University Of Massachusetts Amherst

NIH Research Projects · FY 2025 · 2025-08

Project Summary In the U.S., over one million individuals live with severe visual impairments, a figure projected to double in the next 30 years. Guide dogs offer independent, natural, and safe mobility assistance, allowing handlers to walk at speeds comparable to sighted peers and navigate dynamic environments safely. However, only 2% of this population has access to guide dogs, primarily due to the limited supply of these highly trained animals. The significant cost and time required to train and deploy guide dogs (approximately $50,000 USD and 2 years) and their relatively short working span (less than 10 years) are major barriers. Additionally, guide dog handlers must provide continuous care, including medical attention, feeding, and daily exercise. Despite their compelling benefits, these challenges make guide dogs a less scalable and sustainable solution for the wider visually impaired community. Motivated by recent progress in quadruped robots and their potential for mass production and long-term sustainability, we are determined to create a practical guide-dog robot as an additional solution for navigation assistance for blind or low-vision (BLV) people. We are dedicated to aligning our development process with the authentic needs of end-users, adopting a human-centered design philosophy that involves key stakeholders throughout every stage of the process, including: 1) defining the robot’s specifications, 2) developing algorithms and integrating them into a single robotic system, and 3) conducting evaluations, identifying unforeseen issues, and iteratively refining the hardware and algorithms. Our proposed research is grounded in our comprehensive qualitative study, which included semi-structured interviews with 23 guide-dog handlers, five trainers, seven ob- servation sessions, and two blindfolded guide-dog walking experiences. Our prior work revealed the limitations of existing quadruped robots for the BLV population and pinpointed critical areas for development. Rooted in our extensive understanding of the interaction between guide dogs and their handlers, we offer technological inno- vations such as: 1) new quadruped robot hardware developed through a human-centered approach, featuring compactness, portability, extended operation, and multi-modal sensing (e.g., tactile, audio, and vision), 2) a novel co-optimization framework for robot hardware and controllers to create a robotic navigation assistant optimally designed for BLV people, and 3) a robust navigation algorithm that adapts to scene variations by creating a novel self-supervised learning framework. Our core innovation lies in introducing a human-centered development approach to creating a guide-dog robot, addressing the significant open problem spanning hardware configuration to navigation algorithm development. The developed technology will be consolidated into a single quadruped robot, which will undergo rigorous evalua- tions by guide-dog handlers and white cane users. The successful completion of this project will yield an effective navigation system for BLV individuals and trigger a paradigm shift in the field of guide-dog robot research.

