Rutgers University New Brunswick

NSF Awards · FY 2024 · 2024-08

This doctoral research project combines stable isotope analysis with behavioral and environmental data to investigate how peatland fires and land clearance impact the health and energetics of a wild great ape species. The project advances understanding about how a keystone species adapts to climatic and human-induced fires and habitat loss, while also providing a model of potential energetic plasticity in our hominin ancestors during times of resource scarcity and in variable and unpredictable environments (like fire prone regions). The project supports conservation efforts, international research collaborations, and community engagement at the research site and provides training opportunities for undergraduate students, including those from underrepresented groups in STEM, at Rutgers University. The project examines whether ecological disturbances such as peatland fire burns and road construction influence the local ecology and energetic status of wild orangutans. Carbon (δ13C) and nitrogen (δ15N) stable isotopes from urine and hair are used to identify changes in energy balance in orangutans that experience these ecological disturbances to varying degrees. Plant materials (which include a combination of orangutan plant foods and commonly encountered plants) are also isotopically analyzed to establish an environmental baseline and assess variation in plant physiology and nutrient availability at various distances from disturbances across the site. The project also analyzes urine samples previously collected from before, during, and after past peatland fires to assess the same impacts on health and energetics in orangutans. The researchers use Geographic Information System (GIS) data for spatial analysis and Generalized Additive Mixed Models (GAMMs) to identify shifts in isotopic signatures at different distances from the disturbances while incorporating variables such as age, sex, and environmental factors, to provide a comprehensive analysis of how disturbances influence orangutan health. 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

There are countless mathematical models constructed by mathematicians over the years. Understanding them and their chaotic behavior is an everlasting challenge for future generations. Generally, mathematical objects are better understood when organized according to some notion of equivalence. This common practice is called “classification”, and understanding the extent to which certain classifications are efficient is a common task for working mathematicians. Classification is closely related to rigidity phenomena. “Rigidity” is the study of distinguishing mathematical objects, even when they appear to be almost identical. The main goal of this project is to explore the interplay of classification and rigidity. As an essential part of this project, the PI will train and mentor graduate students at Rutgers University. More specifically, this project concentrates in classification and rigidity theorems in group theory and dynamics using techniques from descriptive set theory. The PI will study rigidity aspects of countable group actions with techniques from measured group theory and cocycle superrigidity. The PI will continue the investigation on the relationship between left-orderable groups, and the corresponding spaces of left-orders under the viewpoint of Borel classification theory. Implications in geometric topology, in particular in the theory of 3-manifolds, are further explored. Moreover, the PI will investigate the descriptive complexity of countable Borel equivalence relations that model classification problems in group theory. 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

Minimal surfaces, often modeled by soap films, are shapes in equilibrium first studied by Lagrange in the 1700s. Such surfaces locally minimize area and appear everywhere in nature— in chemistry, materials science, biology, and general relativity. In mathematics, they have been applied more recently to solve problems in Geometry and Topology, such as the Poincaré Conjecture, the Willmore conjecture, and more recently to prove the Smale Conjecture in many spherical space-forms. The PI will work to discover new minimal surfaces which are not area-minimizing (and thus very difficult to find in nature) - but which can have other major applications in Geometry. In addition to this research, the PI will focus on teaching and training of undergraduate and graduate students as well as advancing the field by organizing conferences, conducting mini-courses for graduate students, writing expository materials and working toward making a computational study of minimal surfaces accessible to the wider mathematical public. More precisely, the objectives of this project are to develop new techniques to study min-max minimal surfaces obtained from high-parameter sweep-outs as well as find further applications in Topology and Geometry. Recently the PI used a two-parameter sweepout to construct long-conjectured singularity models of the mean curvature flow (MCF), and he will further study the geometry and properties of these new examples. In particular, the relationship between minimal surfaces in the shrinker metric and expander metric will be explored in light of Ilmanen’s Genus Reduction Conjecture, asserting that the genus of a surface at a singularity of the MCF must strictly drop. The PI will more generally use min-max and flow methods to show that the lowest genus stabilization of two irreducible splittings is realized by an index 2 minimal surface addressing a conjecture of D. Bachman. Higher-parameter families will be used to prove the Goeritz-Powell Conjecture asserting, roughly speaking, that the fundamental group of the space of genus g Heegaard surfaces in the three-sphere is finitely generated. The PI will also obtain multiplicity one results as well as sharp index estimates in the min-max theory in the case of stable surfaces. 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

