Short Bio

Victoria is a Ph.D candidate student in Computer Science and she is dedicated to the study of human and machine intelligence and vision. Her research sets out to identify brain-wide visual organizing principles and determine if these principles are shared by learning-based models of the ventral stream (convolutional neural networks, CNNs).

Victoria received her B.S. and M.S. in Computer Science and B.S. in Electrical Engineering at Washington University in St. Louis. She was the Co-president of Tau Beta Pi Engineering Honor Society, Missouri Gamma Chapter.

When not in the lab, she enjoys water-color painting, hiking and teaching her cockatiel Ashe singing and cool tricks.


  • Ponce Laboratory under Dr. Carlos Ponce MD. PhD. in Department of Neuroscience in Washington University in St. Louis School of Medicine

    Exploring black-box attacks using generative adversarial networks.

    Automated visual recognition has the potential to change many facets of society, from biomedical imaging to security and transportation. Convolutional neural networks (CNNs) are the best models for visual recognition, and while they show enormous promise, they are notably vulnerable to "black-box attacks" -- malicious inputs designed to make the networks make mistakes (Papernot et al. 2016, Practical Black-Box Attacks against Machine Learning). Because CNNs share many properties with the brain, we can understand what kind of attacks are particularly effective on the most resilient CNNs, by crafting attacks that manipulate activity in the brain.

    The Ponce lab has shown that it is possible to use generative adversarial networks (GANs) to maximize the activity of individual neurons in the brain, through the synthesis of artificial images. I will explore which types of GANs are best in achieving this goal, in both macaque brains and convolutional neural networks.

  • Aravamuthan Laboratory under Dr. Bhooma Aravamuthan M.D., DPhil in Department of Neurology in Washington University in St. Louis School of Medicine

    I am developing an open-field, video-based animal pose tracking framework using computer vision.

  • AIM Laboratory under Dr. Shantanu Chakrabartty in Department of Electrical Engineering in Washington University in St. Louis.

    I investigated sonification techinique that can be used to visualize high-dimensional data like images. The goal is to understand the benefits of sonification compound to visual representations especially in the context of human-in-the-loop systems