Three UCF faculty were named 2025 U.S. National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program award winners while two recent faculty hires transferred their CAREER projects to continue their work at Florida’s Premier Engineering and Technology University.
All five awardees teach and conduct research through UCF’s College of Engineering and Computer Science (CECS), and together their funding totals an estimated $3 million to advance real world technologies and positively impact the world.
The annual award program from NSF supports an estimated 500 early-career STEM faculty nationwide from either institutes of higher education or academic nonprofit organizations who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.
Since the program launched in FY 1995, nearly 100 UCF faculty have qualified for NSF CAREER grants, generating more than $40 million in research funding. It has supported a pathway to implement their research through UCF’s Office of Technology Transfer, which helps bring discoveries to the marketplace through licensing UCF technologies and providing information about sponsored research opportunities.
UCF Associate Professors Sidong Lei and Truong Nghiem along with Assistant Professors Kevin Moran, Wen Shen and Hao Zheng continue to accelerate research in their respective fields through their NSF CAREER projects.
Truong Nghiem
Department of Electrical and Computer Engineering
Project Title: Composite Physics-Informed Learning of Dynamics Systems
Award: $477,585 over five years
Associate Professor Truong Nghiem came to UCF in Fall 2024, bringing expertise in machine learning and autonomous systems.
His research focuses on developing new methods that blend machine learning with physical principles to improve complex systems such as autonomous vehicles, smart buildings and industrial automation systems.
“My work aims to help create the intelligent, autonomous systems of the future—systems that will enhance productivity, improve safety, and make everyday life more convenient and sustainable,” says Nghiem, whose research group is called the intelligent Cyber-Physical Systems (iCPS) Lab. “I specialize in intelligent cyber-physical systems — engineered systems that seamlessly integrate the cyber world, which includes computation, machine learning and artificial intelligence (AI), with the physical world, which includes mechanical and dynamic systems like vehicles, buildings and robots.”
His CAREER project, which he transferred from his previous university, directly supports his ongoing efforts and broadens the scope of his machine learning research.
“This research aims to create a composite physics-informed machine learning (CPIML) framework,” Nghiem says. “Physics-informed machine learning (PIML) embeds the laws of physics into the learning process, leading to models that are more accurate, physically consistent and interpretable compared to traditional machine learning approaches. CPIML takes this a step further by enabling the composition of both physics-based models and PIML components — along with their physical properties — to model more complex, large-scale systems.”
Applications of machine learning that may be integrated into everyday life include improved response times of autonomous vehicles and robots, smarter energy systems that optimize energy use and temperature control, and more reliable industrial robotic systems that require minimal supervision.
Nghiem says he strives for his research to not only provide foundational knowledge but to also have a direct impact on real technologies that people are using right now.
“As our world becomes increasingly automated, ensuring that systems are safe, efficient and trustworthy isn’t just a scientific goal — it’s a societal necessity,” he says. “I have developed efficient models for HVAC systems in buildings that improve energy management, and I’ve also worked on predictive models for autonomous racing cars, pushing the boundaries of what AI can do in dynamic, high-speed environments.”
Like the complex systems Nghiem studies, a university’s network of resources should be robust and reliable. He says he’s fortunate that his research fits perfectly into UCF’s supportive interdisciplinary ecosystem.
“UCF’s commitment is evident through initiatives like the AI Initiative and the Knights Digital Twin Initiative,” Nghiem says. “This work also underscores the importance of combining knowledge from different domains, bringing together AI, engineering and physics to create solutions for real-world problems.”
New Chips to Keep Pace with Modern Processing Demands
Hao Zheng
Department of Electrical and Computer Engineering
Project Title: A Scalable, Polymorphic, and Efficient Architecture for Irregular and Sparse Computations (APEX)
Award: $550,000 over five years
The emergence of artificial intelligence (AI) and machine learning, while transformative, has created new challenges for today’s computing hardware.
Hao Zheng, assistant professor of electrical and computer engineering, says he’s determined to navigate these challenges and arrive at solutions. His NSF CAREER project, much like his research, focuses on how to enhance the performance, energy efficiency and utility of chip processors to support the evolving landscape of AI workloads.
“My research lies in the area of computer architecture and machine learning,” Zheng says. “I aim to design versatile chip processors that can greatly speed up machine learning applications with significantly reduced power consumption.”
Creating general-purpose or fully customized chips have been the most common methods of addressing emerging challenges in computational tasks, but both approaches have drawbacks.
Zheng’s bold solution is to design a chip that can adapt to any applications with various computing tasks. His research group, the Intelligent Computer Architecture and Technology (iCAT) Laboratory, is working to revolutionize current chip architectures, such as graphics processing units (GPUs), to handle the rising complexity of modern AI workloads. These include not just large models but multimodal systems, robotics, simulations and real-time decision-making.
“Specializing the underlying hardware architecture has become a trending solution to meet the computational demands of modern applications,” Zheng says. “However, current specialized hardware, in the form of accelerators, is either fully customized for regular applications or lacks the generality to support a wide range of applications. However, today’s applications are evolving rapidly with increasingly complex workloads such as large language models, multi-modal models, embodied AI, among others.”
Some real-world applications of his research can directly affect how robotics, augmented and virtual reality, autonomous driving, simulations and biological discoveries operate.
“This award will introduce a transformative concept — the polymorphic chip processor — to support ubiquitous irregular and complex applications with intensive data,” Zheng says. “The research will invent a new class of chip processors, grounded in graph theory, that can dynamically adapt to irregular and complex workloads at runtime. We believe this can have a transformative impact on computer architecture, compilers, scheduling and many other key areas in computing.”
Zheng says his NSF CAREER award is just the beginning of what he can achieve here at UCF.
“This honor is a testament to the collective efforts of my entire research team,” he says. “I truly appreciate the collaborative research culture here at UCF. I’ve also benefited greatly from the guidance and encouragement of my colleagues, and I would like to thank our department chair, Dr. Reza Abdolvand, for his support over the past several years. Most importantly, I feel incredibly fortunate to have worked with four exceptional Ph.D. students who have grown alongside me throughout this journey.”
Opportunities for growth and enrichment at UCF are plenty, Zheng says. Exploring emerging unconventional applications for chips, strengthening educational development and collaborating with industry are three pillars he aspires to focus on and expand as he continues his research.
“First, I plan to establish a solid theoretical foundation for irregular application acceleration,” Zheng says. “Second, I intend to collaborate with industry to prototype the concept. By the end of the award period, we aim to have a functional chip processor running in the lab, demonstrating the practicality of our idea.”
One of the most important and personal components of his future efforts is his emphasis on education.
“This is the core mission of both our university and the academic community,” Zheng says. “As a first-generation college student, I am aware that a significant number of UCF students come from similar backgrounds. I will provide mentorship to both undergraduate and graduate students interested in the chip industry.”