Electrical and Computer Engineering There is increasing reliance on artificial intelligence (AI) hardware in critical applications such as military, space, and nuclear environments. Such extreme environments are prone to damage from radiation, which significantly affects the reliability of the electronics.

Traditional radiation analysis and test methods have evaluated the impact of radiation on data and transistor characteristics. These methods are not sufficient for emerging AI hardware where system-level inference accuracy is typically the most important objective.

The specialized cross-layer expertise in AI hardware at SBU, coupled with the expertise on radiation effects and advanced testing facilities available at BNL, creates a unique opportunity. We plan to obtain experimental data via the NASA Space Radiation Laboratory (NSRL) at BNL. NSRL is a unique facility that simulates the high energy cosmic rays found in space by extracting beams of heavy ions from the AGS Booster Synchrotron.

The goal of this research is to adopt a top-down approach in evaluating the impact of radiation on AI hardware. The proposed approach will investigate radiation effects on FPGA-based AI architectures executing application tasks in real time.

We will target single event effects such as single event upsets that cause bit-flips on stored data and single event transients that cause current spikes during signal propagation throughout the chip, and longer term effects such as total ionizing dose that affect the characteristics of the transistors such as threshold voltage.

Although extensive research has been conducted to understand the impacts of radiation on materials, devices, and low complexity circuits, it is highly challenging to connect these effects directly to the inference accuracy of AI applications that are executing on highly complex architectures.

In this research, we will target this challenge by leveraging our previous work on fault tolerant AI hardware, modeling, and the experimental data we will obtain through NSRL Interactive Visualization for Autonomous X-ray Scattering Experimentation BNL, Center for Functional Nanomaterials (CFN) An emerging paradigm in experimental science leverages AI and machine learning not just for data analysis but to fully automate the entire experimental process, known as Autonomous Experimentation (AE).

AE automates all steps – from sample preparation and measurement to data analysis and decision-making for subsequent experiments. This research project addresses the specific needs of autonomous X-ray scattering experiments. A crucial element of AE is the decision-making algorithm which can be considered an optimization problem within a multi-dimensional parameter space.

By enabling an efficient approach to conducting experiments, AE promotes a more interactive and interventional scientific discovery process through a scientist-in-the-loop approach. This approach necessitates visual tools that allow scientists to quickly and accurately interpret imaging outcomes in the context of the underlying physics. Yet, the task is complicated by the multi-valued, high-dimensional nature of the scatter images.

Current visualization techniques are insufficient for this purpose as they tend to strip away the physical semantics that is central to the imaging analysis. In this research we will develop novel visualization tools designed to enhance the interpretability of high-dimensional data generated during AE processes.

Specifically, we will develop a Multivariate Transfer function Editor (MTE) that combines spatial and information visualization techniques, allowing scientists to map complex data into intuitive, colorized displays. This integration will provide a holistic view of experimental outcomes, enabling researchers to interactively explore and refine their models in real-time.

By coupling advanced AE systems with these visualization tools, our approach not only accelerates the discovery process but also empowers scientists to tackle more intricate scientific challenges, bridging the gap between automated experimentation and human expertise.

Integrated Large-Area Photodetectors and Field Cage for New Physics Detection in Time SBU, Physics and Astronomy The proposed R&D is taking place in the context of the second phase of the next-generation US-based leading-edge international experiment for neutrino science the Deep Underground Neutrino Experiment (DUNE). DUNE will consist of two neutrino detector complexes placed in the world’s most intense neutrino beam.

One detector will record particle interactions near the source of the beam, at the Fermi National Accelerator Laboratory (FNAL) in Batavia, Illinois. A second, much larger, set of detectors will be installed more than a kilometer underground at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, 1,300 kilometers downstream of the source.

In its first phase, two 17 kton Liquid Argon Time Projection Chamber (LArTPC) will be installed at SURF, while other two detectors will be added for its second phase. These detectors will be an extremely critical component to achieve the primary physics goal of the experiment as well as expand its capabilities beyond the first phase.

