NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #5

serial: '5'
sid: '143910'
uuid: 7ffe803f-d47a-42b4-84f4-8601e3d11bc5
uri: /nci/ods-data-jamboree/abstractsubmissions
created: '1748834403'
completed: '1748834403'
changed: '1748834403'
in_draft: '0'
current_page: ''
remote_addr: 10.208.28.181
uid: '0'
langcode: en
webform_id: nci_office_of_data_sharing_abstr
entity_type: node
entity_id: '2107'
locked: '0'
sticky: '0'
notes: ''
metatag: meta
data:
  category: 'Development or refinement of analysis pipelines or AI/ML algorithms'
  degree_s_: M.S.
  email: zifan.gu@utsouthwestern.edu
  first_name: Zifan
  keywords_abstracts: 'deep learning, predictive modeling, large language models, translational medicine'
  last_name: Gu
  middle_initial: ''
  organization: 'UT Southwestern Medical Center'
  organization_address:
    address: ''
    address_2: ''
    city: 'Dallas, TX'
    country: ''
    postal_code: ''
    state_province: ''
  other_please_specify_: ''
  summary: |
    My expertise is in developing machine learning pipelines for multimodal data, including both structured and imaging (H&E, multiplex) data. One of my key strengths is being able to communicate with both data scientists and clinicians, supported by my formal training in health data science and bioinformatics. I’m looking to join an interdisciplinary team that not only focuses on algorithm development but also considers how these models can be deployed into the real world.

    I’m particularly interested in projects that incorporate large language models as part of the predictive modeling pipeline, addressing outcomes such as remission, mortality, or disease progression. I’m well-traversed in HPC using PyTorch and Hugging Face, and I’d be excited to join a team that plans to use AWS or GCP as their computing platforms.  
  title: 'Graduate student'
  ttile: 'Looking for a project team to join!'
  upload_abstract: ''