The five recipients are Dr. Waseem Abbas, assistant professor of systems engineering; Dr. Sourav Dutta, assistant professor of electrical and computer engineering; Dr. Kirill Lazebnik, assistant professor of mathematical sciences; Dr. Kyle McCall, assistant professor of materials science and engineering; and Dr. Xinda Wang, assistant professor of computer science. Four of the five, Abbas, Dutta, McCall and Wang, are members of the university’s Erik Jonsson School of Engineering and Computer Science, while Lazebnik belongs to the School of Natural Sciences and Mathematics. The portfolio of funded work illustrates how the CAREER mechanism can seed progress across the full stack of modern technology, from the atoms inside a transistor to the mathematical abstractions that describe dynamical behavior and the code that keeps the world’s software infrastructure safe.

University research leadership framed the awards as evidence of the institution’s growing strength in areas that will define the coming decades of technology. “From resilient networks and advanced semiconductors to artificial intelligence, cybersecurity and fundamental mathematics, these researchers are tackling complex challenges with the potential to shape the technologies and systems of the future,” said Dr. Joseph Pancrazio, vice president for research and innovation and professor of bioengineering. He emphasized that the awards also recognize the recipients’ commitment to integrating research and education, giving students the chance to learn through hands-on discovery and helping cultivate the next generation of researchers and innovators. That educational component is not incidental to the CAREER program; it is a core evaluation criterion, and each of the five UT Dallas projects embeds training, outreach or curriculum development alongside the technical work.

Dr. Waseem Abbas received a five-year award of $514,916 to confront one of the most consequential questions in networked engineering: what happens when a system designed to absorb disruption is hit by something worse. Robot teams, infrastructure networks and distributed computing systems, in which tasks are shared across many devices, are typically engineered with a certain tolerance for failure built in. If an attack, fault or environmental disturbance stays within that expected envelope, the system degrades gracefully. But when the disruption exceeds the threshold, performance can deteriorate rapidly and unpredictably, sometimes collapsing in ways that cascade through the entire network. Abbas aims to develop networked systems that adapt as conditions worsen, so that performance declines smoothly rather than failing catastrophically. A second strand of his research seeks to identify the most critical connections and components within a network, the nodes and links whose protection yields the greatest resilience. By concentrating defenses on those essential elements, operators could maintain core functions without paying for costly, blanket redundancy across every part of the system. The work has obvious implications for autonomous vehicle coordination, industrial automation and the distributed computing fabric that increasingly underpins everyday services.

Dr. Sourav Dutta’s award, totaling $501,234, targets the physical bottleneck that threatens to slow the artificial intelligence revolution: the semiconductor itself. As AI models and autonomous systems generate staggering volumes of data, conventional computer chips struggle to move information efficiently between the separate regions where data is stored, processed and transmitted. Every transfer across those internal distances costs energy and time, and at the scale of modern workloads the cost becomes prohibitive. Dutta will investigate ultrathin oxide semiconductor transistors with a property that could prove transformative: they can be manufactured at low temperatures and stacked vertically on top of conventional silicon electronics. Vertical stacking would shorten the distance data must travel, directly reducing energy consumption and potentially enabling faster, more efficient chips. Because the low-temperature fabrication process is compatible with existing silicon technology, the approach could be integrated with current manufacturing rather than requiring an entirely new industrial base. Dutta also plans to apply artificial intelligence and computational modeling to predict how candidate transistors will perform before they are built, accelerating design cycles and shrinking the optimization process that normally stretches across years of laboratory iteration.

On the software side of the engineering school, Dr. Xinda Wang received $617,397 to build AI tools that can help open-source software communities catch security vulnerabilities earlier, at the moment developers are making changes to code. Open-source software, assembled from publicly available code that anyone can inspect, contribute to or reuse, has become the invisible backbone of the digital world. It powers commercial products, government systems and critical infrastructure alike. That ubiquity carries a hidden risk: when a vulnerability exists in a widely used open-source component, it silently propagates into every downstream system that incorporates the original code, multiplying the potential blast radius of a single flaw. Wang’s team will train machine-learning systems to recognize patterns in how code changes are made and how those changes ripple through other parts of a software project, flagging suspicious modifications before they are merged and deployed. Just as importantly, the researchers will develop AI models capable of explaining why a particular change may pose a security risk. That explanatory capability matters because security teams and volunteer maintainers often operate with limited time and resources; a tool that simply raises an alarm without context is easily ignored, while one that articulates its reasoning can be trusted and acted upon.

