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Profile

  • Hands-on experience with Big Data related concepts and ecosystems
  • Experienced in RESTful API design, implementation and deployment
  • Excellent fundamental background of data structures and data algorithms
  • Handy working experience with C++, C#, Java and .NET programming for more than 5 years
  • Highly motivated with superior analytical, organizational and communication skills
  • Willingness to learn, seek for new challenge, and able to adapt new environment
  • Ability to work as a team member as well as individual with a minimal supervision

Highlights

  • Big data technologies: MapReduce, Hadoop, Yarn, HDFS, Spark, Spark streaming, Machine learning,  Lambda architecture
  • Databases and Tools: HDFS, MongoDB, Cassandra, HBase, Google BigTable, MySQL
  • Programming languages: C++, C#, .NET, Java, Python, Scala
  • Architecture/Design Pattern: MapReduce (summarization, filtering, join, metapatterns), MVC, MVVM, Singleton, Factory, Façade
  • Web development: HTML, CSS, Javascript, Ajax, JQuery, Node.js, AngularJS
  • Networking: TCP/IP, HTTP, UDP, DHCP, SSH
  • Environment: Windows, Linux
  • IDEs: Visual Studio, Eclipse, Intellij, Webstorm
  • Configuration management tools: Git, TortoiseSVN

Education

Sep 2015Present

Master of Applied Science

Concordia University, Montreal, Canada

Research topics: Streaming data algorithm design for big data analysis

Accomplishment:

    • Designed and Implemented trajectory pattern mining framework by leveraging parallel computing technologies
      • Proposed various data partition methods (Fixed-Grid, K-D tree)
      • Proposed clustering algorithms (point-to-polyline, polyline-to-polyline, DBSCAN) to discover density reachability between trajectories
      • Implemented batched-based and streaming-based trajectory pattern discovery framework using Apache Spark Core and Streaming APIs
      • Utilized Apache Kafka with Spark integration for streaming data ingestion
      • Optimized system performance by minimizing data shuffling and Spark workflow tuning
      • Experimentally evaluated the performance of proposed solution on Microsoft GeoLife dataset 

Technologies used:  MapReduce, HDFS, Kafka, Spark (Streaming), AWS EC2 clusters, AWS S3 bucket.

 

Programming on the Cloud (COEN691P) - Cloud based Image Processing Service

Accomplishment:

    • Designed and implemented RESTful API for interacting with the image processing service
    • Developed an image processing service which connects to Cloudinary API as a backend
    • Connected Facebook API using OAuth for user authentication
    • Connected Dropbox using its Core API for images/metadata storage
    • Designed NoSQL data model and implement with Google datastore
    • Designed and implemented an web UI using HTML, CSS, Javascript
    • Debugged and deployed the application to Google App Engine

Technologies used: REST API, OAuth, Google App Engine, Facebook API, Dropbox API, Google datastore, HTML5, CSS3, JQuery, AJAX, Javascript

Relevant coursesProgramming on the Cloud, Model Driven Software Engineering, Microprocessors and their applications, Foundations of Cryptography, Optical Networking

Sep 2007Sep 2011

Bachelor of Computer Engineering

Concordia University, Montreal, Canada

Relevant Courses: Embedded System and Software Design, Communication Network and Protocol, Software Process, Microprocessor Systems,  Software Testing and Validation, Real Time System Design, Computer Graphics

Work Experience

Sep 2011Present

Software Developer

Presagis - CAE

Design, develop and maintain high fidality correlated 3D terrain generation tools

Achievement highlight:

  • Participated in designing next generation of multi-machine build system by leveraging cloud computing technologies 
  • Implemented image texture classification using machine learning algorithm (k-mean) in supporting IR sensor workflow
  • Contributed in REST API design and implementation of different products
  • Handy working experience in applying geometry computation algorithms to create 2D based map features (e.g. road network)

Technologies used: C++, C#, python, .NET, Geospatial Data Abstract Library (GDAL),  Computational Geometry Algorithm Library (CGAL), OpenCV MLL, Visual Studio 2013, OpenGL

Publications and Presentations

  • Yongyi X., Yan L., Chuanfei X. (2016, December). "Parallel Gathering Discovery over Big Trajectory Data", IEEE International Conference on Big Data, Washington, DC.
  • Yongyi X., Chuanfei X., Yan L. (2016, December). "Implementing Trajectory Data Stream Analysis in Parallel", IEEE International Conference on Big Data, Real-time and Stream Analytics in Big Data workshop

Awards

  • IEEE International Conference on Big Data, student travel award, 2016
  • Bravo Zulu Award - Presagis, 2015
  • Computer Engineering Capstone Team Project 3rd place (out of 10 teams), Concordia University, 2011