Andrew B
- Research Program Mentor
PhD candidate at University of California San Diego (UCSD)
Expertise
computational neuroscience, cognitive neuroscience, signal processing, human neuroscience, machine learning, theoretical neuroscience, cognitive science
Bio
I am a graduate student in Dr. Brad Voytek's lab at UC San Diego. I am broadly interested in the computational mechanisms and information processing underlying cognitive processes such as decision making and working memory. I pursue this interest through the use of computational modeling and machine learning techniques in tandem with analysis of electrophysiological recordings. I love to spend my time outside of the lab both playing and watching sports (primarily soccer, basketball, and recently beach volleyball), watching movies, and playing board games. I enjoy mentoring because it challenges me to grow and learn along with students and broadens my horizons beyond the specific domain of research I do on a day-to-day basis.Project ideas
The Dynamics of Rhythmic Brain Activity in (you choose!)
Measures of human brain activity are often composed of activity from millions of neurons. Neural oscillations, which are rhythmic features of this large-scale brain activity, have been shown to change both in neuropsychiatric disease states and dynamically during cognitive functions. In this project, you will perform a literature review to understand what research has already been done to elucidate oscillatory changes in a neuropsychiatric disease or cognitive function of your choosing. Then, you will use one of the many openly available datasets to pursue research questions of your own that the literature review inspired, allowing you to learn and apply state-of-the-art techniques for the analysis of neural oscillations.
Machine Learning for Brain-Machine Interfaces
Brain-Machine Interfaces (BMIs) are an important and promising area of medical neuroscience research that have the potential to dramatically improve the quality of life for many patients. One of the most difficult technical problems for BMIs is reading out the movement intentions of an individual from their brain activity. In this project, you will use an open dataset of motor cortical activity and machine learning techniques to determine how a prosthetic arm should be moved based on a person's brain activity, allowing you to gain valuable experience in applying machine learning to neuroscientific data.