Junzi Sun



Photo

Lives in:
Delft, Netherlands

Email:

Speaks:
Chinese, English, and some Spanish

Link:
github.com/junzis

Blog:
medium.com/@junzi

About me

I am passionate about aerospace and computer science. I like challenges and enjoy meeting people from different backgrounds. I was born in China and completed my bachelor's degree there as well. Since then I have studied and worked in six different countries. Thought I have held various positions, I am first and foremost an engineer. Currently, I am completing my PhD research at TuDelft, in the Netherlands.

#Computing, #Aircraft, #Coding, #Python, #Java, #PHP, #Cloud, #DataMining, #WebApp


Research and Education

2015 - Present: PhD at TuDelft. Research topic: "Developing Aircraft Performance Models Using Data Mining"

2007-2010: MSc of Aerospace Science and Technology, Technical University of Catalonia (UPC) Master Thesis

2003-2007: BSc of Electronic Information Technology, Beijing University of Post and Telecommunication

2009 Jun-Aug: Space Studies Program, International Space University


Publications

Sun, J., Ellerbroek, J., & Hoekstra, J. (2017). Flight Extraction and Phase Identification for Large Automatic Dependent Surveillance-Broadcast Datasets . Journal of Aerospace Information Systems.

Sun, J., Ellerbroek, J., & Hoekstra, J. (2017). Modeling Aircraft Performance Parameters with Open ADS-B Data. In Proceedings of the 12th USA/Europe Air Traffic Management Research and Development Seminar. FAA/EUROCONTROL.

Sun, J., Ellerbroek, J., & Hoekstra, J. (2017). Bayesian Inference of Aircraft Initial Mass . In Proceedings of the 12th USA/Europe Air Traffic Management Research and Development Seminar. FAA/EUROCONTROL.

Verbraak, T. L., Ellerbroek, J., Sun, J., & Hoekstra, J. M. Large-Scale ADS-B Data and Signal Quality Analysis In Proceedings of the 12th USA/Europe Air Traffic Management Research and Development Seminar. FAA/EUROCONTROL.

Sun, J., Ellerbroek, J., & Hoekstra, J. (2016). Large-Scale Flight Phase Identification from ADS-B Data Using Machine Learning Methods. In 7th International Conference on Research in Air Transportation.

Sun, J., Ellerbroek, J., & Hoekstra, J. (2016). Modeling and Inferring Aircraft Takeoff Mass from Runway ADS-B Data. In 7th International Conference on Research in Air Transportation.

Sun, J., & Xhafa, F. (2011, June). A genetic algorithm for ground station scheduling. In Complex, Intelligent and Software Intensive Systems (CISIS), 2011 International Conference on (pp. 138-145). IEEE.


Work Experiences

2015 - Present: PhD, TuDelft, Netherlands

2012-2015: Academic Coordination of SSP, International Space University, France

2011-2012: Researcher in Positioning Technology, Ascamm Technology Centre, Spain

2007-2011: Aerospace Engineer / IT Manager, Barcelona Aerospace Technology Centre, Spain


Some Fun Projects

Open Aircraft Performance Database

As part of Open Aircraft Performance model, the OFE database provides operational and limitation values of aircraft performance parameters. Common aircraft types are included. ADS-B data are use for model construction. Each parameters are constructed based on at least 5000 flight of same aircraft type.
GitHub

pyModeS

The ADS-B decoder has evolved into a fully powered Mode-S decoder with contributions from the community. It decodes ADS-B messages (DF17, DF18) and Enhanced Mode-S (DF20, DF21) messages. Many more aircraft states can now be discovered through Mode-S, in addition to ADS-B.
GitHub and PIP

Aircraft Database

A database to search for aircraft IDs (such as ICAO address, registrations ID, etc) and related information. It is built in Python / Flask and data is from Flight Radar 24.
Aircraft Database and GitHub

ADS-B / EHS Decoding Guide

A guide to decode the ADS-B messages. Part of my PhD research involves using ADS-B data to assist aircraft performance modeling. This is the first project that I worked here at TuDelft.
ReadTheDoc

BlueSky

BlueSky is the open source Air Traffic Simulation we are building in my group. I am only a part of the coding team, contributing to the development of the tool.
GitHub

Memeit

Creating an internet MEME is easy, but it is difficult for a computer to understand the accompanying text. We developed a machine learning system that can extract and understand the text within an internet MEME. The accuracy of the text recognition exceeds 95%.
GitHub

Involve

A web-based platform that standardizes the proposal processing for ISU, Space Studies Program.
GitHub and Web


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