NSF Awards · FY 2025 · 2025-08

The goal of this project is to understand how microbial activity in the root zone of the crop plant sorghum impacts the plant’s ability to grow and utilize nitrogen. Results of this work may be used to engineer plant-associating microbial communities to enhance crop yield, plant hardiness, and for efficient cultivation practices. This project will enhance U.S. national security and economic competitiveness by analyzing a promising system for improving crop yield and crop resiliency. This project will also train and help build the future STEM workforce by providing scientific education and research training to early career scientists and students. This project aims to elucidate homoserine lactone-mediated interbacterial signaling in the rhizosphere and its effects on bacterial community structure and function that impact nitrogen cycling in the soil to support plant growth and health. This project will study a sorghum plant system with growth-promoting bacterial communities. Experiments will utilize a multi-omics approach, bacterial strains with controllable signaling, and synthetic bacterial communities to study these microbial interactions in the root zone of sorghum during its growth. This project combines approaches from synthetic biology and systems biology to improve understanding of quorum sensing in the rhizosphere. Key research objectives are to understand the response of the root-associated microbial communities to diverse homoserine lactone signals, decipher the ability for rhizobacteria to communicate via diverse homoserine lactones, and characterize the effects of this interbacterial communication in the rhizosphere for plant growth conditions. This knowledge will provide the foundation for studying and predicting the breadth of bacterial intercellular communication in the rhizosphere and its effects on the rhizomicrobiome, plant growth, and nutrient availability in the soil. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Massive amounts of data are collected or generated in industries such as finance and healthcare; in scientific fields such as genomics and high-energy physics; and in technological applications such as cloud computing and training machine learning models. This has motivated the design and analysis of data stream algorithms, a highly productive research area within computer science. The focus is typically on algorithms with provable guarantees regarding their running time, memory usage, and accuracy. However, the guarantees established in previous work often require explicit and implicit assumptions about how the algorithms will be applied. Many existing algorithms become ineffective when these assumptions do not hold. This project aims to design resilient data stream algorithms that are less dependent on such assumptions and more reliable in practice. It will also support curriculum development and the training of graduate students. The project tackles several key challenges in the theory of data stream algorithms. First, it aims to develop adversarially robust algorithms that remain effective even when inputs are adaptively chosen in response to algorithmic behavior. This situation naturally arises in interactive data analysis. Second, the project investigates parameter-free and non-adaptive algorithms that do not rely on prior knowledge of problem-specific parameters, avoiding the costly, generic technique of multiple instantiations. Third, it examines when the performance of randomized streaming methods can be matched or approximated by pseudo-deterministic or deterministic algorithms. Such algorithms provide reproducibility, which is important in experimental science. Finally, the project seeks data stream algorithms that address fault-tolerance, ensuring correctness amid hardware or communication errors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Data visualization is a powerful tool commonly used by practitioners to understand, analyze, and identify interesting patterns in their data. However, due to limitations in human perception and issues of efficiency in data processing and rendering, it has become standard practice for data analysts to work with only a sampled subset of their data. However, deriving samples that preserve key aspects of the underlying data is not straightforward. Analysts looking at a sample should be able to draw the same conclusions as they would from the complete original data. Unfortunately, this is often not the case. For example, while uniform random sampling is the most accessible method to data analysts in practice, it frequently fails to represent outliers and patterns, leading to missed critical insights and distorted perceptions of the underlying data trends. This is not a rare occurrence, as interesting insights often lie within outlier behaviors, edge cases, or uncommon aspects of the data. This is detrimental to visual data analysis: sampling choices can critically impact the accuracy and efficiency of common visual analytics tasks, and, when chosen poorly, can lead analysts to incorrect conclusions. This project addresses the gap at the core of this issue: data systems do not model human perception, and sampling algorithms do not optimize for it. Explicitly accounting for human perception in samples can create more effective visualizations and reduce the risk of distorting patterns and trends in the data. Considering that the data analytics market generated revenue of almost US$23 Billion in 2019 and is projected to reach US$132 Billion by 2026, improvements in the visual analytics domain are positioned to have a tremendous impact on the economy. As decisions often rely on visual analysis, improved accuracy in inferences will lead to better decision-making and data communication, which can also support data-driven decision-making by policy-makers and improve the interpretability and credibility of data analyses for the general public. This project will augment data management systems with explicit models of perceptual features of the data, and will contribute algorithms that use perceptual models in data selection for visualization tasks. The investigators will explore the suitability of established saliency models, commonly used in computer vision, that predict areas of a visualization that attract viewers' attention as a proxy of perception. The project will make the following intellectual contributions: (1) A prototype perception-augmented database, with novel representations for perceptual saliency data and optimized storage strategies to minimize overhead. (2) Novel sampling algorithms that use perceptual models toward their data selection objectives, including optimizations for on-the-fly sample augmentation and robustness across a range of visualization tasks. (3) Perception-aware compressed representations of data that can be used towards approximate visualizations for increased efficiency and real-time performance. (4) Novel measures for perceptual quality in samples. These advances will enable data scientists to draw accurate insights from visual analysis, within an efficient and robust analytics pipeline. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Modern datasets are massive, posing a serious challenge to processing and storage. A principled approach for dealing with massive (metric) data is distance sketching. A distance sketch of a metric space (or a graph) is a compact structure that approximately preserves the distances between every pair of points (or vertices). The two most basic compactness measures are the size (i.e., the number of edges) and weight (i.e., the total edge weights) of the sketch. The compactness makes distance sketching a powerful primitive for countless algorithmic tasks. The most basic distance sketch is a spanner, a graph preserving all pairwise distances. Spanners have found many applications over the years, for example, in wireless and sensor networks and distributed computing. Despite its compactness, the structure of a spanner could still be too complex for a wide range of applications. Two other important structural sketches include tree cover and locality-sensitive ordering, composed of a few trees and paths, respectively. Trees and paths are arguably the simplest types of graphs, and hence, structural distance sketches open up a much broader range of applications. The results in this project will impact applications such navigation maps and routing on wireless networks. The project will also train undergraduate and graduate students. This project proposes to study three research directions to expand the understanding of distance sketching on three fronts. First, the project aims to design new algorithms for constructing instance-optimal spanners, which are optimal with respect to every input instance. In contrast, known constructions of distance sketches are only optimal existentially. Second, the project will study spanning tree covers and locality-sensitive orderings, aiming to achieve the same theoretical guarantees of spanners; existing constructions of tree covers and locality-sensitive orderings are inferior to spanners despite their superior structural simplicity. Third, the project seeks to design new algorithms for efficiently constructing distance sketches in different models of computation; many state-of-the-art constructions are very slow, even in the most basic models of computation, including the static or dynamic settings. The techniques expected to develop in this proposal revolve around several different areas: graph theory, discrete geometry, dynamic algorithms, and approximation algorithms, and therefore, this project will potentially impact these areas as well. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY – Peroxisomal impacts on cellular quality control Career Goals: The candidate’s long-term career goal is to become a university professor, where she can combine research in cellular quality control with educating the next generation of scientists and increasing diversity in the academy. She has designed her postdoctoral training to complement her graduate transcriptomics experience with experience in proteomics, biochemistry, and cell biology to address her research questions while becoming a well-rounded biologist. Training Environment: Rice University has an excellent training environment that supports interdisciplinary and inter-institutional research with other Texas Medical Center institutions, including full access to shared state-of-the-art equipment, while maintaining an intimate research setting inclusive of pedagogy, professional communication, and job market preparedness trainings. Dr. Bonnie Bartel, the project mentor, fosters a supportive environment that promotes scientific and professional development. She has an extensive publication and training record at the forefront of discoveries in phytohormones, microRNAs, and peroxisomes. Research: The goal of this project is to leverage the recent finding that functional peroxisomes are targeted for destruction by overzealous autophagy machinery when the peroxisomal chaperone and protease protein, LON2, is dysfunctional. This finding suggests the hypothesis that peroxisomal signals that promote pexophagy are degraded or refolded by LON2. The proposed experiments are designed to uncover mechanisms controlling peroxisomal turnover, to determine how this turnover facilitates overall cellular health, and to identify how peroxisome dysfunction signals to other organelles. Several interconnected approaches will be employed to achieve these goals. First, LON2 substrates will be identified and characterized, including chaperone, protease, and pexophagy-promoting substrates. Second, the proteomic landscape of cells housing dysfunctional peroxisomes will be surveyed to identify differentially accumulated proteins and associated alterations in other essential organelles. Third, transcriptomes will be analyzed to identify retrograde signaling from the peroxisomes to the nucleus, including the transcriptional responses that are induced when peroxisomes are compromised. These studies will incorporate predictive modeling and provide insight into the signaling components regulating pexophagy and cellular signaling responses that ensue when pexophagy is heightened or prevented. As many aspects of peroxisomal function are widely conserved, these experiments exploiting the advantages of the Arabidopsis model will likely provide insights useful for understanding the etiology of human peroxisome biogenesis disorders.

