NCI Data Jamboree (Project Abstract Submission): Submission #46

serial: '46'
sid: '189148'
uuid: b372ff3f-4ad2-4c47-a03a-44a66455f4bf
uri: /nci/datajamboree/abstractsubmission
created: '1785180410'
completed: '1785180418'
changed: '1785180418'
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langcode: en
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data:
  list_of_additional_authors: {  }
  category: 'Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data'
  degree_s_: B.S.
  email: singhk8@mskcc.org
  first_name: Katina
  keywords_abstracts: ''
  last_name: Singh
  middle_initial: H.
  organization: 'Memorial Sloan Kettering Cancer Center'
  organization_address:
    address: ''
    address_2: ''
    city: 'New York'
    country: ''
    postal_code: ''
    state_province: ''
  summary: |-
    As a Research Assistant for the Bone Marrow Transplant Service at Memorial Sloan Kettering Cancer Center, I perform novel immunoassays and link high throughput biomarker data to clinical outcomes. These patients are aggregated to form cohorts in institution led initiatives and in multi-center trials for later evaluation of immunotherapies and to determine impact of conditioning regimen on success of hematopoietic stem cell transplants. Having the opportunity to work on projects across different oncology services here influenced my pursuit of an MPH in Epidemiology and Biostatistics, where I was one of three Master's students permitted to take PhD-level coursework in causal inference. 

    My introduction to using observational data analysis to isolate treatment effects in complex care plans this past semester motivated me to investigate ways I could apply these frameworks to our patient cohorts. I plan to bridge my computational and biological expertise through upcoming classes in Bayesian statistics and pathophysiology, allowing me to better isolate the complex factors that modulate cancer treatments. The Jamboree is the ideal venue to meld these methods with my insights gained from working in a premier cancer center, translating institutional experience into solutions for national oncology challenges.

    Furthermore, this event offers a chance to directly collaborate with leading experts and garner hands-on analysis experience in handling large, diverse datasets. I am particularly excited to utilize the All of Us dataset to identify ways to make personalized healthcare accessible to everyone and examine the role of environmental factors on health for specific communities. Beyond the technical work, I look forward to building lasting connections with other researchers and gaining exclusive mentoring opportunities. It would be amazing to create something what would facilitate greater discoveries in the field and to in some small way, meaningfully contribute to the public effort in the fight against cancer.
  title: 'Research Assistant'
  ttile: 'At the Intersection of Immune Monitoring and Statistical Modeling'