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Xing Han Lu

SWE Intern and CS Student

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About Me

My research interests are in Deep Learning, and application of Machine Learning in Healthcare and Public Health. I also educate high school students about STEM.

Experience

Plotly

Software Engineer Intern

Develop Machine Learning apps using Dash and Plotly. Projects completed include interactive visualization of high-dimensional datasets, object detection enhancement app, and real-time model training viewer.

Surveillance Lab, McGill Clinical and Health Informatics

ML Research Intern

Applied Machine Learning Research at the Surveillance Lab, an Epidemiology lab within the McGill University Faculty of Medicine. The paper was presented at the 2018 AAAI Joint Workshop on Public Health Intelligence, which you can find here.

Education

McGill University

Sept 2017 - May 2020

Honours B.Sc. in Computer Science

Marianopolis College

Sept 2015 - June 2017

Diploma of Collegiate Studies (DEC)

Projects

You can find below a sample of my projects. Click here to see the complete list.

Xing Han Lu's Dash Cytoscape Project

Dash Cytoscape

Dash Cytoscape is a graph visualization component for creating interactive web-based networks. It extends and renders Cytoscape.js, and can customized purely using Python.

Docs | Project Repo
Xing Han Lu's SVM Project

Support Vector Machine Explorer

This app lets you explore Support Vector Clustering (a type of SVM) with UI input parameters. Toy datasets and useful ML metrics plots included. It is fully written in Dash + scikit-learn.

Demo App | Project Repo

Live Model Training Viewer

For every Deep Learning models, keeping track of accuracy and loss is an essential part of the training process, since they indicate how good your models are. This app is a real-time visualization app that monitors core metrics of your Tensorflow graphs during the training, so that you can quickly detect anomalies within your model.

Demo App | Project Repo

Object Detection App

This app provides useful visualizations about what's happening inside a complex video in real-time. The data is generated using MobileNet v1 in Tensorflow, trained on the COCO dataset. The video is displayed using the community-maintained video component.

Demo App | Project Repo

Image Processing App

This app wraps Pillow, a powerful image processing library in Python, and abstract all the operations through an easy to use GUI. All the computation is done backend through Dash, and image transfer optimized through session-based Redis caching and S3 storage.

Demo App | Project Repo

Skills

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