Inaugural cohort of health data science fellows named

Decorative photo of health data science.

The Translational Health Data Science Fellowship Program, a partnership between CITRIS Health, the UC Davis Clinical and Translational Science Center and the UC Davis Department of Public Health Sciences, in collaboration with the UC Davis DataLab, has announced its first cohort of seven students. 

Led by Nicholas R. Anderson, a CITRIS PI and director of informatics research for UC Davis Health, the nine-month fellowship program will support master’s degree and doctoral graduate students at UC Davis for mentored research in health data science.

2022 Fellows

  • Charlie Fornaca
    “Enabling Synthetic Data Usage for Medical Research”
    Fornaca will explore and compare different methods for generating synthetic health care data, with a goal of proposing workflows and platforms for supporting researchers at UC Davis.
  • Jiyeong Kim
    “Helping Caregivers Find Tailored Mental Health Information by Applying NLP to Online Caregiver Forums”
    Kim will search web-based caregiver forums to programmatically determine common and evolving caregiver mental health needs.
  • Linh Nguyen Ngoc Le
    “Predicting Future Atrophy Maps for Early Detection of Alzheimer’s Disease”
    Linh is developing and applying a multimodal neural network to medical images to predict future longitudinal brain atrophy in elderly patients at risk of cognitive decline.
  • Luca Cerny Oliveira
    “Deep Semi-supervised Learning for Reduced Labile Use in 1D Medical Signals”
    Oliveira will use semi-supervised learning, a combination of supervised and unsupervised machine learning methods, on one-dimensional medical signals to see if it can replace deep learning in these instances.
  • Ellen Osborn
    “Leveraging Computational Tools for Genome Analysis of a Human Pathogenic Gene”
    By identifying genes associated with autism in a large nonspeaking autistic study cohort, Osborn hopes to increase understanding of the genetic etiology of autism and contribute to improved patient clinical care.
  • Olivia Sattayapiwat
    “Advancing Evidence for Associations Between Benign Breast Diagnoses and Future Breast Cancer Risk”
    Sattayapiwat’s research applies supervised machine learning methods to longitudinal data gathered from breast imaging registries across the United States to accurately quantify their associations with breast cancer risk.
  • Weitai Qian
    “Enhanced Intrapartum Fetal Monitoring via Context-aware Data Integration”
    Qian’s research aims to increase the accuracy of transabdominal fetal oximetry to decrease in-labor risks for both babies and birthing parents during labor and delivery.