Zhiyuan C.

Key Skills

Software

Don't disclose

Other

Top Subject Matter

Biomedical / Clinical data standards (SDTM, ADaM, TFL)

Biomedical data annotation and structured labeling (clinical variables, tables)

Top Data Types

Text

Document

Medical Dicom

Image

Top Task Types

Mapping

Fine-tuning

Data Collection

Classification

Text Generation

Evaluation/Rating

Entity (NER) Classification

Freelancer Overview

AI Data Annotation & Clinical Dataset Review (Biomedical / Clinical Data Project Experience) | New York, NY. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Master of Arts, Columbia University Irving Medical Center, Department of Biomedical Informatics (2027) and Bachelor of Science, Zhejiang University and University of Edinburgh (2025). AI-training focus includes data types such as Medical and DICOM and labeling workflows including Evaluation, Rating, and Entity (NER) Classification.

Labeling Experience

AI Data Annotation & Clinical Dataset Review (Biomedical/Clinical Data Project Experience), New York, NY

OtherEntity (NER) Classification

Conducted AI-assisted clinical dataset review for SDTM, ADaM, and TFL-style biomedical outputs against predefined statistical and clinical data specifications. Assessed whether AI-generated datasets conformed to expected variable structures, naming conventions, population flags, derivation logic, and analysis-ready formatting. Documented discrepancies by categorizing issues into mapping problems, statistical logic problems, or AI generation errors for downstream correction workflows. • Compared AI-generated outputs against source specifications, shells, and expected clinical programming logic. • Performed completeness, traceability, and cross-standard consistency checks across SDTM, ADaM, and TFL outputs. • Summarized review findings with possible root causes and recommended corrections for technical/statistical review. • Validated structured biomedical elements including subject-level variables, treatment groups, indicators, analysis parameters, and table outputs.

2024 - Present

Structured Biomedical Data Annotation (Selected Project Experience)

Don't discloseMapping

Annotated and validated structured biomedical data elements according to predefined project guidelines and domain-specific rules. Reviewed labeled outputs for consistency, completeness, and logical validity while applying biomedical domain knowledge to resolve ambiguity. Maintained organized documentation of annotation decisions and edge cases to support traceable labeling outcomes. • Labeled subject-level variables, treatment groups, population indicators, and analysis parameters. • Interpreted annotation guidelines and ensured consistent application across complex review tasks. • Performed logical checks to confirm derivations and population flag assignments aligned with specifications. • Recorded documentation for labeling decisions, discrepancies, and edge cases.

2023 - Present

Education

Work History