NCI Data Jamboree (Project Abstract Submission): Submission #2

Submission information
Submission Number: 2
Submission ID: 183015
Submission UUID: 83b183a5-8018-47ee-9eb4-fb7c78f571cb

Created: Mon, 06/08/2026 - 20:30
Completed: Mon, 06/08/2026 - 20:30
Changed: Mon, 06/08/2026 - 20:30

Remote IP address: 10.208.24.28
Submitted by: Anonymous
Language: English

Is draft: No
First Name Megha
Middle Initial B.
Last Name Srivastava
Degree(s) B.S./M.S. in Computer Science
Position/Title/Career Status PhD Student in Computer Science
Organization Stanford University
Organization Address Stanford
Email megha@cs.stanford.edu
List of Additional Authors
Abstract Category Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data
Abstract Keywords machine learning, AI-readiness, distribution shift, causal inference, confounding variables, language models, LLMs
Abstract Title Project Seeker
Abstract I am a PhD student in Computer Science, with significant experience in machine learning, large language modeling, and human-AI interaction. I have recently been transition my research towards applications of AI in medicine, healthcare, and drug discovery, and hope to understand what challenges exist on the dataset-level, and what are ideal datasets that can help push different problems forward. One research area I am particularly interested in is challenges of distribution shift -- e.g. mismatch between the training dataset and test time inference, and how to tackle that. I am particularly curious about methods for identifying potential confounding variables that are unmeasured in the current dataset. My hope is to join a project that can help improve the quality and availability of oncology datasets for machine learning research.