Data Scientist
2014 - PRESENTCanada's Michael Smith Genome Sciences Centre- Developed multiple pipelines for analyzing massive genomics data in Python.
- Wrote tests with over 80% coverage for an analysis pipeline and developed a progress tracking dashboard in Django.
- Leveraged Google Cloud Platform to deploy massive computation with 15k cores running across three continents.
Technologies: Machine Learning, PyData Stack, Django, Google Cloud PlatformLead Developer
2014 - 2015TotalWebmaster- Developed both back-end using the WebApp2 framework and front-end in AngularJS.
- Designed the database in Google Cloud Datastore.
- Developed the daily task pipeline of fetching data from Google Search Console and exporting the data into Google Cloud Datastore and Google BigQuery tables.
- Designed and built the front-end dashboard in NVD3.
- Developed the build-deploy-test workflow in Gulp.
Technologies: Google App Engine
Machine Learning Developer in Vancouver, BC, Canada
Member since March 5, 2015
Zhuyi is a skilled Python developer with over seven years of experience. He is also proficient in JavaScript and Scala. He has collected experience in a wide array of technologies with strong focuses on PyData stack, Django, and cloud computing. He is very detail-oriented and proactive, with great communication skills.
Zhuyi is now available for hire
Portfolio
- Canada's Michael Smith Genome Sciences CentreMachine Learning, PyData Stack, Django, Google Cloud Platform
- TotalWebmasterGoogle App Engine
Experience
- Python, 8 years
- Pandas, 5 years
- Google Cloud Platform (GCP), 5 years
- JavaScript, 5 years
- Scrapy, 4 years
- Django, 4 years
- Machine Learning, 4 years
- Apache Spark, 2 years
Availability
Part-time
Preferred Environment
Linux, Emacs, Git, tmux
The most amazing...
...web app I've built tracks usage from over eight supercomputers across Canada for my research group.
Employment
Experience
- Answer Set for Stanford CS229 (Development)https://github.com/zyxue/stanford-cs229/
Worked out and provided answers to all problem sets in one of the most popular machine learning courses online, CS229 by Stanford.
90 GitHub stars
1.5k views biweekly - Sutton-barto-rl-exercises (Development)https://github.com/zyxue/sutton-barto-rl-exercises
Learning reinforcement learning by implementing the algorithms from reinforcement learning an introduction Edit
30 GitHub stars - Ncbitax2lin (Development)https://github.com/zyxue/ncbitax2lin
Convert the whole NCBI taxonomy into lineages of all known organisms.
For example, the taxonomic lineage of human beings is Eukaryota > Chordata > Mammalia > Primates > Hominidae > Homo > Homo sapiens.
37 GitHub stars - SamFormat (Development)http://www.samformat.info/
SamFormat interprets genomics code on the client side in a highly responsive manner.
Google rank 2nd if searching for "sam flag" as of this entry. - RLjs (Development)https://rljs.herokuapp.com/
Reinforcement learning algorithms implemented in JavaScript and React, demonstrated with Gridworld toy example.
- TPR Parser (Development)https://github.com/MDAnalysis/mdanalysis/blob/master/package/MDAnalysis/topology/tpr/utils.py
TPR Parser closes a 5-year old feature-request ticket for MDAnalysis, a software package for analyzing molecular dynamics (MD) data in Python. TPR is the file that contains all the structural topology information and running parameters of a MD system in Gromacs encoded by XDR Standard (RFC 1014).
- A Comprehensive Introduction To Your Genome With the SciPy Stack (Publication)Genome data is one of the most widely analyzed datasets in the realm of Bioinformatics. The SciPy stack offers a suite of popular Python packages designed for numerical computing, data transformation, analysis and visualization, which is ideal for many bioinformatic analysis needs. In this tutorial, Toptal Software Engineer Zhuyi Xue walks us through some of the capabilities of the SciPy stack. He also answers some interesting questions about the human genome, including: How much of the genome is incomplete? How long is a typical gene?
Skills
Languages
Python, JavaScript, HTML, SQL, Scala, CSSFrameworks
webapp2, Scrapy, Django, Flask, Django REST Framework, Apache Spark, Bootstrap, Redux, AngularJSLibraries/APIs
Pandas, Scikit-learn, SciPy, Matplotlib, NumPy, TensorFlow, D3.js, Google Maps SDK, jQuery, ReactTools
Emacs, Pytest, IPython, BigQuery, GitPlatforms
Jupyter Notebook, Google App Engine, Linux, Heroku, Google Cloud Platform (GCP), Google Cloud Engine, Amazon Web Services (AWS), MeteorStorage
Google Cloud Storage, Google Cloud Datastore, MySQL, PostgreSQL, MongoDBOther
Bioinformatics, Machine Learning
Education
- Master of Science degree in Data Science2010 - 2013University of Toronto - Toronto
Certifications
- Google Cloud Certified Professional Data EngineerAUGUST 2018 - SEPTEMBER 2020Google Cloud