One of them is planned to be another 17 kton LArTPC equipped with a more capable and higher-coverage photon detection system. Particles passing through Liquid Argon (LAr) ionize and excite it, and scintillation light of 128 nm is produced. This light must be shifted to a larger wavelength wavelength to fall within the sensitive region of commercial Silicon Photomultipliers (SiPM).

To achieve larger photon detection coverage compared to the first phase, a lighter, more compact, and cheaper photon detector needs to be implemented. This R&D activity will investigate the possibility of building such a detector as well as a new design for the LArTPC that integrates it.

A Simulation Testbed for Hop-by-Hop Quantum-Classical Hybrid Networks with Integration of Quantum Federated Learning BNL, Computational Science Iniative Quantum networks are essential for the secure, global deployment of quantum computing, enabling collaboration among quantum computers through entanglement-based qubit teleportation.

This project aims to develop a simulation testbed for hop-by-hop quantum-classical hybrid networks that seamlessly integrates quantum and classical technologies within network architectures. One of the key use cases for the testbed will be the implementation of quantum federated learning. The resulting software platform will also allow other researchers to evaluate their quantum network designs and quantum applications.

Rapid Forcasting of Storm Surge with Physics-Guided and Data-Driven Machine Learning SBU, Applied Math and Statistics BNL, Environmental Climate Sciences Department Storm surgeis an abnormal rise of water above the astronomical tide, usually generated by a storm such as a hurricane or typhoon.

Accurate and timely storm surge forecasting is crucial to coastal communities especially given the ongoing climate change which is projected to cause increasingly more frequent and violent hurricanes with higher and wider storm surges – further exasperated by an increasing sea level – due to global warming and glacier melting.

The current gold-standard in storm surge forecasting is a physics-based model entitled the Advanced Circulation (ADCIRC) model. ADCIRC is considered the most accurate physics-based model to date, however, even with several parallel computing versions developed via supercomputer and MPI, running a high resolution ADCIRC with a limited computation resource is still too time consuming to deploy in real-time.

Instead, another less sophisticated and generally less accurate physics-based model, the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model, is adopted as the operational model by the National Hurricane Center (NHC) because of its computational efficiency. In this work, we propose to develop a hybrid modeling approach integrating machine learning (ML) methods, historic hurricane data, and the physics based ADCIRC model.

With the guidance of high-resolution ADCIRC data in the ML model training phase and low-resolution ADCIRC data in the ML online deployment phase, the hybrid model can perform a series of simulations with different boundaries and initial conditions in training, thus learning the underlying physical laws and extrapolate the results in training and in real-time deployment.

Additionally, this hybrid model can incorporate additional variables that are ignored by the physics models and implicitly learn the fundamental principles perhaps overlooked by the state-of-the-art physics models. Other team members: Dr. Zhenhua Liu, SBU Applied Mathematics and Statistics and Computer Science; Dr. Minghua Zhang, SBU School of Marine and Atmospheric Sciences; Dr. Tao Zhang, BNL Environmental and Climate Sciences.

COMING SOON Synchronized Atomic Systems for Quantum Networking SBU, Physics and Astronomy BNL, Instrumentation Division Polymeric Characterization of Intrinsically Disordered Tau Protein: Towards a Molecular Understanding of Aggregation in Prion Like Proteins Prion diseases are transmissible, neurodegenerative diseases associated with the aggregation of intrinsically disordered proteins (IDPs), i.e., proteins that behave like flexible polymers in their healthy state.

Strategic preparedness to such diseases thus requires fundamental advances that provide insight on how sequence determined molecular interactions control the thermodynamic and structural properties of IDPs. The IDP tau protein, associated with Alzheimer’s disease and other tauopathies, undergoes prion like aggregation, and recent studies highlight the importance of electrostatic interactions in such processes.

