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Data Annotation Tech
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ClassificationWorked on multiple platforms and have extensive experience with data/code validation, H2H image ranking and evaluation, and prompt creation. My background consists of studying both biochemistry and software engineering, as well as having experience in both healthcare and wet-lab experiments. All these experiences inform my abilities to work with AI systems and models in order to impart my unique skillset.
Worked on tasks related to H2H comparisons of up to 8 AI images, judging them based on 4 criteria axes. Additionally wrote prompts to generate and resample images via RLHF.
I contributed to the development and evaluation of machine learning models for classifying natural disaster images. I performed exploratory data analysis to identify relevant image features and assisted in optimizing classification models for improved performance. My work involved collaboration with a cross-functional team to handle and label visual datasets according to disaster type and severity. • Labeled image datasets to train machine learning classifiers distinguishing between types of disasters and damage extent. • Participated in the assessment and refinement of key features necessary for reliable model predictions. • Tested and compared the performance of support vector machines and CNNs for improved classification accuracy. • Supported data preparation workflows essential for robust model evaluation and F1 score improvement.
Worked on multiple projects that either required verification of AI responses relating to data analysis/science code written in Python and/or R and creating and evaluating prompts related to coding and programming languages such as Python, HTML, and JavaScript.
Master of Molecular Science and Software Engineering, Molecular Science and Software Engineering
Bachelor of Science, Biochemistry
Volunteer Mentor
Software Development Engineer Intern
