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VideoI am a detail-oriented and motivated medical student with hands-on experience in data annotation and labeling, particularly within healthcare and scientific research domains. My work with Outlier and Alligner has equipped me with practical skills in machine learning, statistical analysis, and large-scale data processing. I have a strong foundation in both wet and dry laboratory techniques, including image analysis using tools like ImageJ, and have managed confocal microscopy data for biological research projects. My passion for digital health is reflected in my development of health-related blogs and innovative app projects, where I focus on improving health literacy and supporting neurodivergent communities. I am eager to apply my analytical skills and domain knowledge to contribute high-quality, accurate training data for AI and machine learning applications.
With this project, I was mainly tasked to compare AI generated answers to questions, as well as assessing whether questions were "valid" or "invalid".
With this project, I was tasked to identify the relationship between different videos in art style and topic, classify the videos under a particular topic.
Within this project, I completed different tasks including image tasks, particularly focusing on placing a bounding box around certain images, identifying the relationship between two images, identifying objects within certain classes, as well as identifying object distortion.
Bachelor of Medicine, Bachelor of Surgery, Medicine
General Certificate of Education Advanced Level, Biology, History, Chemistry
GCSE English and Science Tutor
Peer Mentor Volunteer