The fourth engineering awardee, Dr. Kyle McCall, received the largest of the five grants, $796,646, to probe a question that sits at the frontier of materials science: how the local atomic structure of emerging semiconductor materials determines their properties and their potential in electronics and energy technologies. Many of the most promising new materials exhibit unusual behavior that arises from deviations of atoms away from their ideal, expected positions within the crystal lattice. These local structural distortions can dramatically alter how a material conducts, absorbs light or responds to electric fields, yet scientists do not fully understand how the deviations can be controlled through the composition and arrangement of the constituent atoms. Compounding the challenge, the distortions are invisible to conventional characterization techniques, which average over large regions and therefore miss the local disorder that matters most. McCall and his team will use total X-ray scattering, a technique sensitive to how atoms are locally bonded within a material, to map the relationship between chemical composition, atomic deviations and resulting properties. The ultimate goal is a set of design principles that researchers can use to create new semiconductor materials with desirable properties on demand. The project also carries an educational mission, providing undergraduates with hands-on research experience and developing educational resources for teachers and students across the Dallas-Fort Worth region.

The lone recipient in the School of Natural Sciences and Mathematics, Dr. Kirill Lazebnik, secured a $450,000 CAREER grant for work in pure mathematics with unusually broad applied reach. His research concerns rational functions, the mathematical objects formed as ratios of polynomials, which serve as fundamental tools for mathematicians, scientists and engineers who model and analyze complex systems. Rational functions appear throughout signal processing, dynamical systems and scientific computing, making advances in their theory potentially consequential far beyond mathematics departments. Lazebnik’s project aims to deepen understanding of the structure of the space of rational functions and to explore how these functions might be applied in additional areas of analysis and dynamics. Questions that sound abstract, such as how the space of such functions is organized and connected, often translate into practical insight for engineers studying stability, control and signal behavior. Like his colleagues, Lazebnik has built education into the heart of the grant, with undergraduate research opportunities and community outreach programs designed to train future mathematicians and widen access to the discipline.

Taken together, the five awards reveal a coherent picture of where foundational research investment is flowing and why. The problems the CAREER recipients have chosen sit precisely at the pressure points of contemporary technology: networks that must survive deliberate attack and random failure, chips that must move data without drowning in their own energy consumption, software supply chains that must be defended at the moment of change, materials whose properties are hidden in atomic-scale disorder, and mathematical structures that underpin our ability to model all of the above. Each project also treats education as an engineering problem in its own right, designing pipelines of trained students, teacher resources and community engagement that extend the impact of the research well beyond the laboratory. For UT Dallas, the sweep of the awards across two schools signals institutional momentum in fields where competition for federal support is fierce. For the researchers themselves, five years of stable funding at the start of an academic career is a rare luxury, the freedom to pursue ambitious, long-horizon questions, and to bring students along for the entire journey from first hypothesis to published result.

Subject of Research: 2026 NSF CAREER awards to five UT Dallas faculty supporting research on network resilience, oxide semiconductor transistors, open-source software security, atomic structure of semiconductor materials and rational functions.

Article Title: Technologies, math research get lift from CAREER awards

Article References: Technologies, math research get lift from CAREER awards. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: NSF CAREER awards, University of Texas at Dallas, network resilience, oxide semiconductor transistors, open-source software security, artificial intelligence, semiconductor materials, total X-ray scattering, rational functions, systems engineering, materials science, mathematical sciences

Receiving a substantial five-year grant totaling $500,000 from the NSF, WindSTAR represents a public-private research partnership that epitomizes collaboration between academia and industry. This funding will be evenly distributed between the two universities, each receiving $250,000. The key focus of this grant is to propel forward various initiatives that leverage artificial intelligence (AI) in the field of wind energy. Specifically, projects aim to minimize manufacturing defects, predict turbine component conditions, forecast wind patterns, and establish resilient wind energy systems. These research endeavors not only seek to advance technology but also ensure that it can be implemented effectively in the field.

At the heart of WindSTAR’s initiatives is the acknowledgment of the challenges facing energy grids across the globe. As Dr. Mario Rotea, the director of UTD Wind and WindSTAR site director, articulated, the NSF’s financial support is instrumental in fortifying the resilience of energy grids. In an age where the demand for sustainable energy solutions is never greater, efforts to innovate and implement state-of-the-art technologies have become paramount in addressing potential weaknesses in energy infrastructures. This funding mechanism serves as an enabler, fueling UTD’s vision for developing transformative technologies within the energy sector.