NSF Awards · FY 2025 · 2025-07

Despite recent advances in embodied artificial intelligence (AI), achieving general-purpose intelligence (i.e., human-level intelligence) , where AI agents can flexibly perform like humans across a wide range of tasks across diverse environments, remains a fundamental challenge. Two persistent gaps hinder progress: (1) limited understanding of physical space, which constrains agents' ability to efficiently learn broad physical skills; and, (2) difficulty in open-ended social interactions, which limits multi-person (and agent) interaction scenarios. This project aims to bridge these gaps by developing embodied generalist agents capable of perceiving, reasoning, and interacting effectively with both the physical world and other agents in dynamic, evolving environments. In parallel, the project will support course development, student mentoring, and outreach activities, integrating foundational research with experiential learning to prepare the next generation of U.S. students for leadership in academia and industry. This project will focus on building robust physical and social world models for embodied agents by integrating physics engines with generative foundation models. The technical agenda includes: (1) developing paradigms that allow robotic agents to autonomously propose new tasks, generate corresponding environments, and acquire novel physical interaction skills through self-supervised simulation; (2) creating methods that combine open-world knowledge from foundation models with spatiotemporal memory and model-based planning, enabling agents to interpret goals, intentions, and social cues for effective collaboration in real-world social contexts; and (3) designing systems that co-optimize world model architectures and hardware to achieve real-time performance. Collectively, this project will establish a foundational platform for embodied AI and drive progress toward embodied general intelligence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Decoding the structures and properties of unknown molecules through analyzing the wavelengths of their electromagnetic properties is known as spectral analysis. Spectral analysis is crucial for scientific discovery and practical applications in various fields, including material manufacture, drug design, food safety, explosive detection, and non-invasive diagnosis. Spectral analysis offers rapid, sensitive, non-destructive, and cost-effective identification of unknown molecules through their characteristic numerical signals and outperforms traditional chemical analysis. However, the process of translating numerical signals into molecular structures is currently resource-intensive and not user-friendly because it often requires extensive trial-and-error and specialized training. This project aims to revolutionize spectral analysis using state-of-the-art artificial intelligence (AI) in an automatic, accelerated, and accurate fashion. This project will treat spectral signals and molecular structures as two different "languages". Models developed in this project will automatically transform spectral signals and molecular structures into descriptions of molecules in the two languages and enable rapid conversion between each description based on advanced AI-powered language translation tools. The resulting universal toolkit will simplify and streamline spectral analysis in practical scenarios and benefit applications in scientific research, national healthcare, national security, educational activities, and other domains. The primary intellectual contribution of this project is the development of a novel chemistry-informed, multi-modal, powerful, and flexible deep learning framework to realize automatic, accelerated, and accurate end-to-end spectrum-to-structure translations. Investigators will adapt and leverage foundation models from the frontier of AI, especially pre-trained large language models (LLMs) like Transformers. This project will design an encoder-decoder architecture, where the spectrum encoder converts the input numerical spectral signals (e.g., wavenumber-absorbance pairs from infrared (IR) spectra and chemical shift-intensity pairs from nuclear magnetic resonance (NMR) spectra) into context vectors, and the structure decoder transforms these context vectors into the output molecular fingerprint containing two-dimensional (2D) topological structures and three-dimensional (3D) spatial conformations of target molecules. The encoder and decoder will be pre-trained on high-quality data sets of molecular spectra from experimental measurements and theoretical calculations and fine-tuned to boost the performance. The project will accomplish three fundamental thrusts, including (a) developing natural-language representations for both spectral signals and molecular structures that align with the architecture of foundation models, (b) designing multi-modal learning frameworks to leverage pre-trained foundation models as the backbone approaches and inject chemical constraints as domain-specific knowledge for an end-to-end spectrum-to-structure translation, and (c) tailoring our multi-modal learning frameworks using chemistry- and data-informed schemes to adapt to the practical instrumental analysis pipeline for applications in real-life scenarios. This project will demonstrate significance across a broad range of disciplines where spectral analysis is essential for identifying or recognizing single molecules or molecular mixtures, including chemistry, biology, medicines, pharmacology, astronomy, security, materials science, food science, and environmental science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Many industrial processes require separating a chemical mixture into its components. For example, crude oil is separated into various fractions to give gasoline, lubricants, asphalt, etc. Alcohol and water are separated to produce spirits and bioethanol for fuel. Such separation processes are highly energy-intensive. Energy efficiency may be improved by using adsorptive or membrane-based separations using nanoporous materials, which are materials with pores that are a few nanometers wide. But mixtures confined within nanopores behave differently from unconfined mixtures. This project will identify general principles governing the behavior of mixtures within confined environments. The project will combine experiments, computer modeling, and machine learning to predict mixture behavior in nanoporous materials. The results will help improve the energy efficiency of chemical separation processes. Additional benefits to society will come from teaching data science to engineering students, as well as outreach activities to engage K-12 students and teachers. A detailed understanding of adsorption equilibrium in chemical mixtures is critical to design optimal sorbents for separation processes. However, acquiring mixture adsorption data by experiments or computations is time- and resource-intensive. The challenge becomes severe for multi-component mixtures because the amount of data needed increases steeply as the number of components in the mixture increases. Therefore mixture adsorption is often predicted from single-component adsorption data, relying on models such as the ideal adsorbed solution theory. Yet, such models are known to fail for many non-ideal mixtures. This project will develop a systematic understanding of non-ideal adsorption in zeolites using the framework of the real-adsorbed solution theory. To accomplish this objective, the research team will study aqueous polar mixtures confined within zeolite nanopores using atomistic simulations and experimental techniques. The team will examine the effect of zeolite structure, and the impact of hydrophilic defects and cations, on the phase behavior of mixtures. These insights will be used to construct a deep learning model that can predict adsorbed-phase activity coefficients from the topology and geometry of zeolite structures. The ultimate goal of this project is to create an expert system that can predict a wide range of nonideal adsorption behaviors under confinement. The project will develop curricular course modules on machine learning at the undergraduate level, promote undergraduate research, and conduct outreach activities at the pre-college level. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The Algebraic Geometry Northeastern Series (AGNES) is a series of biannual conferences in the field of algebraic geometry. The conference is hosted on a rotating basis by an association of universities in the Northeast region. This award supports six AGNES conferences, which will be held at Dartmouth College on November 8-10, 2024, at Rutgers University in Spring 2025, at the University of Massachusetts, Amherst in Fall 2025, at Stony Brook University in Spring 2026, at Brown University in Fall 2026, and at the University of Pennsylvania in Spring 2027. Each AGNES conference has two goals. First, each conference promotes the dissemination of cutting-edge research in mathematics. The centerpiece of each conference is a series of research lectures by top mathematicians; there are also educational talks for graduate students and events which promote new collaborations or development of peer relationships. Algebraic geometry is a field in the mathematical sciences concerned with solution sets of polynomial equations. It has deep connections to many other areas of pure mathematics, such as topology, arithmetic, number theory, differential geometry, dynamical systems, and homological algebra. At the same time algebraic geometry has found important applications in many subdisciplines of applied mathematics, including cryptography, complexity theory, mathematical biology, and computer vision. The scientific scope of AGNES is greatly enriched by lectures from neighboring mathematical subjects, such as arithmetic geometry, dynamics, complex geometry, and computational geometry. Further information about conference events can be found at the website: http://www.agneshome.org/ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-07

