Conducted research on small object detection using Generative Adversarial Network in a two-stage object detection framework, which was published in ICCV 2019. Also, submitted a paper to CVPR 2020 about weakly-supervised object localization task.

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In Fall 2019, I have worked as a grader for a computer vision course at UMass, Amherst. I have involved in grading and developing assignments for the course.

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During Data Science for Common Good Fellowship at the University of Massachusetts, Amherst, my colleagues and I built a robust detection based model for The Nature Conservancy to classify camera trap images capture in the wild. Our final deployed model got published as an open-source tool that any ecologist can utilize to analyze wildlife images reducing hours of the manual load.

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While working in the lab, I was involved in two projects "Quantifying Inclusive Green Growth (IGG) Metric" and "Wind Farm Clustering". I had a chance to explore and apply various state-of-the-art techniques such as topic model, clustering algorithms as well as a synonym and pronoun detection. In the end, I had a chance to give my first talk about IGG project at the Hanse-Wissenschaftskolleg Institute for Advanced Study in Germany. Also, for the wind farm clustering project, our team successfully built a data-driven model embedded on a modeling platform that helps evaluate and meet growing energy demands in the U.S.

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As a signal processing analyst, I have analyzed data to produce daily reports. I implemented and distributed an API based on the natural language process to facilitate the analysis, which decreases the processing time from an hour to ten minutes. I also worked as a squad leader for six months and led six squad members. Awarded a certificate in the recognition of being an exemplar squad leader.

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