Massive and diverse high-dimensional datasets are now routinely collected in a wide range of scientific fields. In many instances, in addition to the primary data from the target study, other datasets from different populations or under different environments with a similar structure to the primary data have been collected. Incorporating such related auxiliary data is desirable to make more accurate and informative decisions. For example, the availability of large-scale genomic and proteomic data promises a better understanding of disease processes and suggests the possibility of more accurate prediction of disease outcomes. Efficiently extracting meaningful information from multiple such datasets becomes a critical problem in medical research, which presents unprecedented opportunities to statisticians and data scientists. The project's goal is to devise a collection of advanced statistical tools for efficient integrative analysis of EHR and genomics data. The PIs aim to address the pressing need for novel statistical methods to perform efficient integrative analysis that combines multiple data sources. The PIs plan to develop new methodologies and optimality theory for efficiently integrating large-scale data from multiple sources and to address critical biomedical problems using the newly developed methods. There are three major research goals to be pursued. One is to develop data-driven algorithms with theoretical optimality guarantees for transfer learning in various settings, including estimation and inference of high-dimensional covariance matrices, covariance functions for functional data, instrumental variable regression, and conformal inference. The second is to develop a class of adversarially robust algorithms that efficiently integrate the heterogeneous information from the multi-source data, including constructing the guided adversarially robust learning and conducting the group significance test for high-dimensional and nonparametric models. The third is to address the urgent needs and new challenges in biomedical studies through the analyses of EHR data and integrative genomics, using the newly developed methods for transfer learning and adversarially robust learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project provides new methods for estimating causal effects from non-randomized studies. Quantifying the causal effect of a variable on another one is of fundamental importance in science because it allows for the understanding of what happens if a certain action is taken, e.g., if a drug is prescribed to a patient. When randomized experiments are not feasible, e.g., because of costs or ethical concerns, quantifying the effect of a treatment on an outcome can be very challenging. Roughly, this is because the analysis must ensure that the treated and untreated units are “comparable,” a condition implied by proper randomization. In these settings, the analyst typically proceeds in two steps: 1) they introduce the key assumptions needed to identify the causal effect, and 2) they specify a model for the distribution of the data, often nonparametric, to accommodate modern, complex datasets, as well as the appropriate estimation strategy. One key difficulty in non-randomized studies is that estimating causal effects typically requires estimating nuisance components of the data distribution that are not of direct interest and that can be potentially quite hard to estimate. Focused on the second part of the analysis, this project aims to design optimal methods for estimating causal effects in different settings. Informally, an optimal estimator converges to the true causal effect “as quickly as possible” as a function of the sample size and thus leads to the most precise inferences. Establishing optimality has thus two fundamental benefits: 1) it leads to procedures that make the most efficient use of the available data, and 2) it serves as a benchmark against which future methods can be evaluated. In this respect, the theoretical and methodological contributions of this project are expected to lead to substantial improvements in the analysis of data from many domains, such as medicine and the social sciences. The project also aims to offer opportunities for training and mentoring graduate and undergraduate students. For certain estimands and data structures, the principles of semiparametric efficiency theory can be used to derive optimal estimators. However, they are not directly applicable to causal parameters that are “non-smooth” or for which the nuisance parts of the data distribution can only be estimated at such slow rates that root-n convergence of the causal effect estimator is not attainable. As part of this project, the Principal Investigator aims to study the optimal estimation of prominent examples of non-smooth parameters, such as causal effects defined by continuous treatments. Furthermore, this project will consider optimal estimation of “smooth” parameters, such as certain average causal effects, in newer nonparametric models for which relatively fast rates of convergence are possible, even if certain components of the data distribution can only be estimated at very slow rates. In doing so, the project aims to propose new techniques for reducing the detrimental effect of the nuisance estimators’ bias on the quality of the causal effect estimator. It also aims to design and implement inferential procedures for the challenging settings considered, thereby enhancing the adoption of the methods proposed in practice. 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 International Symposium on Microarchitecture (MICRO), sponsored by the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM), is widely recognized as a premier forum for disseminating cutting-edge ideas and innovations in the field of computer architecture. MICRO brings together a diverse group of researchers in areas spanning computer hardware/software/hybrid design, systems, chips, and compilers to facilitate interactions and advance the state-of-the-art in computing research and emerging areas. This grant seeks to broaden student participation at MICRO 2024 to be held in Austin, Texas on November 2-6 along with a series of co-located workshops on related topics. The travel fund will provide students with valuable exposure to new ideas and opportunities to engage with leading experts in their fields. It will advance computer systems design education and research in academia, cultivating a strong workforce to tackle the intersection of technological and societal problems that lie ahead. This award will provide travel support for at least 25 US-based students attending MICRO 2024 by helping defray a portion of their travel, lodging, and attendance expenses. Each awardee will receive approximately $600 of support, subject to their individual needs. This fund especially focuses on increasing institutional, geographic, and demographic diversity and balances the participation of students who are first-time attendees, lack existing travel support, women, underrepresented group members, and students with disabilities. 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