Project Summary Many complex cellular processes from muscle contraction to vesicular transport are driven, not by a single myosin motor, but by the coordinated action of teams of motors. A key unanswered question is how do single motors couple and coordinate their activity in ensembles to give rise to these complex cellular processes? This knowledge gap is preventing a detailed molecular understanding of a host of intracellular processes, as well as the development of therapies for diseases that result from the dysfunction of these processes. A widely held view is that teams of myosin molecules coordinate their activity through their ability to sense the magnitude and direction of an applied load. However, this idea has not been fully, nor directly, tested in part because myosin’s biochemical and mechanical transitions occur faster than most existing technologies can resolve. Thus, it is still not clear which step or steps in the cross-bridge cycle are load sensitive, and what the specific nature of these load-sensitivities are. To gain this knowledge I have developed novel laser trap assay methods and analyses with the necessary spatial and time resolution to directly characterize the load-sensitive steps in myosin’s mechanochemical cycle. These data will be used to develop molecular models of single molecule behavior under a variety of conditions. I then use the mini-ensemble laser trap assay to directly characterize how ensembles of motors work together to generate force and motion, providing the first direct test of our models and the information needed to refine and improve the models. To date I have focused primarily on muscle myosin II, but recently I have begun taking similar approach to characterize the behavior of the processive motor, myosin Va. And I now plan to expand this approach to investigate the two other classes of myosin V motors (Vb and Vc) and myosin III to better understand the complex processes that they drive within the cell. If this work is successful the information will transform our understanding of molecular motors by providing the first complete molecular explanation of how these prototypical molecular motors coordinate their activity. This will then provide crucial insight into how these motors drive many complex cellular processes, and by doing so will reveal novel molecular targets for more effective therapies for a host of myosin associated diseases.

NSF Awards · FY 2025 · 2025-06

Artificial intelligence (AI) built upon biological input can emulate downstream biocomputing and send feedback to alter biological activities, offering closed-loop hybrid intelligence (HI) framework to connect biological and physical computing systems. This research project aims to establish closed-loop hybrid intelligence by co-designing high-resolution neuromodulation and neuromorphic devices that can apply to cultured neurons and organoids. The outcome of this research will result in engineering tools and methods that may make an impact in AI hardware, human-machine co-learning, brain-machine interfaces, and disease modeling. The educational objectives of this project are aimed at training and inspiring young engineers and scientists who are equipped with the multidisciplinary background required to help define the future trajectory of AI, brain sciences, and advanced manufacturing. The completion of this project will: 1) advance transformative AI technologies capable of closed-loop cell interfacing with recording, processing, and controlling modalities; 2) educate undergraduate and graduate researchers to contribute to the nation’s workforce needs in AI, advanced computing, and brain-computer interface; 3) contribute to K-12 education through weekend seminars and mentoring student-teacher pairs; and 4) promote public awareness of AI technologies towards biological/bio-inspired computing. The research objective of this project is to combine high-resolution optoelectronic neuromodulation and neuromorphic hardware to demonstrate closed-loop HI in biological neural networks and organoid models. The resulting HI system will showcase the power of optogenetic-neuromorphic co-design capable of closed-loop, energy-efficient, and high-accuracy cell interfacing, and suggest its promise in developing optogenetic interventions in organoid models. Three major contributions of this research project include: 1) optogenetic cell interfaces composed of close-packed light-emitting diodes and microelectrodes, which are particularly co-designed with neuromorphic cell interfaces towards closed-loop HI; 2) neuromorphic cell interfaces composed of close-packed photomemristors and microelectrodes, which are co-designed with optogenetic cell interfaces towards closed-loop HI; 3) demonstration of the closed-loop HI in cultured neural networks and organoid models, with perspective applications for developing effective optogenetic interventions. This project is funded by the Foundations of Emerging Technologies Program and the Electronics, Photonics and Magnetic Devices Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This Faculty Early Career Development (CAREER) award supports research seeking to advance our fundamental understanding of how people learn to walk more efficiently with wearable robotic exoskeletons. Wearable robots have the potential to enhance mobility for a wide range of users, including individuals with neurological or musculoskeletal impairments, older adults, and able-bodied individuals. Unlike traditional walking aids, robotic exoskeletons can adapt their mechanical assistance to meet each user's unique needs and adjust over time to optimize gait efficiency. However, current personalization approaches focus primarily on adapting the robot without fully considering the human user's natural ability to learn and adapt. This research project attempts to address this critical gap by investigating novel ways to guide and leverage human neuromotor learning to further enhance the performance of gait-assistive exoskeletons. This award will also support training the next generation of researchers to develop robotic systems that enhance human locomotion through three key activities: (1) hosting a regional locomotion research symposium for students, (2) engaging students in hands-on human-robot physical interaction projects, and (3) raising public awareness of the potential of robotic technology to improve mobility. The overall goal of this CAREER award is to develop gait-assistive robotic exoskeletons that not only adapt their mechanical assistance to the user but also actively "coach" the user to work with the device to walk more efficiently. By influencing both human adaptation and its own behavior, an exoskeleton can optimize cooperative learning between itself and the user, leading to faster and greater improvements in walking efficiency for diverse users. To achieve this goal, the PI's team will conduct a series of foundational experiments to attempt to: (1) develop new metrics to assess the user's proficiency in operating gait-assistive exoskeletons; (2) determine how the characteristics of exoskeleton assistance adaptation algorithms influence user learning; and (3) identify effective multimodal interaction methods—such as auditory and/or vibrotactile cues—to guide and accelerate (i.e., "coach") user learning. The results of these experiments intend to ultimately inform development of novel adaptive exoskeleton control algorithms that incorporate multimodal "coaching" signals to enhance human-robot cooperative learning and maximize gait efficiency. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This I-Corps project is based on the development of a material for capturing carbon dioxide (CO₂) emissions from industrial and power plant sources. Billions of tons of CO₂ are emitted globally each year, and there is a need for scalable, cost-effective capture technologies. Current solutions are often energy-intensive, toxic, and/or prohibitively expensive. This technology uses a bio-degradable, non-toxic material derived from renewable feedstocks such as wood and agricultural waste. Commercial applications include the sequestration of exhaust gases and passive CO₂ capture from the atmosphere in industries such as energy production, cement and concrete manufacturing, and air purification, with broader applications in filtration and thermal insulation. The material’s lightweight, porous structure enables efficient CO₂ adsorption, while its simple manufacturing method allows for shaping into a variety of forms for many applications. This technology may provide a low-cost, environmentally responsible material that is easily produced, regenerated, and, at the end of its lifecycle, recycled, mulched as fertilizer or incorporated into concrete. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of nanocellulose xerogels for carbon dioxide (CO₂) capture. The material is produced from renewable feedstocks such as wood and agricultural waste using a combination of freeze–thaw toughening, solvent exchange, and ambient drying to produce monolithic structures with extremely low density and high surface area. In addition, the material is functionalized with amino acids and peptides to enhance CO₂ affinity without the need for corrosive or toxic chemicals. This structural and chemical synergy enables reversible CO₂ adsorption, efficient regeneration, and a long operational lifespan. Unlike traditional amine-based technologies, this material does not require special reactors, uses no hazardous solvents, and avoids high energy input during manufacturing. Laboratory demonstrations show competitive CO₂ uptake (~1 mmol/g), with additional potential in passive capture and closed-environment applications. This material may have applications beyond CO₂ capture, including superabsorbents, air and water filtration, oil remediation, and thermal insulation, and can be safely composted or used as fertilizer at the end of its use cycle. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-06

