Spotlight: UMaine’s 2022 NSF Career Award Recipients

The year 2022 marked a first for UMaine, as five faculty members were selected in the same year for the prestigious National Science Foundation (NSF) CAREER Award, a premier early career funding mechanism, which is intended to support enduring success in scholarship, teaching, and public service.

The National Science Foundation (NSF) CAREER Award, is considered one of the most prestigious awards for junior faculty members in the U.S. Awarded to early career researchers who have demonstrated the potential to serve as academic role models and to lead advances in the mission of their department or organization, the award provides funding for up to five years to support the research and educational activities of the recipient. This support can be critical for faculty members who are just starting their careers and trying to establish themselves in their field. In addition to providing funding, the NSF CAREER award also serves as recognition of the recipient’s achievements and potential, which can help to boost their career and increase their visibility in the scientific community. 

This prominent award recognizes the exceptional potential of the faculty and their research. In 2022, UMaine’s Justin Dimmel, Salimeh Yasaei Sekeh, Babak Hejrati, Qian Xue, and Yingchao Yang all received awards.

Justin Dimmel

Assistant Professor of Mathematics Education and Instructional Technology

Justin Dimmel received an award for his project to investigate the transformative educational potential of using virtual reality technology.

The emergence of extended reality (XR) technologies, such as virtual and augmented reality, offers a profound shift in our capacities for representing and interacting with information. Three-dimensional figures can now be represented as diagrams that appear to extend into space in ways that are free of material or physical constraints. They can be rendered at any size, in any orientation, and at any position in space, and can thereby realize a far more varied set of mathematical concepts than what is possible with physical models. 

The goal of Dimmel’s project is to investigate the transformative educational potential of these representations and to generate a knowledge base that teachers, teacher educators, and researchers can use to reimagine the learning and teaching of geometry. 

Salimeh Yasaei Sekeh

Assistant Professor of Computer Science

Salimeh Yasaei Sekeh’s project investigates three desirable properties when developing deep networks, including performance, efficiency, and robustness. Her project also includes a comprehensive plan to integrate research results into inclusive, diverse, and cross-disciplinary educational multilevel programs by funding graduate research assistants, summer research fellowships for high-school students and teachers, and organizing a hybrid (online and in-person) deep-learning boot camp.

The overall goal of her research program is to develop a comprehensive and fundamental understanding of the robustness and computational aspects of deep networks by leveraging tools and concepts from probability, information theory, and statistics. 

The project aims to make critical advances in areas such as proper formulations of subnetwork adversarial robustness, characterizing transferability via curriculum learning, and in  developing efficient approaches for reducing computational complexity involved in training, among others. 

The theoretical and methodological outcomes of this cross-disciplinary project will broaden the prior knowledge of deep learning, a type of machine learning, and will improve prediction, exploration and detection applications of machine-learning models. 

Babak Hejrati

Assistant Professor of Mechanical Engineering

Through his award by the NSF Disability and Rehabilitation Engineering (DARE) program, Babak Hejrati will establish a framework for helping people with mobility issues — such as older adults with mobility decline and those who have had a stroke — to improve their walking ability using wearable robots. 

People with walking problems due to aging or neurological disorders such as stroke and Parkinson’s disease often participate in gait training therapy to improve their walking ability. Walking is a complex skill that requires highly coordinated leg and arm movements. Current methods for gait training often focus on improving leg movements, but often overlook the importance of arm movement, particularly arm swing, which impacts stability, balance and the efficiency of energy use while walking. 

Hejrati plans to develop two new wearable robotic devices to examine how the neural circuits that control limb movements interact while walking at different speeds to produce coordinated arm and leg movements in subjects without mobility issues. In patients with mobility issues, the robotic devices will be able to help induce proper whole-body response and enhance their walking ability. 

Qian Xue

Assistant Professor of Mechanical Engineering

Assistant professor of mechanical engineering Qian Xue researches the sensing ability of seal whiskers, which have attracted increasing research interest because of their exceptional sensitivity and accuracy. Previous studies have shown that blindfolded seals can use their whiskers to track the disturbances left behind by moving objects in the water, known as hydrodynamic trails, that were generated several minutes before, as well as discriminate the size and shape of upstream objects through their wakes.

However, relatively little is known about the mechanisms of seal whisker sensing. Xue’s research looks at how the unique geometry of seal whiskers responds to different vibrations in the water, including self-induced vibrations in calm water and wake-induced vibrations from other objects at both the single-whisker and whisker-array levels.

Xue will use a tool known as an immersed-boundary-method based fluid-structure interaction computer model to simulate the vibrations of a single whisker and multiple whiskers in a wide range of parameters. The simulation results will be validated by comparing them to the previously obtained experimental measurements in order to better understand how the whiskers respond to fluid vibrations.

Yingchao Yang

Assistant Professor of Mechanical Engineering

Ultrathin two-dimensional (2D) nanomaterials have been extensively researched for use in devices like electronics, photonics, batteries and more. The stability of components made from the materials is critical to their reliability, but toughening the brittle materials — making them more resistant to fractures, for example — often comes at the cost of their mechanical strength. What’s more, 2D high-entropy materials (HEMs), nanomaterials that consist of multiple elements, are asymmetrical, and thus harder to fracture. 

Yingchao Yang’s research goal is to study the asymmetrical fractures of 2D HEMs. He will use the NSF funding to pursue four research objectives: fabricating stable 2D HEMs; conducting in situ tensile testing in a scanning electron microscope to visualize the deformation and fracture scenarios of 2D HEMs and their ripple effects understand the various impacts on the materials’ mechanical behaviors; developing and applying a multiscale framework to simulate fracture behaviors of 2D HEMs with focus on crack initiation and crack propagation; and visualizing crack evolutions at the atomic level via in situ tensile testing using transmission electron microscopy.

The Office of Research Development (ORD) offers workshops, protected time writing sessions, and individual consultations to faculty for this and related early career faculty development funding programs.  For more information please contact Associate Director of Research Development, Saul Allen (