The collaborative nature of WindSTAR is underscored by its designation as an NSF Industry-University Cooperative Research Center (IUCRC). This platform not only facilitates robust partnerships between academic researchers and industry players but also ensures the effective integration of governmental perspectives into the research framework. With its mission to cultivate groundbreaking research in wind energy, WindSTAR has completed a remarkable 79 projects aimed at delivering tangible solutions for industrial partners. These projects encompass a range of critical innovations, including digital modeling techniques to evaluate performance, measurement campaigns to ensure accuracy, and advanced control systems essential for optimizing energy production.

One of the essential features of WindSTAR is its ability to create a direct pipeline between academic research and the pressing needs of industry. This relationship has proved mutually beneficial, transforming the educational trajectory for many graduate students. As Dr. Edward White, head of mechanical engineering at UTD, noted, over 25 graduate students have had the opportunity to engage actively with industrial partners, acquiring invaluable experience that many have leveraged into successful careers following graduation. This close-knit relationship embodies the potential for university research to serve not just as an educational tool, but as a catalyst for workforce development within the ever-evolving energy sector.

Remarkable success stories have emerged from WindSTAR’s initiatives. Alumni, such as Umberto Ciri, who earned his PhD in 2019, exemplify the symbiotic relationship forged between academia and industry. Now an associate professor at the University of Puerto Rico at Mayagüez and a 2025 NSF Faculty Early Career Development Program (CAREER) award recipient, Ciri credits his formative years as a doctoral student at UTD for equipping him with the skills and insights necessary to innovate in the field of wind energy. His experience underscores the capacity of programs like WindSTAR to foster a dialogue between academia and industry, leading to impactful research outcomes that resonate within the larger field.

In addition to enhancing operational efficiency within wind farms, WindSTAR provides a multifaceted exposure to the wind energy sector that transcends beyond specific fields of study. Ciri emphasizes the diverse range of activities and discussions he participated in as pivotal in broadening his understanding of the industry landscape. Engaging in WindSTAR not only cultivated his technical expertise but enriched his insight into various dimensions of renewable energy, providing him with a unique advantage as he navigated his early career stages. The center’s diverse scope reflects a commitment to multifaceted education and engagement with the broader implications of energy technologies.

Moreover, the ramifications of projects conducted through WindSTAR extend to actual energy production and policy. As the landscape of energy generation shifts towards sustainable practices, the ability to predict the performance of turbine components and optimize manufacturing processes emerges as a pressing necessity. The incorporation of AI technologies enables researchers to create advanced predictive models that not only enhance operational efficiencies but also inform strategic decisions on energy generation. The focus on developing robust wind energy systems aligns with broader global objectives towards mitigating climate change impacts through sustainability initiatives and the reduction of carbon emissions.

Furthermore, the research conducted at WindSTAR plays a crucial role in shaping public policy and industry standards. As knowledge and technology evolve, so must the associated regulations and infrastructures. WindSTAR’s collaborative efforts with industry and governing bodies aim to ensure that innovative practices are not only theoretically sound but also practically viable and sustainable in the long run. The active participation of WindSTAR researchers in developing protocols, guidelines, and best practices helps bridge the gap between innovative research and real-world implementation, ensuring that advancements are effectively translated into actionable outcomes.

The significance of WindSTAR extends beyond its research initiatives. It represents a robust model of how academic institutions, industry partners, and government agencies can unite to address pressing energy challenges. The emphasis placed on collaboration, knowledge-sharing, and the integration of cutting-edge technology encapsulates a forward-thinking approach essential for tackling future energy demands. As the global community grapples with the realities of climate change and energy scarcity, the work being done at WindSTAR signifies a potent commitment to advancing sustainable solutions for a resilient energy future.

In conclusion, the continuing support from the National Science Foundation to the Center for Wind Energy Science, Technology and Research heralds a crucial advancement in the pursuit of renewable energy technologies. As WindSTAR embarks on new projects fueled by AI and collaboration, the center stands at the forefront of pioneering solutions that align with global efforts for energy independence and sustainability. The collaborative fabric woven through the partnership of academia, industry, and government enables transformative research to emerge, impacting the landscape of wind energy for generations to come. The future of energy research rests upon such iconic partnerships that promise to deliver not just efficiency, but a paradigm shift towards sustainable energy systems.