Public safety has become an increasingly important issue in the United States due to the potential threat posed by hidden weapons and homemade explosives in public places where extensive security checks are not available. Traditional security systems, such as X-ray machine screening, are expensive and primarily deployed in high-security areas like airports and government buildings. This project proposes to leverage the prevalent WiFi infrastructure in many public spaces to enable hidden object detection. The project team utilizes extracted WiFi signal features to identify the shape of hidden objects and determine their materials, and subsequently be able to detect suspicious items. The success of this project will greatly enhance public safety by offering easy to deploy and low cost detection systems at public venues (e.g., schools, theme parks, and sports stadiums), thereby addressing the urgent need for better safety in everyday public spaces. Building upon the team's previous foundational work, this project investigates using received WiFi signal features to determine the types of materials of objects inside bags. Target identification models and domain adaptation frameworks based on deep learning techniques are designed to ensure a good identification accuracy in diverse environments. Robust shape reconstruction algorithm helps to recognize suspicious objects. Additionally, new mechanisms using directional antennas are developed to mitigate the impact of the bag carrier's movements. The TTP project will create a prototype system and validate the system's functionality, accuracy, and robustness. The project team seeks to integrate the project's research efforts with educational activities such as developing graduate and undergraduate curricula. The team will also recruit underrepresented students into the project. The team will work closely with technology collaborators for field trial and potential deployment into an operational environment. 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 Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers University plans to hold “Frontiers in Complexity Theory: A Graduate Student Workshop” at Rutgers University on July 29–August 1, 2024. The workshop will bring together a large cohort of graduate students working in theoretical computer science to learn about some of the most recent trends in complexity research. The event will be the concluding event of the DIMACS Special Focus on Lower Bounds in Computational Complexity, which is itself part of the DIMACS/Simons Institute Collaboration on Lower Bounds in Computational Complexity. The workshop is structured around four “mini-workshops,” each consisting of a series of three two-hour tutorial-style lectures that guide participants from foundational ideas to recent breakthroughs in a current area of complexity theory research. The mini-workshops are: 1) Meta-complexity, 2) Error-correcting Codes, 3) Algebraic Complexity, and 4) Derandomization. Each participant will attend two of the four mini-workshops, attend keynote presentations by the preeminent researchers Avi Wigderson and Ryan Williams, and hear an extended presentation on a recent breakthrough result showing the existence of locally testable codes that have both constant relative distance and rate and are testable with a constant number of queries. By gathering a large cohort of 70-80 graduate students from across the United States and placing them at the forefront of current research in complexity theory, the workshop has the potential to be formative for the relevant generation of complexity theorists, with lasting effects on the field. This award provides accommodations that makes it possible to host these students for the duration of the workshop. 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

