Paulina P
- Research Program Mentor
PhD candidate at University of California San Francisco (UCSF)
Expertise
bioinformatics, computational biology
Bio
My research sits at the intersection of biostatistics and visual neuroscience. I am a tool-builder and methods developer inspired to derive biomedical insights from neural recording data. I worked in both biotech startups and academic research labs. Outside of lab, I enjoy playing tennis, trying new cafés around the world, and dancing. I am originally from Guatemala and am doing my best to promote scientific research in Latin America. One of my strongest motivations for getting a PhD is being able to teach and mentor students. It is incredibly fulfilling to witness my students' research journey and the unexpected paths science takes them to!Project ideas
Visual Sensory Processing
use publicly available large-scale recordings of retinal ganglion cell responses to various visual stimuli, such as static images, moving patterns, and natural scene movies. By training convolutional neural networks (CNNs) and recurrent neural networks (RNNs) on these datasets, the project aims to create models that can predict ganglion cell activity based on the visual input and vice versa. Additionally, the project would employ explainable AI techniques to interpret the learned features and understand the underlying neural coding mechanisms. The ultimate goal is to build a comprehensive computational framework that can simulate the retina's processing capabilities, contributing to advancements in artificial vision systems and neuroprosthetics.
Characterizing microrganisms in human eye lids
The CDC recently reported a case of a bacterial agent that infected pharmaceutical eye drops, leading to sever infection in a host of patients. Using publically available data on the ocular microbiome, we can identify genetic signatures of pathogens such that outbreaks like this are detected sooner. Eye infections are common and there are multiple sequencing efforts aimed at mapping their manifestation. By compiling these and building a public resource, we can help clinicians rule out known pathogens and more rapidly develop treatments against new ones.