However, how intermolecular interactions determine whether tau protein aggregates or remains in its healthy state is not well understood. A significant roadblock in understanding these processes is that a self-consistent model of tau protein connecting sequence defined molecular interactions to thermodynamic properties has not been developed.

Such a model would be an important tool to predict tau protein properties and guide the development of drug treatments and diagnostic tools. In this proposal, we aim to develop a self-consistent model of tau protein electrostatic interactions at biologically relevant pH and salt concentrations. This will be accomplished through a combination of scattering experiments, coarse-grained molecular dynamic simulations, and scaling theory.

The combined approach will result in a predictive model determining the role of electrostatic interactions on tau protein polymeric structure at physiological conditions. Furthermore, the work will be an important step towards developing a model that accounts for the different intramolecular interactions determining the properties of tau protein.

Hybrid Quantum Algorithm for Parabolic and Elliptic Partial Differential Equations SBU, Appled Math and Statistice BNL, Computational Science Initiative In the past few years, companies such as Google, IBM, IonQ, Quantinuum, and Rigetti have achieved remarkable advancements in the development of quantum computers.

A number of companies have now launched quantum computers with around 50 qubits, with IBM leading the way with a 433-qubit quantum computer - the largest number of qubits currently available.

Despite the fact that the accuracy of quantum computers is dependent on the implementation technique of the Noisy-Intermediate Scale Quantum (NISQ) hardware, such as superconducting or trapped ion, regardless of their architecture, they are prone to various noises, errors, and decoherence. Contemporary quantum devices are restricted in their ability to produce dependable results for practical computing problems.

Despite vendors releasing more advanced quantum computers with higher Quantum Volume, which is a means of quantifying a quantum device’s computational power, these devices are still a long way from achieving quantum supremacy for practical problems. Moreover, the attainment of fault-tolerant quantum computers is currently unfeasible and may remain for several decades.

Consequently, it is imperative to maximize the utilization of NISQ devices to gain a quantum advantage on such devices. To optimize the utilization of NISQ devices, various methods have been proposed.

Variational Quantum Algorithms (VQAs), including Quantum Approximate Optimization Algorithm (QAOA), Variation Quantum Eigensolver (VQE), and Quantum Neural Networks, have gained significant attention as potential approaches to achieve quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) devices.

Therefore, we propose to develop a variation and quantum-classical hybrid algorithm on NISQ devices to solve the partial differential equations in electrophysiology, representing the cardiac tissue as overlapping intracellular and extracellular domains, as is needed in defibrillation modeling.

Analysis of Francisella Tularenis Membrane-Derived Structures and Host-Pathogen Interactions Using Cryo-Electron Tomography SBU, Microbiology and Immunology BNL, Lab for BioMolecular Structure, NSLS II Francisella tularensis is an intracellular bacterial pathogen that causes the zoonotic disease tularemia. F. tularensis is highly virulent and easily transmitted to humans when aerosolized.

Inhalation of even small doses of bacteria results in a severe pneumonia with high rates of morbidity and mortality. The molecular basis for the high infectivity, virulence, and intracellular pathogenesis of F. tularensis is not well understood.

To interfere with host immune responses, intracellular bacteria typically secrete virulence factors that target host pathways. However, secretion systems and effector proteins used by F. tularensis to modulate host responses are poorly understood.

To address this gap in knowledge, we have characterized the production of novel tubular structures by Francisella as potential systems for interaction with host cells and delivery of virulence factors. Based on our published and preliminary findings, we hypothesize that F.

tularensis responds to specific signals during host cell infection to upregulate tube production, using a novel cytoplasmic machinery that creates extensions of both the bacterial inner and outer membranes, and that these tubes interact with the host to facilitate pathogenesis.

To address this hypothesis, we will pursue two Specific Aims: (1) Characterize the molecular and structural basis for tube production by Francisella; and (2) Analyze tube production during Francisella infection of macrophages. The Francisella tubes are distinct from previously reported bacterial structures and their presence suggests a novel mechanism by which F. tularensis interacts with its environment.