Changes in land ownership significantly impact pastoralist and agricultural communities across the world. This doctoral dissertation examines how communities adapt not just to environmental and resource effects but also to the social changes that accompany changes in land ownership. The investigators specifically study the various social networks and other mechanisms that pastoralist communities utilize as their societies move from conditions of communal to individual land ownership. The research trains a graduate student in scientific data collection and analysis and broadens the participation of underrepresented communities in science. Research findings will be shared through scientific publications and with policymakers to help develop sustainable and robust land management practices. To understand the impacts of land ownership on social connections, social cooperation, and local resource management, the investigators use both qualitative and quantitative data collection. This includes interviews, surveys, and group discussions. The data will be analyzed to test for various categories of demographic variability in adaptation and social resilience. The research results contribute to the science of resource and land management, environmental anthropology, and social adaptations to environmental change. 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

One of the great successes of modern science has been the understanding that simple objects can combine to form complicated structures. For instance, most of the matter that surrounds us is made up of protons, neutrons, and electrons. These building blocks, taken individually, are rather simple, but, when enough of them interact with each other, they produce complex and varied structures, from the intricate patterns of tiny snowflakes to enormous spiraling galaxies. This is a very powerful idea: in principle, it suffices to understand the behavior of simple particles to derive everything. However, in practice, this is a very challenging task, as keeping track of large numbers of interacting particles is impossibly difficult. Instead, sophisticated techniques need to be developed to extract the relevant collective behaviors. This project consists in developing and investigating several such techniques. This project focuses on three types of particle systems, both classical and quantum, which exhibit different types of collective behavior. The first is a model of interacting quantum particles called Bosons. This is a toy model for helium atoms, which are known to form a superfluid phase at low temperature, in which the helium flows without viscosity. The principal investigator (PI) is studying the so-called "Simplified Approach", which has been shown to reproduce much of the complex behavior of the interacting Bose gas, while being much more tractable. The second is a classical model in which the PI is proving the existence of crystalline phases, in which infinite large-scale regular patterns spontaneously emerge. The third is a model of interacting quantum particles called Fermions. This is a toy model for the electrons in conductors, which are known to form a superconducting phase at low temperature, in which electricity flows without resistance. The PI is investigating "hierarchical models", for which exact solutions can be found, and complex behavior can be proved. This project includes a significant educational component at various levels. The PI is developing graduate, undergraduate, and master's level courses that incorporate the techniques developed in the project, thus introducing students to the tools and techniques of mathematical research. In addition, the PI is producing and distributing educational videos aimed at high school students, undergraduates, and the general public, which are informed by the PI's perspective as a researcher. In addition, the PI is involved in a project to design new mathematical reasoning courses at Rutgers, based on the formal proof assistant called "Lean". This project lies in the field of mathematical physics and aims to develop new tools and refine existing ones to analyze the effect of interactions in a systematic and mathematically rigorous way. Specifically, it consists of the analysis of three types of systems: interacting Bose gases, classical hard-core particle models at high density, and interacting lattice Fermi gases. To analyze the interacting Bose gas, the PI is investigating the "Simplified Approach", which is a nonlinear, nonlocal partial differential equation (PDE) in three dimensions. Its analysis has yielded very promising results: it reproduces all known and conjectured behavior of the Bose gas for all densities. The objectives of this part are to solve the more important problems that are still open about this PDE and study its relation to the original many-Boson problem. The PI has developed a framework to study a large class of hard-core particle models at high density and prove that these behave like crystals in that regime. The objectives of this part are to extend the family of hard-core particle models for which we can rigorously prove ordering phase transitions to include three dimensional models, liquid crystals, as well as continuum models. To study interacting lattice Fermi gases, the PI is using the Renormalization Group (RG), which is a powerful tool to study systems of interacting quantum particles, but it is notoriously difficult to implement. The PI has introduced a family of models for which the RG analysis can be carried out easily, rigorously, and exactly, and nontrivial properties can be proved. The main objective of this part is to define and study Fermionic hierarchical models that exhibit superconductivity. 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

