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Research
I am interested in low rank models, high dimensional statistics, optimizations and geometry related problems in machine learning, such as computational topology.
I am also generally fascinated by interdisciplinary problems in data science.
The following is a list of past research projects:
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Enriched spatial analysis of air pollution: Application to the city of Bogotá, Colombia
Zhexu Jin, Mario Andrés Velásquez Angel, Ivan Mura, Juan Felipe Franco
Front. Environ. Sci., 2022
[doi]
[bibtex]
[code]
We used spatial temporal kriging to identify a highly polluted cluster located in the south-west cluster in the city of Bogotá. Within this cluster, we observe a disproportionate representation of people from several vulnerable groups.
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Towards Geometry-Aware Cell Segmentation in Microscopy Images
Zhexu Jin, Gaoyang Li, Huansheng Cao, and Dongmian Zou
Neurips 2022 Workshop: Medical Imaging Meets Nuerips
[doi]
[abstract]
[poster]
Proposed to perserve geometry of the instance segmentation for medical imaging using losses inspired by persistent
homology.
We sped up the loss computation via a 1-dimensional simplification and implemented the new loss based on lower star
filtration. Benchmarked the proposed method against other commonly used instance segmentation methods
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Lab/Discussion Teaching Assistant:
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STAT 107 Data Science Discovery (Fall 2023*, Spring 2024*)
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Blackwell Summer Scholars Program (Summer 2024)
*: List of Teachers Ranked as Excellent By Their Students
Grading Teaching Assistant:
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STAT 426 Statistical Modeling II (Fall 2024, Spring 2025)
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STAT 511 Mathematical Statistics (Fall 2025)
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Basically Blog Posts
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More to come!
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This guy makes an awesome website.
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