Subject of Research: Wind Energy and AI in Renewable Energy Systems
Article Title: Advancing Wind Energy: The Impact of WindSTAR and AI Innovations
News Publication Date: October 2023
Web References: WindSTAR, NSF Grant Information
References: Not Applicable
Image Credits: The University of Texas at Dallas

Keywords

In a competitive field, Khan stands out as one of the 471 selected fellows from a pool of accomplished scientists, engineers, and innovators. This year’s cohort of honorees will be formally celebrated during a special event in Washington, D.C., scheduled for June 7. Such recognition not only highlights individual accomplishments but also underscores the importance of collaborative advancements in science and technology that are essential in addressing contemporary global challenges.

Khan’s body of work showcases extensive contributions to machine learning, particularly in its applications to cybersecurity—a discipline of increasing importance in today’s digital landscape. His research focuses on developing algorithms capable of analyzing large, continuous data streams, which is critical for real-time cybersecurity monitoring. As technology advances, the sophistication of cyber threats also evolves, necessitating innovative approaches to protect sensitive information and maintain the integrity of data systems.

His work has previously garnered notable funding, including a substantial grant from the National Institute of Standards and Technology, aimed at establishing a Center for Secure and Trustworthy Artificial Intelligence at UT Dallas. This initiative seeks to tackle the emerging challenges posed by artificial intelligence technologies, particularly in the realm of cybersecurity. Such foresight is essential for adapting educational and technological frameworks to current trends and potential future hurdles.

Khan’s journey in academia began in 2000 when he joined UT Dallas, and over the years, he has become an international leader in big data analytics. His research deftly intertwines several domains, from cybersecurity to political science. He has pioneered methods for updating machine learning models, enabling these systems to keep pace with changing tactics employed by cyber adversaries. This adaptive capability is transformative, allowing organizations to enhance their defenses against evolving threats.

Collaborative efforts are central to Khan’s research philosophy. In partnership with colleagues from various disciplines, including those from the School of Economic, Political and Policy Sciences, he has produced significant tools such as ConfliBERT. This AI-driven, open-source platform serves as a repository for valuable insights into political conflict and violence, showcasing how interdisciplinary approaches can yield innovative solutions to complex societal issues.

Khan expresses a deep appreciation for the honor bestowed upon him as an AAAS fellow, acknowledging the importance of being part of a diverse scientific community. The recognition is a testament not only to his individual efforts but also to the support he has received from the University of Texas at Dallas throughout his academic career. His contributions reflect a blend of rigorous research and a commitment to improving societal understanding of both technological and social phenomena.

As a fellow of the IEEE and other prestigious organizations, Khan’s standing in the scientific community is further reinforced. His accolades include the IBM Faculty Award and the IEEE Technical Achievement Award, among several others. These honors reflect his sustained dedication to research excellence and innovation within the field of computer science, illustrating the profound influence he has had on both students and peers alike.

Khan’s research funding sources encompass a broad range of reputable agencies, including the National Science Foundation and the National Security Agency. His interdisciplinary work and inquiries have not only advanced academic knowledge but also provided practical implementations that can influence technological protocols and national security measures. These efforts underscore the crucial intersection of academia and industry, highlighting the vital role that research plays in addressing real-world challenges.

Moreover, Khan’s educational background illustrates his commitment to the field. With a PhD from the University of Southern California and an undergraduate degree from Bangladesh University of Engineering and Technology, his academic journey exemplifies the global nature of scientific inquiry. His diverse experiences enrich his perspective and allow him to contribute unique insights into discussions about data management, cybersecurity, and machine learning.

In conclusion, Dr. Latifur Khan’s recent election to the prestigious AAAS fellow class marks both a personal and professional milestone in his career. His extensive research contributions continue to influence the landscape of computer science, especially in areas intertwined with cybersecurity and machine learning. As an educator, researcher, and innovator, he remains committed to pushing the boundaries of knowledge in technology, contributing to the advancement of a safer and more informed digital world. This recognition stands as a beacon of inspiration for aspiring scientists and serves as a reminder of the importance of collaborative efforts in the pursuit of scientific excellence.

Subject of Research: Machine Learning in Cybersecurity
Article Title: Dr. Latifur Khan Elected as AAAS Fellow for Contributions to Machine Learning and Cybersecurity
News Publication Date: October 2023
Web References: UT Dallas Profile
References: AAAS Fellows Program
Image Credits: The University of Texas at Dallas

Keywords

Applied sciences and engineering, Computer science, Artificial intelligence, Cybersecurity, Machine learning, Data mining, Deep learning.