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Label Studio
Doccano
Argilla
CVAT
Roboflow
SuperAnnotate
Image
Geospatial Tiled Imagery
Segmentation
Classification
Point/Key Point
Object Detection
Question Answering
Evaluation/Rating
Data Collection
Function Calling
RLHF--- I bring substantial experience in data labeling, annotation, and curation from both industry and research settings, though under different job titles. ---At Apple's Product Safety lab, I performed systematic data annotation on X-Ray and CT imagery (identifying & classifying), and documenting failure modes across thousands of battery samples. This required consistent labeling standards, cross-referencing multi-modal data (imaging + electrochemical + physical teardown), and meticulous quality control to ensure every data point was traceable to a root cause. --- At UNSW, I led the creation of a structured materials dataset: annotating SEM/TEM micrographs with compositional and performance labels, then building a visualization solution that linked composition/microstructure/properties, which directly enabled predictive modeling of untested material compositions. --- The differentiators of me in AI training data work: --- (1) I generate and validate simulation data using DFT, molecular dynamics, and CFD tools for intermetallic, which means I understand how synthetic training data is produced and where its failure; --- (2) I am fully bilingual (Chinese/English) and can handle annotation tasks across both languages. Finally, my Apple experience trained me to treat data quality not as a checkbox but as a safety-critical requirement, the same rigor I'll bring to building ground-truth datasets for AI.
ImagePoint/Key PointProject Overview Built a labeled dataset of battery failure modes for Apple products, combining non-destructive imaging (X-Ray, CT) with electrochemical diagnostics and physical teardown results to create ground-truth labels for safety-critical defect classification.
ImagePoint/Key PointProject Overview Built a structured, multi-modal labeled dataset mapping intermetallic compound compositions (binary In-X, Al-X and ternary In-Al-X systems) to their microstructural features and functional properties. The dataset enabled predictive modeling of untested material compositions within the same system.
Master of Materials Science, Materials Technology
Bachelor of Materials Science and Engineering, Materials Science and Engineering
Battery Test Engineer
Process Technician (Process R&D)