Quantum Chromodynamics is the theory of the strong nuclear force and predicts a unique state of matter called the Quark-Gluon Plasma (QGP), which is made of free quarks and gluons. During high-speed collisions of heavy ions, nuclei break down into these basic particles, forming the QGP. This matter was found to behave like a nearly perfect liquid at the Relativistic Heavy Ion Collider (RHIC). Scientists continue to study the QGP, with RHIC offering a wide range of collision energies and the Large Hadron Collider (LHC) reaching the highest temperatures and energy densities. With this award, the PI and graduate students will investigate the properties of the QGP. They will measure observables that arise when jets of particles move through the QGP. Their aim is to move from a basic description of the QGP to a detailed understanding of its properties. The PI also aims to boost diversity and competitiveness in U.S. STEM fields by inspiring female students, recruiting women and minority undergraduates in physics, and involving them in research. The PI's research will use hard probes to study the QGP. She will measure jet observables in heavy-ion collisions at LHC using the CMS detector and at RHIC using the STAR detector. To map out the phase diagram of the QGP at different scales, the PI and her team will measure the center of mass energy dependence of jet shapes for inclusive jets and jets coincident with vector bosons. This will help constrain the jet energy, enabling a controlled measurement of how the medium modifies jets and the plasma's response. Additionally, they will directly measure heavy flavor quark jets and D0 meson-tagged jets at lower kinematic regions where mass effects are significant, probing the flavor dependence of parton energy loss. The research will incorporate machine learning techniques to simultaneously analyze jet measurements, enhancing the precision and efficiency of data interpretation. These complementary measurements will determine key features of the QGP, including its thermodynamic properties and transport coefficients, how it responds to jet energy loss, and how it affects jet structures. Furthermore, the PI will promote STEM education for underrepresented minorities through initiatives such as encouraging female middle and high school students to pursue STEM, incorporating high-energy nuclear physics research into introductory physics courses, and mentoring minority students throughout the research process. 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

Over a century ago, the Research Vessel Albatross collected fishes from the Philippines, now stored at the Smithsonian Institution. The archive provides the potential for rare insights into how fish have evolved in response to fishing, habitat loss, and other challenges. The research will compare historical and modern fish and will focus on blue sprat, a small coastal species important for food. The research findings can help understand adaptation across many species facing similar challenges. The project will also support paid research internships for students with limited access to careers in science. The project will host workshops to build international exchange with the Philippines. Finally, this research can inform fisheries by identifying fishing zones and where seafood was caught. This project will help to understand the architecture and genomic origins of rapid adaptation, in part by testing the hypothesis that local adaptation provides the raw material for rapid evolution through time. Species objectives include to 1) assemble and annotate high-quality genomes to understand genetic architecture in blue sprat (Spratelloides delicatulus); 2) resequence the genomes of ~1000 individuals across at least five sites in historical and modern eras to identify loci targeted by spatially divergent or temporal selection, and 3) measure morphology and growth to test for the functional importance of genomic variation. The project will focus on historical (1907-1909) samples held by the Smithsonian Institution and modern samples collected in collaboration with Silliman University. The ethanol preservation by the R/V Albatross is a unique scientific accident that provides excellent DNA preservation over the last century. 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

