Applying to Space @ VT's GSoC 2018 Program
Interested students that want to apply to Space @ Virginia Tech's GSoC 2018 program need to complete the following:
- A simple coding task related to space science and engineering, for examples please see our proposal guide.
- Fill out this Google form in order to tell us a little about yourself
This will help our mentors in working with you to develop proposals that are to be submitted via the Google Summer of Code website starting March 12th, 2018.
Once the two items above are submitted it may take mentors a week or more to review your material, in this time please begin working on a proposal as noted below.
As you are preparing your proposal, please use our guide for our proposal format as well as info on completing a simple coding task related to space science and engineering.
Below is a list of ideas that students can propose to and/or gives a sense of the types of projects we're looking at for the Summer of Code program for 2018. You may contact the mentors listed on each idea if there is something that isn't completely clear or if you need a little more information to see if that idea interest you.
- Web based visualization tools to access space science datasets
- Description : The Virginia Tech SuperDARN website hosts several data visualization tools that are used by the Space Science community. Currently, most of the existing visualization tools use IDL as the core backend language (http://www.harrisgeospatial.com/SoftwareTechnology/IDL.aspx) and generate static plots. The main goal of the project is to use our open source data analysis toolkit DaViTPy and the latest data visualization software such as Python (matplotlib, seaborn, bokeh) and d3.js to develop interactive and dynamic visualization tools.
- Expected Outcome : Develop data visualization tools for space science datasets that will be hosted on the Virginia Tech SuperDARN website.
- Possible Mentors : Bharat Kunduri (email@example.com), Kevin Sterne (firstname.lastname@example.org), Mike Ruohoniemi (email@example.com), Jo Baker (firstname.lastname@example.org)
- Difficulty Level : Medium
- Open source tools for atmospheric modeling
- Description: MSIS is an empirical model of the Earth’s atmosphere generated using data from the Mass Spectrometer and Incoherent Scatter radar, that is often used as the benchmark against which results from physics based models are compared. Despite its wide use in the atmospheric science community, it is not easily accessible in its current implementation (Fortran/C++/IDL). Re-writing this model and associated post-processing tools in Python would would serve a two fold purpose - for the selected student, it will be an excellent avenue to practice intermediate programming skills and concurrently learn about the application of empirical modeling to atmospheric and space science; for the atmospheric science community, it will make the MSIS model much easier to access and use.
- Expected Outcome: Develop a well documented Python implementation of MSIS, along with data visualization tools that can be distributed as within the atmospheric science community.
- Skills required/preferred: Intermediate programming skills, preferably in Python. An interest in atmospheric and space science.
- Possible Mentors: Karthik Venkataramani (email@example.com), Scott Bailey (firstname.lastname@example.org)
- Difficulty level: Easy/Medium
- ICON spacecraft conjunction tool
- Description: The ICON spacecraft, scheduled for launch in spring 2018, will make observations of the Earth’s upper atmosphere & boundary to space. Many of these observations are complementary to ground-based observations, but take advantage of the unique geometry & opportunities afforded an observatory in space. The ICON spacecraft performs many maneuvers & rotations to view different targets on the horizon of the Earth. To capitalize on its observations, and permit the community of ground-based observers to use their data in a coordinated manner, we need to be able to identify overlapping observations. The ICON mission makes public their mission plan, which includes where the spacecraft is & which way it is pointed, but to make use of this, we need to combine this information with existing codes that show where those views of the horizon are, and identify the % of overlap and timing of overlap with ground-based observatories. The student would learn about the available data, existing geometry codes, and combine these to produce a freely available tool that takes input information about a ground-based observatory and identifies a time-sequence of overlapping ICON datasets.
- Expected Outcome: Develop a tool for the space science community that will be hosted on the Space@VT website.
- Skills Required/Preferred: Basic knowledge of Python, Matlab. Experience in working with vector geometry, applicable to spheres, ellipses etc. is preferred, but not required.
- Possible Mentors: Scott England (email@example.com)
- Difficulty Level: Medium
- Develop open source software in python to download, analyze and visualize space science datasets
- Description : Several research groups from different institutions led by the Virginia Tech SuperDARN group developed a open source python based data analysis toolkit : DaViTPy (https://github.com/vtsuperdarn/davitpy) which is used by the space science community. However, a majority of the efforts in DaViTPy development are limited to processing data from SuperDARN radars. The main goal of the project is to expand the scope of DaViTPy to analyze and visualize data from different satellites.
