Engineering smarter heart diagnostics

My passion for mechanical engineering stems from a desire to apply physics and engineering principles to solve real-world biomedical challenges. Over time, I became especially interested in the intersection of fluid mechanics, medical diagnostics, and machine learning — fields that offer immense potential to improve patient outcomes. 

My research focuses on developing non-invasive diagnostic techniques using medical imaging for assessing congenital heart diseases, as well as exploring electroosmotic flow-driven microfluidic devices, machine learning, and blood rheology analysis. Currently, I am developing non-invasive diagnostic tools using pressure-volume loop analysis to assess cardiac function in patients with Duchenne Muscular Dystrophy (DMD). The goal is to identify more sensitive diagnostic metrics like mean elastance, contractility, and energy efficiency that can detect heart problems before significant damage occurs and offer improved accuracy over traditional measures. Additionally, I am applying machine learning to evaluate the prognostic value of these metrics, enhancing diagnostic precision and ultimately improving clinical outcomes. This work is conducted in collaboration with Cincinnati Children's Hospital Medical Center. I am particularly driven by the potential to transform early detection and long-term care for vulnerable populations such as children with DMD, where timely diagnosis can dramatically improve quality of life. 

Early in my PhD, I spent nearly nine months on a project only to realize the software I was using could not perform the task I needed. Having to start over with a new tool taught me resilience, adaptability, and persistence — lessons that reshaped my approach to research and strengthened me as a researcher. Collaborating with cardiologists on interdisciplinary projects has also been deeply impactful, giving me a clearer perspective on how engineering innovations can directly influence patient care. A key part of my journey has been the mentorship of Dr. Rupak Banerjee, whose guidance has been instrumental in helping me navigate these challenges and refine my research approach. Additionally, publishing multiple peer-reviewed papers in reputable journals, leading a NIOSH-funded research project as Principal Investigator, and being recognized as a finalist in the Summer Bioengineering Conference student paper competition are some of my key accomplishments. 

I expect to graduate in 2026. After earning my PhD, I plan to advance research in cardiovascular diagnostics and biomedical innovation, working at the intersection of academia, healthcare, and industry to translate discoveries into practical tools. I am particularly passionate about developing accessible, technology-driven solutions that can improve patient outcomes and extend advanced diagnostics to underserved clinical settings. 

Outside the lab, I am deeply committed to guiding undergraduate students across various disciplines. I find great satisfaction in supporting their academic and professional growth, helping them navigate challenges and expand their potential. Additionally, I have served as a judge for the CEAS EXPO. I am a member of professional organizations such as the American Heart Association and American Society of Mechanical Engineering, which help me stay connected and growing in the field. 

Featured image at top: Israel Ajiboye is using machine learning to improve heart diagnostic outcomes. Photo/pixabay.