Project Summary Phage-Polymer Nanoparticles for Treatment of Antibiotic-Recalcitrant Wound Biofilm Infections Wound biofilm infections are associated with significant morbidity, mortality, and cost, and can be refractory to conventional antibiotic treatment. The proposed research will create new polymer-phage conjugates to treat wound infections. Phage therapy provides a self-amplifying strategy to combat bacterial infections. However, phages do not efficiently penetrate into the dense biofilm matrix, limiting the efficacy of phage therapies for treating biofilm infections. These issues will be addressed through integration of nanomaterials developed by Rotello with the in vitro, in vivo, and clinical expertise of Patel in phage therapy. In published studies, Rotello and Patel demonstrated that engineered polymers can encapsulate phages to generate non-covalent protein-phage nanoparticles (PPNs) that retain infectivity and feature enhanced efficacy against biofilms relative to free phage in vitro and in vivo. In the proposed research, Rotello will develop new homopolymers and block copolymers to generate polymer-phage conjugates, focusing on Phage K that Patel has shown has cross-strain and cross-species activity. These polymers will use a cationic block to electrostatically anchor the polymer to phages. One PPN family will systematically vary the hydrophobicity of the homopolymer to optimize biofilm penetration and eradication. The second PPN polymer will feature an exterior block with charge-switchable functionality that will go from anionic (non-interacting) to cationic (biofilm penetrating) at biofilm pH, providing targeted delivery of PPN. Conjugates will be screened in vitro against methicillin-resistant Staphylococcus aureus (MRSA) planktonic bacteria and biofilms. Effective PPNs will be incorporated into hydrogels, with hydrogel porosity used to provide controlled PPN delivery. Conjugates effective in vitro will be screened in a realistic murine wound biofilm by Rotello. Patel will then perform pre-clinical studies of the antimicrobial and wound healing efficacy of PPNs, supported by histopathology. Aim 1: Rotello will synthesize homo- and block copolymers and non-covalently conjugate them to phages specific against MRSA study isolates. Infectivity of the conjugates will be quantified and will be tested for biofilm penetration and eradication. Patel will establish cross-strain and cross-species activities of phages. Aim 2. Rotello will incorporate effective PPNs into hydrogels to provide wound dressings with controlled release of phage. Our expectation is that control of hydrogel pore size will regulate PPN release, providing optimal phage activity. Aim 3. Rotello and Patel will use murine wound biofilm models to test PPN-hydrogel wound dressings. These studies will combine parametric pilot experiments using luminescent MRSA by Rotello with full pre-clinical evaluation by Patel. Efficacy in these models will be quantified by decreased bacterial counts, enhanced wound healing, diminished purulence, and decreased inflammation as outcomes.

NSF Awards · FY 2025 · 2025-06

Jianhan Chen of the University of Massachusetts at Amherst is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to to study the driving forces and molecular mechanisms of RNA phase transitions using coarse-grained models. Flexible biological macromolecules, such as disordered proteins and flexible RNAs, have recently been discovered to undergo spontaneous phase separation and form biomolecular condensates that play fundamental roles in a myriad of cellular functions from stress response to cellular signaling. Chen and his research group will develop a new intermediate resolution model for condensates of RNA (iConRNA) and collaborate with experimental labs to accurately calculate the phase diagram of RNAs and study how the interplay of various molecular forces control phase behaviors. These efforts will meet the urgent need for efficient computer models that can simulate the phase transitions of these dynamic molecules in studies of biomolecular condensates. Chen will integrate the latest advances in biomolecular condensates into courses at University of Massachusetts (UMass), train undergraduate and graduate students in interdisciplinary research, and contribute to broadening participation in STEM education and research through the Eureka! program at UMass. Jianhan Chen will develop a coarse-grained (CG) molecular modeling framework for efficient simulation of the spontaneous phase transitions of flexible RNAs as well as heterotypic protein/RNA phase separation. The proposed iConRNA model will represent each nucleotide using 6 or 7 beads, and includes explicit base stacking, base pairing, Debye–Hückel electrostatics, and Lennard-Jones potentials. Parameterized using atomistic simulations and experimental data, iConRNA model will be designed to recapitulate transient local and long-range structure features of model RNAs, fold several small RNAs, and correctly capture the length and sequence dependence of the phase separation of various RNAs. Specific research objectives of this proposal will be to: 1) accurately model the temperature and magnesium ion-dependence of RNA structure and phase separation; 2) elucidate how the complex interplay of intrinsic structural propensities and intermolecular interactions govern the mechanism of RNA phase separation and condensate material properties; and 3) develop a self-consistent CG framework for modeling heterotypic protein/RNA phase separation. Integrated with experimental studies, these efforts will uncover crucial new molecular details and mechanisms underlying the complex magnesium and temperature dependence of RNA phase transitions. In parallel, Chen will distribute the CG models through the open-source OpenMM package to enable the community to study biomolecular condensates in biology and materials engineering. The project will train several undergraduate and graduate students in the interdisciplinary field of computational biophysics and support efforts to broaden the participation of high-school students and the general public in STEM education and 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 2025 · 2025-06

