Key Skills

Software

AWS SageMaker

CVAT

Labelbox

Label Studio

Prodigy

SuperAnnotate

Top Subject Matter

No subject matter listed

Top Data Types

Document

Image

Medical Dicom

Top Task Types

Computer Programming Coding

Data Collection

Diagnosis

Mapping

Object Detection

Freelancer Overview

I have hands-on experience in the preparation and annotation of high-quality datasets for AI and machine learning projects. My background in biochemistry and neuroscience has equipped me with a keen eye for detail and a strong understanding of complex subjects, enabling me to accurately label and categorize both text and image data for diverse applications. I am proficient with leading data annotation platforms and have contributed to projects involving medical, scientific, and educational data, ensuring both accuracy and consistency throughout the labeling process. Key skills include data quality control, annotation tool utilization, and collaboration with cross-functional teams to refine labeling guidelines. My ability to quickly grasp technical instructions and adapt to new domains sets me apart, as does my commitment to maintaining high standards that are essential for training robust and reliable AI models.

Labeling Experience

Medical Image Annotation for Disease Detection

LabelboxImageBounding BoxPolygon

Led a data labeling project focused on annotating medical images to support AI models for disease detection. Responsibilities included drawing bounding boxes and polygons around abnormal tissue regions, as well as performing semantic segmentation to differentiate between healthy and diseased areas. The project involved labeling over 5,000 images with strict adherence to clinical guidelines and quality control measures. Regular cross-validation with domain experts ensured high accuracy and consistency, and the labeled dataset was instrumental in training robust computer vision models for diagnostic applications.

Led a data labeling project focused on annotating medical images to support AI models for disease detection. Responsibilities included drawing bounding boxes and polygons around abnormal tissue regions, as well as performing semantic segmentation to differentiate between healthy and diseased areas. The project involved labeling over 5,000 images with strict adherence to clinical guidelines and quality control measures. Regular cross-validation with domain experts ensured high accuracy and consistency, and the labeled dataset was instrumental in training robust computer vision models for diagnostic applications.

2024

Education

Work History