Bioengineering | College of Engineering
Predicting Recovery in Heart Failure Using Microscopy and Image Processing
Frank Sachse, Associate Professor
Patients with end-stage heart failure (HF) benefit from the implantation of left ventricular assist devices (LVADs). The two primary functions of these devices are first to restore cardiac output by active propulsion of blood from the left ventricle to the aorta and second to produce mechanical unloading of the left ventricle. Several studies demonstrated that a significant number of patients (‘responders’) with end-stage dilated cardiomyopathy and end-stage HF can recover substantial cardiac function following left ventricular unloading.
Patients with chronic HF that rely on implanted LVADs are usually placed on a list of individuals destined to receive heart transplants. This list includes responders as well as non-responders. Clearly it would be desirable that potential responders undergo clinical protocols, which might lead to cardiac recovery and thus help to preserve hearts for other patients.
A critical barrier to the treatment of end-stage HF patients exists because, until now, it has not been possible to predict at time of LVAD implantation if a patient will respond to unloading with sustained cardiac recovery. Our prior studies suggest that we have a criterion that will allow us to decide whether a patient is likely to be a responder. The criterion is derived from microscopic images of cardiac tissue that are analyzed with methods of image processing.
The stipend for this SPUR project is funded by an American Heart Association grant awarded to Dr. Stavros Drakos, MD, PhD.
Materials Science & Engineering | College of Engineering
Developing Sustainable Energy Storage Using Machine Learning
Taylor Sparks, Assistant Professor
With the goal of limiting or replacing non-renewable energy sources, extensive research has gone into renewable forms of energy, such as solar and wind power. With this increased research drive, demand has increased, efficiency has increased, costs have dropped, and implementation of these technologies has extended globally. While these sources of renewable energy are in a position to supply world energy demand, one huge problem stands in their way: continuous supply is not always available and storing excess energy generated during peak times is not possible with current battery technologies. As demand for sustainable and “green” energy continues to rise, more efficient and cost-effective batteries are becoming necessary. Typically, battery material development and testing has been a time-consuming endeavor with low odds for success, however new research paths in materials science are beginning to change this. These paths, which the PI is a pioneer in, include utilizing machine learning techniques to predict material performance before synthesis ever occurs. If machine learning techniques are applied to study battery materials, battery efficiencies and cyclability as a function of material combinations can be studied to identify which materials stand to have high success rates and high-performance characteristics when implemented into batteries. There is a long list of materials which need to be assessed for potential applications in batteries. A variety of tasks will need to be undertaken for this research, including gathering, overseeing, and preparing data for use in machine learning algorithms.
School of Computing | College of Engineering
Developing Software to Support Stronger Time Management
Jason Wiese, Assistant Professor
Applying strong time management behaviors is very important; for example, it correlates with both academic performance and job performance. For many young adults, the transition from high school to college represents a major change in the amount of control they have over their time, and the literature shows that many students struggle with this transition because they do not have the time management skills to handle this freedom on their own. A wide variety of software exists to support people in applying their existing time management strategies. On the other hand, there is very little software to support people in improving their time management practices. This project focuses on developing software to support undergraduate students in improving their time management practices.
How should such software be designed? That is the question we aim to explore in this project. Time management literature suggests a variety of principles and strategies. This project will explore software solutions that may improve support for developing time management skills. Adopting new time management practices is a type of behavior change. Behavior change is a topic of broad interest, often discussed in a health context. The results of this work will also have broader implications for opportunities for technology to support behavior change.