Posts classified under: Predoctoral Trainees

As a Ph.D. candidate in Mechanical Engineering, my research focuses on the vital role of cerebrospinal fluid (CSF) and glymphatic flow in maintaining brain health and function. CSF acts as a protective cushion for the brain, absorbing shocks and reducing the risk of injury, while also playing a crucial role in waste removal by transporting metabolic byproducts and toxins away from the brain. The glymphatic system enhances this waste removal by facilitating the clearance of harmful proteins, such as beta-amyloid, which are linked to neurodegenerative diseases like Alzheimer’s. My research focuses on exploring the mechanisms of fluid, particle, and ion transport within the glymphatic system, as well as investigating the effect of external factors, such as head impact, on the CSF flow. By integrating cutting-edge engineering approaches with bioscience and clinical research, I aim to advance our understanding of brain health and develop innovative solutions. Additionally, I incorporate data science techniques to combine numerical modeling and experimental data, enhancing the accuracy and applicability of the findings for translational research in neurological disorders.

Mentor: Mayumi Prins, Ph.D.

My research combines insights from neuroscience, psychology, and computer science to understand human learning and memory. I am interested in how the brain supports working memory and context-dependent statistical learning where temporal regularities are consistent within an environment but interfere between environments. I use machine learning algorithms to relate brain activity to behavioral measures, and I model behavioral results with neural networks.

Mentor: Jesse Rissman, Ph.D.

Sam is a 2nd year Masters student in the Dept. of Bioengineering at UCLA, transitioning to the Ph.D. program. He previously attended Azusa Pacific University where he played collegiate football and received a B.S. in Systems Engineering in 2019. His current research focuses on the system design of  a synchronous behavioral and functional ultrasound imaging platform to acquire information about functional network connectivity changes after traumatic brain injury. His research interests include computational neuroscience, machine learning, and graph theory to understand plasticity and working memory in the whole brain.

Mentor: Neil Harris, Ph.D.