- Expected Outcome : Contribute to DaViTPy and develop python based data analysis tools for analyzing data from satellites.
- Skills Required/Preferred: Basic knowledge of Python. Experience working with matplotlib and pandas is preferred but not required.
- Possible Mentors: Bharat Kunduri (firstname.lastname@example.org), Kevin Sterne (email@example.com), Evan Thomas (firstname.lastname@example.org)
- Difficulty Level: Easy
- Develop big data tools for advanced querying capabilities of space science datasets
- Description: An important part of the analysis carried by researchers and students working in Space@VT is querying several datasets and retrieving data that satisfy certain criteria. However, the data is stored as binary files with no indexing, making it extremely slow and arduous to search through and filter the data. The main goal of the current project will be to experiment with new data storage and indexing tools such as Apache Parquet and Elasticsearch and develop a framework that enhances the querying capabilities, especially for geo-spatial and time series data.
- Expected Outcome: Identify appropriate big-data frameworks and use them to develop tools that enable faster queries over large geo-spatial and time series data with optimal use of disk space.
- Skills Required/Preferred: Experience working with big data tools is preferred.
- Possible Mentors: Bharat Kunduri (email@example.com), Kevin Sterne (firstname.lastname@example.org), Mike Ruohoniemi (email@example.com), Jo Baker (firstname.lastname@example.org), Evan Thomas (email@example.com)
- Difficulty Level: Difficult
- Real time monitoring of space weather
- Description: Space weather directly influences several technologies we are dependent on, such as GPS, Satellite communications and aviation. It is therefore necessary to monitor and asses space weather in real time. Currently, there are several (SuperDARN) radars making continuous measurements of space weather. However, it is not straightforward to visualizing the collective data from all these radars in real time. The goal of this project will be to develop a tool to collect, process and visualize data from several SuperDARN radars in real time. Such a tool would be immensely useful not only to the space science community but also to a wider group of researchers who are impacted by space weather.
- Expected Outcome: Develop a tool to monitor data from SuperDARN radars in real time.
- Possible Mentors: Kevin Sterne (firstname.lastname@example.org), Bharat Kunduri (email@example.com), Keith Kotyk (firstname.lastname@example.org), Mike Ruohoniemi (email@example.com), Jo Baker (firstname.lastname@example.org)
- Difficulty Level: Medium
- Parallelizing computations of SuperDARN processing routines
- Description: There are many ways in which code can run more efficiently and quickly, one of which may be parallelizing functions where results of one part of code are not directly necessary to continue running following code. Many of the core SuperDARN data processing routines were written prior to multi-core computing was widespread. This project idea has a student analyzing data processing code (mostly written in C) and parallelizing where possible in order to increase the overall speed of data processing. Other multi-core or multi-threading techniques can be considered in order to improve the efficiency of the data processing code.
- Expected Outcome: Expect an overhaul of current data processing routines in order to increase the speed of producing data products for the SuperDARN and space science community. Code will be added to SuperDARN’s RST github repo.
- Skills Required/Preferred: C, parallel processing
- Possible Mentors: Kevin Sterne (email@example.com), Bharat Kunduri (firstname.lastname@example.org), Mike Ruohoniemi (email@example.com), Pasha Ponomarenko (firstname.lastname@example.org), Keith Kotyk (email@example.com), Evan Thomas (firstname.lastname@example.org), Simon Shepherd (email@example.com)
- Difficulty Level: Difficult
- Applying machine learning algorithms to Space Science data
- Description: As with the growth of space science data, challenges arise as how to efficiently discover patterns and extract information from large datasets with a high accuracy. We are going to use machine learning algorithms to automatically identify patterns in space science data that will make scientific research easier. One example of this application is categorizing different types of backscatter from SuperDARN radar observations using unsupervised learning algorithms.
- Expected Outcome: Develop python based tools for applying machine learning algorithms to space science data.
- Skills Required/Preferred: Machine learning algorithm, Python, experience working with space science data is preferred.
- Possible Mentors: Xueling Shi (firstname.lastname@example.org), Mike Ruohoniemi (email@example.com), Jo Baker (firstname.lastname@example.org)
- Difficulty Level: Medium/Difficult