NSF CAREER awards recognize 7 Purdue Engineering faculty
Seven Purdue Engineering faculty members have earned National Science Foundation Faculty Early Career Development Program (CAREER) awards in the past year.
The prestigious CAREER awards support early-career faculty with potential to serve as academic role models in research and education, as well as lead advances in the mission of their department or organization.
“Early career recognitions from federal agencies, like the NSF CAREER awards, help faculty take those crucial steps in forming a successful career,” said David Bahr, senior associate dean of faculty for Purdue’s College of Engineering. “Purdue faculty at all stages of their careers make a difference through their research, and when our newest faculty are honored in the very competitive federal funding arena, we know our faculty are heading in the right direction.”
Bahr added: “The fact that peers from around the country reviewed and recommended, and program managers agreed, to support these faculty in their efforts for discovery and learning makes them even more impactful. Engineering discoveries backed by NSF create not only knowledge but also people. The faculty and students supported by this funding make ideas become reality, and their solutions can be shared with both researchers in the field and industrial partners, improving our state, region, nation and world.”
Laura Blumenschein, Assistant Professor of Mechanical Engineering
Towards a Framework for Emergency Adaptability in Soft Continuum Robots
$650,000
May 1, 2026 – April 30, 2031 (estimated)
The project aims to develop contact-aware soft robots that can leverage touch intelligently, working with instead of against contact. The research team is studying how passive and reactive responses to touch can be designed in a soft robot arm to perform useful tasks, such as avoiding damage, seeking support, and automatically grasping target objects.
This new breed of soft robots could be valuable in areas facing increasing labor shortages and risks for humans – think picking fruit in dense trees, working in healthcare scenarios, or aiding in hazardous inspection and search-and-rescue tasks. Goals are for adaptive behavior to emerge through encoding a representation of the environment in the interactions.
Soft robot design tools from the research project will be used to develop hands-on kits to help middle and high school students explore basic engineering and science topics.
Learn more: Laura Blumenschein receives NSF CAREER award to develop contact-aware robots
James Davis, Assistant Professor of Electrical and Computer Engineering
PTM-SEER: Software Engineering Foundations for Re-Using Pre-Trained Neural Models
$687,140
June 1, 2026 – May 31, 2031 (estimated)
The project tackles a growing dilemma: how to safely, effectively and efficiently reuse AI models that have been trained. While pre-trained models are becoming building blocks for modern software, engineers lack a playbook for reusing AI models. As AI becomes more embedded in everyday technology, engineers must choose which models to trust, how to adapt them, and how to explain their behavior.
The idea is to help engineers make smarter, more transparent decisions about whether to reuse AI components and how to proceed if they do so – ultimately making AI reuse more practical, efficient and trustworthy.
Broader impacts include an ecosystem-wide dataset of AI models, plus a toolkit for K-12 through graduate students and practicing professionals. By bolstering the foundation for trustworthy AI engineering, the work aims to support U.S. economic competitiveness, expand academic-industry partnerships, and deepen the pipeline of AI-skilled software engineers.
Learn more: Purdue ECE professor James Davis receives NSF CAREER award for research on reusing AI models
Zahra Ghodsi, Assistant Professor of Electrical and Computer Engineering
$564,455
July 1, 2026 – June 30, 2031 (estimated)
Distributed Large-scale Machine Learning with Security Guarantees
The colossal scale of modern AI systems limits large-scale AI development to powerful entities with extensive computing resources. As a result, AI pipelines often lack transparency, while reliance on a few service providers creates risks to system reliability.
Distributed AI development offers an alternative approach, enabling organizations and individuals to contribute to AI systems in a more transparent way, particularly for applications that serve the public good. However, distributed environments are also vulnerable to malicious activity.
The solution: Create tools for an open, secure and distributed AI development paradigm. Key innovations include verification mechanisms for heterogeneous pipelines that use private data, as well as techniques for proving that computations performed by potentially untrusted workers are correct.
More broadly, the project seeks to empower stakeholder communities and individuals to participate securely in large-scale AI development, strengthen workforce understanding of AI tools including their opportunities and vulnerabilities, and foster collaboration between researchers, stakeholder communities, and industry practitioners.
