Computational Neuroimaging Science Lab

Graduate researcher under Dr. Kilian Pohl

Stanford University

Spring 2022 - present

  • Designed a training technique for deep learning models to extract unobserved features from their own latent-space representations

  • Applied method to train a model to learn a neurocognitive disorder classification task while learning to track brain age evolution

  • Succeeded in representing brain age through self-learned latent-space curves without dropping classification performance

  • Obtained 0.96 R 2 -score on extracting patients’ ages from their MRI scans while retaining the model’s original task performance

Stanford Machine Learning Group

Graduate researcher under Dr. Andrew Ng

Stanford University

Spring 2022 - present

  • Designing a pre-training automated method to remove mislabeled training samples on supervised learning image classification / detection tasks through embedding-space properties and gradient analysis

  • Achieved SOTA mislabel detection results on CIFAR10 and CIFAR100

  • Intended application will attempt to remove corrupted auto-labeled satellite images identifying wind farms and oil platforms

GT-LIGO Research Group

Undergraduate researcher under Dr. Laura Cadonati

Georgia Tech

Fall 2019 - Spring 2021

  • Researched computational models for glitch detection and classification on gravitational wave data

  • Work as a member of the LSC - the Laser-Interferometer Gravitational Waves Observatory Scientific Collaboration - on the computational efforts to improve data quality

  • Developed a statistical study on the efficiency of data quality channels in identifying glitches in gravitational wave data

  • Developed a Machine Learning model to identify and classify glitches with comparable results to quality channels