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raw-resume2021.html
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---
layout: default
title: Raw Resume
---
<div id="raw-resume-content">
<div>
<h2 class="children-center">{{site.name}}</h2>
</div>
<div>
<div class="children-center">
<div class="inline-block"><b>Phone:</b> {{site.phone}}</div> |
<div class="inline-block"><b>Email:</b> <a>{{site.email}}</a></div> |
<div class="inline-block"><b>Located in:</b> {{site.location}}</div> |
<div class="inline-block"><b>Website:</b> <a>{{site.github}}</a></div>
</div>
</div>
<hr>
<div>
<h5>Technical Skills</h5>
<div>- <b>Programming: </b>Python, Java, R, C/C++, HTML/CSS, JavaScript, SQL, Groovy, bash/shell, powershell</div>
<div>- <b>Python: </b>Plotly Dash, flask, celery, sqlalchemy, socket, pandas, scikit-learn, keras, tensorflow, pyspark, matplotlib</div>
<div>- <b>Database/Cache: </b>SQL Server, MySQL, MongoDB, Redis, RabbitMQ</div>
<div>- <b>Pipeline: </b>Airflow, MLflow, Jenkins, Azure DevOps, Github Actions</div>
<div>- <b>Deployment: </b>Nginx, Docker, Terraform, Cloud-init, SaltStack, Kubernetes(kOps), Helm, cythonize, gunicorn</div>
<div>- <b>AWS: </b>VPC, EC2, EBS, RDS, S3, EFS, Route53, LoadBalancer, CloudFormation, CloudWatch, Tag Editor</div>
<div>- <b>Azure: </b>Azure SQL, Storage, Data Lake Gen 2, Data Factory, Data Explorer(Kusto), Azure Function</div>
</div>
<hr>
<div>
<h5>Experience</h5>
<div class="space-between bold-text">
<div>Software Engineer(DevOps)</div>
<div>July 2021 - Present</div>
</div>
<div class="space-between italic-text">
<div>DataJoint - Neuroscience/ScienceOperation</div>
<div>Houston, TX</div>
</div>
<div class="content-padding-left">
<div>
<b class="italic-text">* CICD: </b>Developing generic <b>Github Action</b> reusable workflows for <b>30+</b> repositories to automate Python packages and Docker images build, test, release/publish
</div>
<div>
<b class="italic-text">* SciOps Kubernetes Cluster: </b>Setting up and maintaining <b>Kubernetes</b> clusters for dev/test/production using <b>kOps and k3d</b>; configuring Nginx ingress controller, let's encrypt cert manager, Cillium Container Network Interface(CNI); deploying JupyterHub, flask, ReactJS, etc. using <b>helm</b> or <b>kubectl</b>
</div>
<div>
<b class="italic-text">* SciOps Worker Clusters: </b>Configuring <b>Terraform</b>, <b>cloud-init</b> and <b>SaltStack</b> to provision CPU and GPU(<b>Nvidia/CUDA</b>) workers in different scales as needed
</div>
<div>
<b class="italic-text">* SciOps Operation Automation: </b>Automating SciOps operations such as onboarding new customers, managing customers' permission, generating usage and billing report, etc. using <b>Python</b>, <b>SQL</b> and <b>bash</b>
</div>
</div>
<br>
<div class="space-between bold-text">
<div>Software Engineer(Data Science)</div>
<div>May 2019 - July 2021</div>
</div>
<div class="space-between italic-text">
<div>dataVediK- Oil & Gas</div>
<div>Houston, TX</div>
</div>
<div class="content-padding-left">
<div>
<b class="italic-text">* Interactive Drilling Dashboard: </b>This is an <b>enterprise</b> product that I worked with two more engineers. Developed a <b>Plotly Dash</b> dashboard that visualizes processed data using Bootstrap, CSS media query, <b>Redis</b> and sqlalchemy. Also, implemented a <b>socket</b> service will notify when <b>Airflow</b> pipeline finished processing in order to <b>synchronize</b>(refresh) the dashboard's data.
</div>
<div>
<b class="italic-text">* CI/CD Pipeline: </b>Set up several <b>Azure Pipelines</b> for continuous development, testing and continuous deployment in <b>dev, test and prod</b> stages. Additionally, made a <b>Jenkins</b> pipeline to work with on-premise infrastructures.
</div>
<div>
<b class="italic-text">* ML Pipeline: </b>Set up a <b>MLflow</b> server for machine learning experiment logging, parameter tuning, continuous training, model management and model serving.
</div>
<div>
<b class="italic-text">* ETL Pipeline: </b>Working with a data engineer, set up an <b>Airflow</b> server for our data ETL pipeline.
</div>
<div>
<b class="italic-text">* Prediction Task Manager: </b>Working with a front-end developer, designed and developed a <b>production</b> web application that supports job queuing and parallel processing for drilling speed prediction using JavaScript, <b>flask</b>, sqlalchemy, <b>celery</b>, RabbitMQ, gunicorn, Nginx, supervisord, Docker and AWS EC2, AWS Cognito Authentication, HTTPS
</div>
<div>
<b class="italic-text">* Drilling Status Detection: </b>Working with a domain expert, developed two <b>classification</b> models for detecting drilling status using Logistic Regression and Random Forest with the convenience of the MLflow server
</div>
<div>
<b class="italic-text">* Drilling Speed Prediction: </b>Working with a domain expert, applied Gaussian Process <b>Regression</b> for feature synthesis based on geographical information as well as <b>feature engineering</b> based on correlation matrix and F1 score ranking, built a non-linear regression model using LSTM RNN.
</div>
</div>
</div>
<hr>
<div>
<h5>Education</h5>
<div class="space-between bold-text">
<div>Southern Methodist University</div>
<div>Aug 2017 - May 2019</div>
</div>
<div class="space-between italic-text">
<div>Master's of Computer Science</div>
<div>Dallas, TX</div>
</div>
<div class="space-between bold-text">
<div>Qingdao University</div>
<div>Aug 2013 - May 2017</div>
</div>
<div class="space-between italic-text">
<div>Bachelor's of Software Engineering</div>
<div>Qingdao, China</div>
</div>
</div>
</div>