Five Iowa State Researchers Earn NSF CAREER Awards

During the 2025-26 academic year, five Iowa State University research scholars were selected for Faculty Early Career Development Program CAREER awards from the National Science Foundation (NSF), recognizing their leadership at the intersection of research, education, and innovation.

The NSF CAREER program is widely regarded as one of the agency’s most prestigious honors, supporting early-career faculty who demonstrate exceptional potential as both researchers and educators. This year’s Iowa State recipients reflect the breadth of inquiry across the university, with projects spanning cardiovascular modeling, trustworthy artificial intelligence, safe and decentralized machine learning, and advanced additive manufacturing.

Together, the five awards represent more than $2.6 million in federal support for research that addresses complex, real-world challenges while creating new opportunities for student engagement, interdisciplinary collaboration, and workforce development.

“NSF CAREER award recipients exemplify the creativity and commitment that define Iowa State’s research mission,” said Vice President for Research Peter Dorhout. “Their work not only advances knowledge in critical fields but also supports our institutional strategic aspirations of being the university that creates opportunities and forges new frontiers while also being the most student-centric research university.”

2025-2026 NSF CAREER Recipients

Abhay Bangalore Ramachandra, assistant professor of Mechanical Engineering
Proposal Title: A Unified Multiscale Computational Approach to Model Vein Graft Failure
Total Intended Award Amount: $500,000 over 5 years

Abhay Bangalore Ramachandra’s CAREER project aims to improve outcomes for patients who undergo coronary bypass surgery, a common treatment for coronary artery disease. In many bypass surgeries, surgeons use vein grafts to reroute blood around blocked arteries, but these grafts can fail over time, sometimes requiring additional surgery and increasing the risk of complications. Ramachandra’s project will use advanced computer models to better understand how blood flow, inflammation, and vessel changes interact to affect whether vein grafts succeed or fail.

By combining models that examine vein grafts at the tissue-level mechanics and cell-level responses, and using data science and artificial intelligence to refine them, the project will create a virtual testing framework for exploring new treatments and technologies designed to improve graft performance. The educational component will train students in computational modeling tools, expand their application to other blood vessel diseases, and strengthen biomedical engineering education through new learning modules and hands-on training opportunities.

Ping He, assistant professor of aerospace engineering
Proposal Title: Autonomous AI Agents for Translational Multidisciplinary Design Optimization
Total Intended Award Amount: $549,499 over five years

Ping He’s project aims to make artificial intelligence (AI) a more reliable partner in engineering design, helping computer-generated concepts move more efficiently from simulation to real-world use. Computer models are essential tools for designing complex engineered systems, but they do not always fully capture how systems behave once they are built or tested. He will develop autonomous AI agents that can identify errors in physics models, apply corrections, ensure those corrections hold up across different conditions, and measure how those changes affect final design performance.

The project will apply these methods across areas, including fluid mechanics, structural dynamics, heat transfer, acoustics, and multiphase transport, with test cases involving bio-inspired drones, battery packs, and magnetic metal manufacturing. He’s research could help engineers explore complex design options faster and with greater confidence by creating AI-based design tools that are more trustworthy, physically realistic, and broadly applicable. In his CAREER project educational component, He will create hands-on learning modules for students from elementary school through graduate education, helping them build critical thinking skills and prepare for careers in AI-enabled engineering design.

Mengdi Huai, assistant professor of Computer Science
Proposal Title: Enabling Reliable Uncertainty-Aware Decision Making with Unreliable Data
Total Intended Award Amount: $549,938 over 5 years

Mengdi Huai aims to make deep learning models more trustworthy in high-stakes settings where unreliable predictions can have serious consequences, such as medical diagnosis. While today’s AI models can make powerful predictions, real-world data are often noisy, incomplete, or otherwise imperfect, making it difficult to know how much confidence to place in those predictions. Huai’s project will improve a statistical approach called conformal inference, which helps quantify uncertainty for individual model predictions, so that it works more reliably with messy real-world data.

By developing new methods for handling noisy data, filling in missing information, and making uncertainty estimates more useful for decision-making, Huai will help researchers and practitioners build safer, more transparent machine learning systems across a range of fields. The educational component will bring these advances into existing and new courses, provide research opportunities for undergraduate and graduate students, and introduce K-12 students to data science, machine learning, and uncertainty-aware decision-making.

Shana Moothedath, assistant professor of Electrical and Computer Engineering
Proposal Title: A Principled Framework for Multi-Task Representation Learning for Scalable, Decentralized, and Safe Sequential Decision-Making
Total Intended Award Amount: $515,000 over 5 years

Shana Moothedath aims to improve machine learning systems’ ability to work together, learn from limited or varied data, and make safe decisions in complex real-world settings. Her project focuses on systems composed of many interconnected parts, such as smart farms, automated control systems, and other networked technologies. The challenge is to coordinate actions while adapting to changing operating conditions. Moothedath intends to develop new methods that allow these systems to share what they learn without exposing private data, operate efficiently without relying on a single central controller, and account for safety requirements while making decisions.

By advancing the foundations of reinforcement learning, her work aims to make decision-making systems more reliable, scalable, and adaptable. The research will also support education and workforce development by giving K-12 and college students stronger preparation in math, coding, machine learning, and intelligent system design.

Sougata Roy, assistant professor of Mechanical Engineering
Proposal Title: Advancing the Printability of Aluminum Alloys via In-situ Alloying and Hybrid Processing Strategies
Total Intended Award Amount: $550,000 over 5 years

Sougata Roy’s CAREER project aims to make 3D printing high-performance aluminum parts easier and more reliable for demanding structural purposes, including naval and aerospace applications. Aluminum components can offer advantages such as lighter weight, improved fuel efficiency, and resistance to certain forms of cracking, but additive manufacturing methods often struggle with aluminum because the material can develop cracks and defects as it cools and solidifies.

Roy’s project will focus on laser powder-blown Directed Energy Deposition (L-DED), a metal 3D-printing technique used to repair parts and build large components, and will investigate why cracks form and spread during the process. Roy will develop new strategies to reduce defects, manage internal stresses by tuning microstructure and solidification behavior during laser metal deposition process. By combining advanced manufacturing experiments with high-resolution X-ray strain mapping, the project will generate new knowledge that could expand the range of aluminum alloys suitable for additive manufacturing and help industry produce stronger, more reliable printed metal parts. He will also disseminate his research findings annually via Iowa State’s Center for Industrial Research and Service (CIRAS) as well as conduct high school-level student outreach via local Science Bound Saturday programs.