There have been substantial advancements in deep learning (DL) methods over the past decade. However, DL methods are not robust against either naturally occurring misleading features or problems purposefully by adversaries. These vulnerabilities originate from DL’s reliance on observational data to learn the relationship between inputs and outputs. Consequently, DL models may encounter pitfalls, including false (spurious) input-output relationships in the training data, and adversarial attacks designed to manipulate the input data to distort the resulting output. A principled direction to eliminate these vulnerabilities requires going beyond learning from low-level, superficial relationships in the data to using instead high-level concepts and insights into causality between the links. This will conceptualize the data to guide and improve robustness of DL models. In this project, we approach this problem by developing a “robustifier” framework to improve robustness in a principled way for any DL model, against natural and adversarial factors, while also handling uncertainty in the data. The robustifier under this framework first probes a DL model with a graph of random variables, where each variable represents a higher-level concept (e.g., “color”, “shape”, etc.). The data from the graph can then be used to enhance the robustness of the DL model by performing causal inference to neutralize confounding concepts, and produce robust, uncertainty-aware prediction. Methods developed in this project will be applied in visual recognition to improve capabilities of perception models, and in healthcare to enhance robustness in analyzing patient status. This project will build formal connections between DL and probabilistic graphical models (PGM), two major machine learning (ML) paradigms with complementary strengths. It will advance the state of knowledge in ML through formulating a new Bayesian deep robustifier framework that unifies DL and PGM by: (1) developing “Bayesian deep neutralizers”, which fundamentally neutralize naturally occurring spurious features by first using PGM to infer high-level concepts from DL representations, performing causal inference to neutralize confounding (spurious) concepts, and then producing robust, uncertainty-aware prediction; (2) developing “Bayesian deep defenders”, which fundamentally defend against adversarial attack by using PGM to infer high-level concepts robustly, even from attacked DL representations, and at test time, adapting to diverse attacks unseen during training; (3) designing concrete methods to robustify DL models with minimal performance sacrifice and computational overhead, especially for large models (e.g., GPT-4); and, (4) investigating theoretical guarantees and providing valuable insight for future research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Learning from data is performed by iterative algorithms throughout statistics and machine learning. Iterative learning algorithms find models that best fit the labeled training data, in order to make predictions on unseen and unlabeled data. These iterative algorithms are run in computers until an optimality criterion is met or exhausting computational resources. Empirical evidence suggests that terminating algorithms early, before convergence, enhances prediction performance on unseen data in certain learning scenarios. The project aims to develop theory to explain this early-stopping phenomenon, as well as practical methodologies to determine the optimal early iteration for best predictions on unseen data and saving computational resources. The research will involve students at both undergraduate and graduate levels. Modern estimators in statistics and machine learning are ubiquitously defined as solutions to optimization problems, whether convex or nonconvex. These optimization problems are solved iteratively using gradient descent and its accelerated variants, or proximal iterative schemes if the objective function is non-smooth. On the other hand, inferential methodologies such as debiasing, the construction of confidence intervals in regression models or estimation of the generalization error have so far been developed assuming algorithm convergence. This research will bridge this gap and develop inferential methodologies for algorithm iterates, including at a constant number of iterations or far from convergence. The project aims to develop estimators of the generalization error of the iterates, with application to early stopping to minimize population error. The project further plans to equip iterative algorithms with confidence intervals and hypothesis tests valid throughout the trajectory, allowing practitioners to perform hypothesis rejections and discoveries at early iterations, without relying on algorithm convergence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This conference proposal requests support for a Graduate Student Symposium (GSS) linked with the North American Wind Energy Academy (NAWEA)/WindTech Conference, with the Symposium scheduled for October 29, 2024. The 2024 GSS, occurring one day prior to the NAWEA/WindTech Conference, serves as an educational forum tailored for graduate and undergraduate students interested in wind energy research and careers. Beyond providing a platform for academic exchange, it endeavors to tackle the pressing necessity of bolstering science communication competencies among students within this domain. This imperative arises from the prevailing societal skepticism, and various challenges encountered in the field of wind energy and broader climate change sciences. The theme of the 2024 GSS is Wind Energy Workforce Engagement and Training. It aims to provide an enriching and interactive platform for undergraduate and graduate students, helping them hone their communication and networking skills alongside other students and leaders in the fields of wind energy technology, research, and innovation. The inclusion of tutorials on science communication, facilitated by experts from the Alan Alda Center for Communicating Science, will equip students with the necessary skills to effectively convey their research findings to diverse audiences. The GSS builds on, and complements various collaborative efforts (i.e., projects, conferences, and workshops) by the local partner organizations who have been working together since 2021 to ensure a successful transition to offshore wind energy for the State of New Jersey, with regional conferences held at Rutgers University and at Rowan University. The 2024 GSS will primarily serve as a regional event, offering students from institutions in New Jersey, southeastern Pennsylvania, Delaware, Maryland, and southern New York the opportunity to drive to and participate in this single-day event. 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.