Learn more: Purdue ECE professor Zahra Ghodsi receives NSF CAREER Award to advance open, secure, and distributed AI
Qi Guo, Assistant Professor of Electrical and Computer Engineering
$609,929
May 1, 2026 – April 30, 2031 (estimated)
Computational Passive 3D Imaging
A passive 3D imager estimates the distance of objects from photographs captured without emitting light into the environment. Compared with active 3D technologies such as light detection and ranging (LiDAR), passive 3D imaging offers covertness, energy efficiency and hardware simplicity – making it attractive for applications in national defense, scientific exploration, robotics, and wearable devices.
However, despite substantial hardware and software progress, existing passive 3D imagers are limited by a short operating range, high computational cost, poor low-light performance, and trouble integrating additional imaging functions.
The project will create a new family of passive 3D imaging solutions to overcome these constraints by performing specialized computations directly on naturally-available environmental light, using coordinated optics and algorithms. Preliminary results demonstrate clear range, efficiency, low-light robustness, and integrability improvements – indicating ability to transform 3D perception.
Camera-themed learning activities for middle through graduate school students will advance engineering education.
Learn more: Purdue ECE professor Qi Guo earns NSF CAREER award for passive 3D imaging research
Nusrat Jung, Assistant Professor of Civil and Construction Engineering
$545,869
Sept. 1, 2026 – Aug. 31, 2031 (estimated)
Personal care products release chemicals into indoor air during normal use. Those chemicals stick to surfaces, form small airborne particles, and move through ventilation systems into outdoor air. The project will examine how product usage, heat, and building operation govern this behavior.
The research will combine controlled laboratory experiments with full-scale testing at the one-of-a-kind Purdue zEDGE Test House. Using high-resolution, real-time mass spectrometry and advanced aerosol instrumentation, the project will generate datasets on how chemicals behave across gas and particle phases under realistic building conditions, informing AI-assisted models of chemical movement and ventilation control with direct implications for building system design.
A rigorous education program will encompass Purdue’s architectural engineering curriculum; the Jung-advised Global Air Quality Trekkers EPICS team; and the mobile zEDGE platform, which brings science to the public through Purdue’s Grandparents University®, air quality demonstrations at the West Lafayette Public Library, and a study abroad program in Finland.
Learn more: Nusrat Jung wins NSF CAREER award
Haitong Li, Assistant Professor of Electrical and Computer Engineering
$611,366
July 1, 2026 – June 30, 2031 (estimated)
From smart devices to data centers, future AI will need to reason, solve complex problems, and respond more effectively in real time. One promising path is neuro-symbolic AI, an emerging approach that combines the strengths of neural networks, which power today's large language models, and symbolic reasoning, which applies rules and logic. But that kind of advanced reasoning pushes today's computing platforms to their limits.
This project addresses that gap by building a new class of computing chips for neuro-symbolic AI through cross-stack co-design, specialized memory technologies, and advanced three-dimensional integration. Such a new computing foundation will equip future AI systems with two capabilities at once: reasoning through complex, multi-step problems and operating at far lower energy.
In parallel, the project will create course materials and hands-on learning experiences in neuro-symbolic AI and semiconductors for students and K-12 educators, enhancing participation and literacy while helping prepare the future semiconductor workforce.
Learn more: Purdue ECE professor Haitong Li earns NSF CAREER award to build a new class of AI hardware
Rachel Surowiec, Assistant Professor of Biomedical Engineering
$596,068
May 1, 2026 – April 30, 2031 (estimated)
Although bone fractures are common and costly, most tools used to estimate fracture risk focus on bone mineral density and overlook important parts of bone tissue, including water and collagen. Water helps support the structure and function of bone, while collagen is a protein that gives bone flexibility.
By combining advanced imaging, computational modeling, and data-driven analysis, the project will create more accurate tools for understanding how bone fails and identifying fracture risk earlier. It calls for investigating how water within the bone matrix, the structural framework that makes up most of bone tissue, contributes to bone strength and resistance to fracture. Researchers are examining how bone water changes during aging and reduced physical activity, as well as when and where these alterations begin to weaken bone.
The project also includes a hands-on research program in biomedical imaging, data science, and modeling for undergraduate students, including those balancing work, caregiving responsibilities, or remote learning.
Learn more: Purdue BME professor Rachel Surowiec receives NSF CAREER award to advance bone fracture research
Click here to read about all of Purdue's NSF CAREER awardees.