This project aims to improve our understanding of how to answer questions about one’s own health and wellbeing using self-tracked data, known as self-experimentation, and to provide better designs of technology to support the practice. Self-experimentation has been gaining traction due to the $54 billion health tracking industry. However, there is a mismatch between what technology currently provides (for example, step counts and sleep scores) and what people expect from it, which is personalized health insights and recommendations. This project seeks to understand and enhance the agency of individuals to make decisions about their health and wellbeing by supporting explorations such as, “Is coffee or milk the trigger for my irritable bowel syndrome symptoms? Does taking aspirin affect my menstrual flow? Will exercising in the evening improve my sleep quality?” This project’s designs will lead to more personalized feedback for health concerns such as physical activity, diet, and sleep. The research will thus support current efforts to manage chronic diseases in the United States, for example, obesity, diabetes, and heart disease. In addition to engaging with different types of communities, this project will advance education by improving how students are taught to use the scientific approach to answer questions about their own health through self-experimentation. This project will transform how we approach technology to support personal health management. It will expand our understanding of self-experimentation and significantly impact technology design to support and empower individuals in managing their health and wellbeing. Specifically, the research will contribute a new theory in the form of the life-cycle model of self-experimentation, its stages along with their associated properties and barriers. The project will also provide empirical evidence of design patterns addressing known challenges, such as improving experiment selection through novel interface design patterns to support goal elicitation and personalized data representations. Other known challenges include improving robustness of experimentation data through tailored experiment designs and learning-based experimentation support, and personalizing communication of findings through tailored visualizations and post-experiment paths of action. By systematically understanding and addressing the barriers and needs for effective self-experimentation this research will reshape self-experimentation from a challenging and unreliable tool available only to people with advanced knowledge and significant resources to a well-understood practice accessible to individuals throughout their health tracking journey. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Understanding how marine life will respond to changing environmental conditions is critical for ocean management and the wellbeing of people that rely upon marine resources through fisheries. A key problem in this area is understanding how environmental change affects the way that species obtain food. Rising ocean temperatures may increase the food requirements of predatory animals which can cause prey populations to decline. Alternatively, warming can cause multiple predators to compete and interfere with each other, reducing effects on prey. This CAREER project addresses this problem by focusing on abundant native and invasive predatory crabs and their consumption of prey blue mussels in the Gulf of Maine, which is among the fastest warming habitats on the planet. This project measures how temperature affects the physiology of the focal species and how they interact with each other using a series of laboratory experiments, then uses mathematical models to calculate the impacts of temperature on the relative abundance of predators and prey. The educational components of this project include the formation of two working groups that pair early career scientists (graduate students and postdoctoral fellows) with middle and late career mentors to train on team-based approaches to science. The investigator is developing data science workshops and laboratory modules to enhance undergraduate education. Results of this research inform the management of marine resources such as shellfish and broadly contribute to our understanding of how marine life will respond to changing ocean conditions. Predicting the ecological consequences of changing environmental conditions will require an understanding of how temperature affects species interactions such as predation and competition. Further complicating this endeavor is the fact that communities in nature are often composed of multiple interacting predators that can produce non-linear and complex effects on prey species and overall community composition. This project 1) uses an integrated approach, combining single species physiological assays to compare the relative thermal performance of multiple predatory marine invertebrates (native and invasive) and prey blue mussels from the Gulf of Maine; 2) measures species interactions within the community using mesocosm experiments, quantifying temperature dependent predation, intraguild predation, and competition; and then 3) uses this information to construct numerical models to predict the effects of temperature on the biomass dynamics of both predators and prey. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-06

MIRA Abstract This proposed research explores the complex phenomenon of cachexia, a condition characterized by severe weight loss and muscle wasting that occurs in various terminal illnesses such as sepsis, cancer, and heart failure. Despite advancements in addressing underlying chronic illness, there remains a critical need to determine the mechanisms that can reverse lean mass losses during cachexia, which will inform the development of effective therapeutics for recovery. During endurance flight and fasting, migratory birds naturally experience dramatic reductions in lean body mass, in excess of what is experienced during cachexia, yet these animals rapidly recover muscle and organ mass losses quickly, with few functional consequences. This proposed research investigates the functional consequences of tissue loss in multiple organ systems, the metabolomic profile of catabolic and anabolic states, and the mechanisms of tissue regeneration in migratory birds with the goal of using this novel, natural model to discover new mechanisms associated with tissue anabolism. We propose to use innovative whole animal approaches to investigate lean body mass reductions, enabling longitudinal studies of organ system function, molecular mechanisms, and recovery processes using this novel, avian natural system. Over the next five years, our goals include combining functional assessments with metabolomics and transcriptomics to uncover unique mechanistic determinants of recovery. Key questions to be addressed involve understanding structural- functional relationships, analyzing metabolite and cytokine responses during lean mass loss, and elucidating molecular pathways driving tissue degradation and remodeling. By leveraging expertise in organismal physiology, metabolism, and molecular biology, we aim to contribute significantly to the field's knowledge base. Ultimately, our research strives to inform the development of novel models and therapeutic strategies for combating cachexia and promoting recovery in patients with debilitating conditions.