The proposed studies will employ cryo-electron tomography as a major focus to reveal the molecular and structural basis for regulated tube production by Francisella.

These studies will advance fundamental knowledge of microbial physiology and microbial pathogenesis, reveal strategies by which intracellular pathogens interact with the host to cause disease, and provide a basis for improved medical countermeasures Self-Healing Wireline Transceivers with Embedded Intelligence for Extreme Environments SBU, Electrical and Compuiter Engineering Nicholas St.

John Custom application specific integrated circuits (ASICs) play an important role in readout systems within high-energy physics experiments. These ASICs should have high performance and efficiency, while operating in harsh environments such as high radiation and cryogenic temperatures.

In existing work, the reliability of these ASICs is enhanced by adding redundancy to critical circuit blocks and/or relying on worst-case design methodology. These existing approaches are not only time consuming to implement, but also introduce significant area and power overhead.

In this work, we propose incorporating embedded machine learning (ML) capabilities within one of these ASICs to autonomously boost performance in an energy-efficient manner. Specifically, our work will involve the study and implementation of embedded ML functionality within a full-duplex wired transceiver ASIC.

The proposed ASIC will autonomously optimize the efficiency of the wired data link, while also detecting anomalies caused by extreme environmental conditions. When an anomaly is detected, the ASIC will correct errors without relying on traditional techniques such as redundancy and worst-case design methodology.

AI-Enabled Sparse Data Acquisition, Compression and Federated Processing SBU, Electrical and Computer Engineering BNL, Computational Science Initiative Today, there is a large amount of big data available for analysis. Especially, the data volume produced by large-scale scientific instruments (e.g., BNL’s scientific instruments, National Synchrotron Light Source II (NSLS-II) and sPHENIX detector) is often of substantial size.

The big amount of data poses great challenges for storage, transmissions and analysis. Moreover, with the advancement of mobile networks, there is a growing potential to collect data remotely and analyze data using edge computing devices. The wireless devices are often lightweight, operate on limited battery power, and need to run cost effectively.

A device, such as an Unmanned Aerial System (UAS) or a drone, can serve as an edge computing device, but is constrained by the energy and transmission bandwidth. Consequently, wireless connected devices need to be lightweight in computation, and operate power and bandwidth efficiently in data acquisition and transmission.

Making significant advancements in data compression algorithms is crucial to optimize real-time, high-volume 3D image analysis at the edge. The advancement from this work will greatly enhance both data analysis and data communication processes, resulting in improved performance and efficiency for edge computing.

Diamond-Based Quantum Sensing for High Energy Physics Applications SBU, Physics and Astronomy BNL, Instrumentation Division This project deals with the development of solid-state detectors for applications in high energy physics, mainly, for nuclear recoil registration and directional information detection systems.

Here, our aim is to make proof-of- principle demonstration of directional detection using defect center-based quantum sensing in diamond. This will consist of three steps; firstly, we develop a quantitative model for events registration based on properties of defect centers.

This will involve first-principles calculations based on density-functional theory (DFT) to quantitatively determine the effects of nearby displaced nuclei on the properties of defect-centers. We will then prepare diamond samples by irradiation with high energy neutrons (MeV) sources.

Finally, we will use confocal microscopy for fluorescent nuclear track detection (FNTD)-type experiments to reconstruct 3-dimension maps of neutron damages. Proton FLASH Therapy Targeting for Deep-Seated Tumors BNL, Collider Accelorator Dept. We propose to develop proton FLASH radiation therapy by using spread-out Bragg peak of proton beam for deep-seated tumors.

This will bring therapeutic gain by differential enhancement of tumor control while preserving normal tissue.

This study is in collaboration with the Department of Radiation Oncology in Stony Brook University (SBU) where the expertise in the focused radiosurgery is being performed clinically, and the Collider Accelerator Department from Brookhaven National Laboratory (BNL) with expertise of designing and constructing the compact magnet synchrotron to accelerate protons enough to make spread-out Bragg peak