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🛰️ Welcome to Sats 🛰️

Hey! Welcome to the Stanford Satellite Team! Whether you're an incoming frosh, alumni, PhD, or even not a part of the Stanford community, we're glad you're here. Our current mission Project Sequoia is a 3U (about the size of a long shoe box) fully in-house imaging satellite that will demonstrate CubeSats as a user-defined service with changeable machine learning models. Current Satellite Team Leads:

SlackLogo.png@Spencer Wallace  and SlackLogo.png@Ashley Raigosa  and SlackLogo.png@Theo Makler 

Stanford Affiliated ☀️

If you're a Stanford student, professor, or affiliate we'd love for you to join the SSI Sats community! Once you've joined our slack messaging hub, join the satellites channel and don't be shy.

Outside Stanford ❄️

If you're not affiliated with Stanford but are interested in the team and our project, please email the team leads Akasha Hayden and Ian Chang (,

Sub-Teams and Schedules


  • Avionics (Electrical Engineering) — Learn electrical engineering in space, make your own camera, radio, solar panels, and design your own custom circuit boards (we'll even teach you how to make rave lighting for your room).
  • Satellite Systems Engineering — Talk with top space representatives in both government and industry, attend local satellite and space conferences, and learn how to launch a satellite safely and legally 😉. Also, check out the Policy team.


SSI Updated General Event Calendar
Winter 2020 Meeting Schedule
Sub-Team Time (PT)
General Meeting Sunday 10AM
Systems Tuesday 6PM
GNC Wednesday 6PM
Avionics Thursday 6PM
Software Tuesday 5PM
Structures Thursday 8PM
Find all zoom meeting links via internal slack pages

Sequoia 2019-Present 🌲

Sequoia is an open-source, 3U CubeSat that will demonstrate on-board image classification and processing with updateable machine learning models. The goal of the project is to obtain a high volume of scientifically important imagery for ecological and climatology research. Researchers many times have no need of images saturated with clouds or uninteresting areas—so why not filter them out with convolutional neural networks? We will retrain Sequoia’s deep learning with images taken by the satellite, uplinking improvements. The Stanford Student Space Initiative is developing deep learning models for forest fire risk assessment and detection and a number of other applications. The mission architecture is user definable with the operator specifying desirable image locations or types and resolutions, and the satellite maximizing delivery of fully open-source images.

Here's our in-progress github (not in any finalized state) for the project. A full public project rundown of all systems, code, and designs will be released before we launch.

Past Projects

The principal work of Stanford Satellite Team has been the development of various Optical Communications technologies. POINTR, a 1U segment of a 3U cubesat launched in 2018 but never conected with ground control due to improper oribit insertion from the launch provider.

In addition, members have worked in Stanford faculty labs to build:

  • SNAPS, the Stanford NAno Picture Satellite, a 1/4U imaging cubesat deployed from the ISS in 2016
  • QB50 Discovery, Stanford's submission to an international 50-member cubesat constellation
  • Morgana, a CubeSat designed to study high energy particles in the upper atmosphere. (Cancelled)