NIH Research Projects · FY 2026 · 2025-05

PROJECT SUMMARY Early childhood is a period of neuroplasticity and change across physiological, cognitive, and behavioral domains. Interactions among domains abound and cumulatively support future health outcomes. The present fellowship hones in on the relationship between sleep and motor development. Research with adults supports the impact of specific sleep features (e.g., spindles and slow oscillations) on motor memory consolidation. As such, the central hypothesis of the present work is that sleep oscillations support motor learning in early childhood and that maturational changes in these sleep features support motor development. Specifically, the research aims to identify sleep EEG markers that predict motor memory consolidation (Aim 1) and motor development (Aim 2). To address Aim 1, 4-year-old children (48-52 months) will participate in a within and between subjects design that will ensure rigorous investigation while controlling for individual differences. They will learn the mirror-tracing task at either 7AM or 7PM and performance will be tested 12- and 24-hrs later following an interval with sleep (7PM-7AM) and wake (7AM-7PM; within-subject, order counterbalanced). Overnight sleep bouts will be recorded with polysomnography (sleep EEG) and a unique behavioral coding schema, microgenetic coding, will be used to measure mirror-tracing performance in addition to traditional measures. To address Aim 2, I will use data from a longitudinal R01 of the sponsor in which 3-4-year-old children’s sleep is assessed every 6 months for 1 year. At each timepoint, they will complete a standardized motor assessment (Movement Assessment Battery for Children III) and overnight sleep will be recorded. Sleep physiology (sleep oscillation coupling and topography) are hypothesized to predict motor learning (Aim 1) and motor development (Aim 2). The aims of the proposal have public health significance in that they will identify potential windows of opportunity to intervene on health behaviors (i.e., good sleep habits), learning, and motoric ability at a critical developmental period of the lifespan. The findings can inform future intervention studies, particularly in collaboration with physical and occupational therapists, as well as early childhood education policies and considerations for pediatricians. Collectively, the proposed development plan incorporates activities for training in cognitive neuroscience, sleep, coding and machine learning, statistical analyses, and grant writing. Successful completion of this proposed training plan will result in several submissions of first-author manuscripts to peer-reviewed journals and will establish an essential research foundation to support a competitive early career funding proposal. The fellowship will provide protected time and unique cross-disciplinary training for successful completion of outlined goals.

NIH Research Projects · FY 2026 · 2025-05

Project Summary Animal models are critical for biomedical research. They allow us to rapidly test the importance of any gene for development or diseases. Advances in technology now allow us to reduce or eliminate protein expression in the entire animal or in selected tissue. Similarly, mutations in the coding sequence of a gene associated with a disease can be introduce in animal model to test if they are indeed responsible for the disease state. Antibodies are critical tools to study protein localization and function. They can be used to block a protein function, send it for degradation, immunoprecipitate partners associated with the protein to identify functional complexes. While companies have focused on developing antibodies to human proteins, and to a lesser extend to mouse proteins, there is a need for antibodies that recognize proteins in diverse animal models. Despite the close similarity of protein sequences between human and mouse, many antibodies do not cross react. In addition, some proteins that have been recently identified or studied have not been targeted. Here we propose to produce antibodies against selected proteins in mouse, and amphibian (Xenopus and Axolotl) to facilitate the work in these animal models.

NSF Awards · FY 2025 · 2025-05

Metabolites such as sugar and fatty acids are essential for maintaining human health because they influence energy supply, growth, and cell signaling. Abnormal metabolic changes can lead to diseases such as diabetes, cancer, and heart conditions. Detecting metabolites in living systems in real-time is crucial, but conventional methods require obtaining and destroying samples, which means they can't be used to measure metabolites in individual cells. This project addresses the gap by developing fluorescent probes for highly sensitive and selective detection of key metabolites within living cells. These probes will enhance research in human health, nutrition, plant science, food safety, and environmental studies. Additionally, the project will support interdisciplinary scientist training and engage middle school students through workshops and lab tours. The specific goal of this project is to develop genetically encodable RNA-based fluorescent probes for metabolic imaging and profiling in living cells. These probes comprise three modular domains: a target-binding riboswitch, a fluorogenic RNA reporter, and a transducer. Fluorogenic RNA aptamers can selectively bind and activate the cellular fluorescence of chemical dyes. Riboswitches are natural metabolite-sensing RNA regulatory elements. About 60 distinct classes of riboswitches have been identified so far, covering a wide spectrum of important nucleotides, amino acids, ions, enzyme cofactors, signaling molecules, etc. Upon binding target metabolites, riboswitches can undergo rapid conformational change. The transducer here can be a simple RNA duplex or structural switching RNA sequence that will convert the conformational change of riboswitches into the activation of fluorogenic RNA signals. The major objectives are: (1) to engineer modular allosteric fluorogenic RNA “Broccoli”-based metabolite sensors with a duplex transducer and many-to-most existing class of riboswitches; (2) to develop a new in silico platform to further improve the throughput of probe design and their sensitivities; and (3) to obtain quantitative, multi-colored, and time-resolved imaging profiles of diverse metabolites in individual living bacterial and mammalian cells This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-05

