Five UConn Researchers Receive CAREER Awards

Five UConn faculty members received prestigious CAREER Awards from the National Science Foundation (NSF) in the 2026 cycle.

"The National Science Foundation has a history of recognizing talented, up-and-coming researchers whose work translates into important discovery as well as instructional brilliance, and UConn's five NSF CAREER awards continues that legacy," says Lindsay Distefano, UConn associate vice president for research development. "This would be an impressive accomplishment during any year and is especially noteworthy in the present research climate. The achievement recognizes the resilience of the award winners as well as the talented staff members who guided them through the process and helped their projects and potential truly stand out."

The Faculty Early Career Development (CAREER) Program is a highly competitive and prestigious award for early-career scientists. NSF CAREER funding supports researchers in establishing a strong basis for a lifetime of leadership in research and education.

These awards represent a $3.3 million total investment by the NSF in UConn's early career faculty.

Each project includes a significant educational and/or outreach element such as training undergraduate and graduate students, hosting seminars to share research, collaborating with stakeholders, and developing courses related to the work.

The faculty members who received the awards in the 2026 cycle are:

Karolina Heyduk, assistant professor, Department of Ecology and Evolutionary Biology, College of Liberal Arts and Sciences, NSF CAREER Award

Karolina Heyduk is studying photosynthetically intermediate plants to help understand the repeated evolution of complex traits. Crassulacean acid metabolism (CAM) is a form of photosynthesis where plants take in carbon dioxide at night and store it for sugar production during the day. This nighttime activity helps plants use water more efficiently and allows them to better tolerate drought. Found in about 6% of vascular plants, CAM has evolved independently at least 60 times. How CAM evolved remains a mystery, and Heyduk will address this gap by studying intermediate phenotypes (called C3+CAM) to understand their diversity and the genetic changes associated with their evolution.

Farhad Imani, assistant professor, School of Mechanical, Aerospace, and Manufacturing Engineering, College of Engineering, NSF CAREER Award

Farhad Imani is developing a new class of intelligent robotic manufacturing systems that can inspect parts, interpret multimodal sensor data, and reason through uncertainty to repair valuable components. Imani's lab is building cooperative robotic systems capable of inspecting damage in real time, making diagnoses, determining repair strategy, and adapting as conditions change. The goal is to shift from programmed automation to cognitive automation, where machines can reason through changing scenarios rather than relying solely on predefined instructions by using hyperdimensional computing. This brain-inspired framework combines multimodal sensing, engineering knowledge, physical constraints, and simulation into an interpretable decision loop.

SeungYeon Kang, assistant professor, School of Mechanical, Aerospace, and Manufacturing Engineering, College of Engineering, NSF CAREER Award

SeungYeon Kang is working on advanced manufacturing processes that operate at the micro and nanoscales. Kang is developing new ways to fabricate tiny structures in three dimensions using ultrafast lasers. Her work combines precision laser processing with advanced materials and manufacturing techniques to create highly detailed microscale and nanoscale structures that would be difficult to produce using traditional methods. By rethinking how these structures are made, there is potential to make manufacturing faster, cleaner, and significantly more flexible.

Chang Liu, assistant professor, School of Mechanical, Aerospace, and Manufacturing Engineering, College of Engineering, NSF CAREER Award

Chang Liu is developing a suite of nonlinear analysis frameworks capable of better predicting the onset of turbulence and regulating drag and heat transfer. Existing frameworks focus on linear dynamics, meaning they are liable to make serious prediction errors when dealing with channel flows in time-varying regimes like tidal currents and blood flows in the body. Developing frameworks that take nonlinear dynamics into account can help improve aviation safety during takeoff and landing, fuel efficiency, thermal management of data centers, and the energy efficiency and reliability of electronics.

Qian Yang, associate professor, School of Computing, College of Engineering, NSF CAREER Award

Qian Yang's CAREER project is addressing challenges currently preventing a wider adoption of machine learning for industrial and scientific applications that have so far been limited by the lack of large datasets on which to train models. Yang is developing algorithms capable of learning and model reduction of complex dynamical systems from computational simulation, and machine learning-enabled real-time analysis of experiments. These novel algorithms will be capable of learning differential equations and performing image segmentation for data collected in scientific experiments. The incorporation of reliable machine learning into these fields can help accelerate the pace of scientific discovery and manufacturing innovation.