I was born in Colombia, and grew up in Queens, New York and Levittown, NY. I attended Johns Hopkins University from 2015-2019 where I received a B.S., double majoring in Computational Neuroscience and Biophysics, with a minor in Mathematics. In 2017, I was introduced to research by Shigeki Watanabe, where I worked for 2 and a half years studying the molecular players in ultra-fast endocytosis, a form of compensatory endocytosis following neurotransmission. In 2018, I also began working for the Global Health and Engineering lab at JHU, where I learned how to code and helped develop preprocessing pipelines and transfer learning networks for a CNN trained to identify mosquito species and genus. I discovered I enjoyed computation and thinking about problems algorithmically and mathematically, so I started working with Jian Liu in 2019 to develop mass-action membrane and protein dynamics models at the Center for Cell Dynamics, in combination with my experiments in the Watanabe Lab and in the Inoue Lab and Plotnikov Lab. After taking a cognitive class my senior year, I decided to take a 2 year post-bac in Chris Honey’s lab, where I developed behavioral experiments to probe temporal integration and separation, looked at representations of sensorimotor commands in RNNs, and learned about neural oscillations. Taking all this together, I realized that in most wet labs, we rely on ‘head models’, and computational models allow us to place our assumptions down on paper, and seeing if our models make sense internally. I strive to create biologically realistic, but practically useful, models of the mind and brain. This brought me to the Redwood Center. My other mentor is Bob Knight, and there I analyze human single neuron data. In my free time, I like to strength train, run, and have recently begun salsa dancing.
My current research at Redwood explores spatiotemporal representations in trained spiking neural networks. I am investigating temporal receptive properties in these systems, and how much extra computing power is afforded by the introduction of dynamics, and how this relates to temporal properties in the early visual system.