Project summary Immunothrombosis is a critical element of intravascular immunity, but its dysregulation or malfunction leads to a range of thrombotic disorders including stroke and disseminated intravascular coagulation. The massive vaccination campaign during the recent COVID-19 pandemic brought to light a novel immunothrombotic pathology, a relatively rare but extremely dangerous side effect of adenoviral (Ad) vectored vaccines, which is now known as vaccine-induced immune thrombotic thrombocytopenia (VITT). Although COVID-19 is no longer a global health threat, the close association of VITT with a specific delivery vector raises the specter of other Ad- vectored vaccines also eliciting this deadly side effect, a grave prospect given the popularity of this platform. VITT has been linked to the emergence of autoantibodies recognizing a cognate chemokine, platelet factor 4 (PF4), but the specific mechanism underlying this pathology remains elusive. Understanding its molecular mechanism and etiology is critical for addressing the currently unmet need to design rational therapeutic and prophylactic strategies targeting VITT. It will also go a long way towards filling the gaps in understanding the delicate interplay between the beneficial and deleterious effects of immunothrombosis and provide the urgently needed ammunition to suppress the latter without sacrificing the former. We will use a combination of experimental and modeling tools to study VITT emergence and progression on different scales, ranging from micro- (formation of platelet-activating immune complexes) to macroscale (thrombi formation). We have already obtained a complete amino acid sequence of the pathogenic VITT antibody and produced its recombinant copy (RVT1) in quantities sufficient for both biophysical and biological investigations. On the microscale, we will use mass spectrometry and other biophysical tools to study the architecture and biological properties of the immune complexes composed of PF4 and RVT1. On the macro-scale, we will use these complexes to study thrombi initiation and formation using in vitro models based on microfluidic devices mimicking vascular environments relevant for VITT pathogenesis (e.g., cerebral venous vasculature). Bridging the micro- and macro-scales will allow us to elucidate the detailed mechanism of VITT progression by understanding how the disease outcome is modulated by the physical and biochemical properties of its molecular triggers. It will also provide a unique opportunity to address another enigmatic feature of VITT - its frequent localization within the cerebral venous sinuses. Lastly, correlating the amino acid sequences of the pathogenic antibodies and the germline sequences for a set of VITT patients will reveal the etiology of this disease, enabling the design of effective prophylactic and monitoring strategies. The proposed research will be carried out by an interdisciplinary team comprising chemists and biophysicists (Dr. Kaltashov's lab at UMass-Amherst), hematologists and molecular biologists (Dr. Nazy's lab at McMaster University School of Medicine) and biomedical engineers (Dr. Jiménez' lab at UMass-Amherst).

NSF Awards · FY 2025 · 2025-04

This project aims to develop algorithms and theoretical analysis for large-scale resource allocation, which involves distributing resources among various users, applications, or tasks. This process is essential in many contexts, including scheduling work shifts, providing aid during disasters, assigning courses to students, matching research papers with reviewers, and many other scenarios requiring distribution of goods, resources, or services. A key example is optimizing worker shift assignments to maximize efficiency while ensuring a balanced distribution of workloads among staff. Another example is coordinating the distribution of supplies and volunteers during disaster relief efforts to ensure that aid reaches those in need as quickly and effectively as possible. By improving resource allocation strategies, the project aims to enhance operational efficiency while achieving balanced distributions of resources to meet the needs of individuals and organizations across various fields. To achieve this, the project tackles three core challenges: (1) Effective Preference Elicitation – In many domains, users have complex preferences. For instance, a worker might be able to take either shift A or B but not both. This research direction focuses on developing methods to gather agent preferences effectively while minimizing cognitive burden. (2) Preference Uncertainty – Users may be uncertain about their preferences. For example, when assigning students to courses, match quality is inherently uncertain. The goal of this project is to develop allocation mechanisms that account for such inaccuracies in agent preferences. (3) Efficient Computation – Scaling algorithms to handle large problem instances is critical. For example, scheduling shifts in large organizations may involve hundreds of workers and thousands of shift slots. The project aims to design computational frameworks that produce balanced and efficient outcomes in such scenarios. The project will yield practical software tools that enhance AI-driven resource allocation. The educational activities will contribute to curriculum development on collective decision-making and further equip graduate students with essential academic skills, including public speaking, research methodologies, and scholarly writing. By integrating techniques from machine learning and optimization, this research takes a comprehensive approach to resource allocation. It examines the full process from preference elicitation to algorithmic implementation and output guarantees. Specifically, the project will: (a) investigate methods for eliciting expressive yet tractable agent preferences; (b) explore automated prompting techniques to incentivize more complete preference reporting; (c) model preference uncertainty using learning-theoretic and statistical approaches; (d) develop simple, interpretable algorithms adaptable to various problem domains; and (e) ensure that final allocations are balanced, efficient, and robust. Through this interdisciplinary effort, the project seeks to advance the theoretical foundations and practical applications of balanced and efficient resource allocation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-02

ABSTRACT. Conventional cancer chemotherapy typically induces apoptosis, but many tumors become resistant to these agents by inducing anti-apoptotic pathways. Therefore, anticancer therapies targeting non-apoptotic pathways, such as ferroptosis, is an exciting field that offer great potential for overcoming cancer chemo-resistance. Our group is particularly focused on the development of a selective therapeutic strategies based on the ferroptosis pathway that could complement the existing therapeutics to target hard-to-treat cancers such as non-small cell lung cancer (NSCLC) that are resistant to conventional therapies. Using a two-stage screening paradigm, the PI engineered a drug-like nanomolar potent ferroptosis inducer named TKD1079. This compound has high mouse microsomal stability (T1/2 = 402 min) and low plasma clearance (Clint = 1.7 μL/min/mg). Mechanistically, TKD1079 induces ferroptosis selectively instead of apoptosis by causing glutathione peroxidase 4 (GPX4) depletion. TKD1079 triggered ferroptosis in both human fibrosarcoma HT-1080 and adenocarcinoma lung NCI- H23 cancer cell lines while sparing normal, healthy cells (Beas-2B) with 500-fold therapeutic index (TI). Cell death induced by TKD1079 was fully suppressed by the canonical ferroptosis inhibitors deferoxamine and - tocopherol, indicating that it specifically induces ferroptosis. However, the solubility of TKD1079 is low (1.3 M in PBS) and engineering TKD1079 analogs with improved drug like properties including better solubility is required for in vivo studies. Our central hypothesis is that a version of TKD1079 that preserves pro-ferroptosis activity but with increased potency, solubility and stability will be useful to overcome apoptosis/chemo-resistance in hard-to-treat cancers such as NSCLC. We will address this hypothesis by exploring two specific aims: (1) SAR study of first-in-class ferroptosis inducers based on TKD1079 scaffold, and (2) identify the optimal top 2-3 TKD1079 analogs that drive ferroptosis selectively in vitro with improved pharmaceutical properties. Successful completion of this proposed work will facilitate the development of a first-in-class TKD1079 analogs that retain the many positive features of TKD1079 and possess improved solubility for lung cancer therapeutics that trigger ferroptosis and will propel their development towards pre-clinical application.