First assignment
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Neuro Data Design Course

Taught by: Joshua Vogelstein (@jovo, Prof) and Greg Kiar (@gkiar, TA)
Taught in: Fall 2016 and Spring 2017
Time: MW 17h00-18h60
Location: Clark Hall 2nd Floor Design Studio (East)

This course will provide 3 design-credits per semester, and will be a team-based year-long undertaking in data science and neuroscience. Students will be required to have some background in programming, statistics, and linear algebra, and should be willing to learn more. Each week, the course will take 3 hours. In the first 1.5 hours, students will meet with the professor and TA for a meeting in which each group will present their work of the previous week and establish goals for the next week. In the second, the TA will be leading an activity or presenting a demo which will go into detail on a specific task or tool that the students will be able to make use of in their projects. The goal of this year of work is to produce a tool that lowers the barrier to entry for or solves some previously unsolved computational neuroscience task. The software produced will be open-source under the Apache 2.0 licence, and must be reproducible (i.e. another can run it and recreate the findings documented/reported).


This class communicates largely in Slack. Please join our Slack channel at to get involved.

Office Hours

The Wednesday sessions will function much like a lab (or office hours when we have no pressing topic to cover). If you wish to work with the TA, get help on something, etc. outside of these hours, send him a Google Calendar invite (if you don't have his gmail address ask him via Slack) with the following information:

  • Date + Time set
  • Title of event as "Meeting with <yourgroupname>"
  • Description of event enumerating all of the things you would like to cover within the meeting.


Each week students can earn up to 5 points across: deliverables, feedback/planning sessions, lab/demo sessions, feedback, and communication. Additionally, there will be a final project due on December 9th at 11:59pm.


  • Each Week, students will create an issue with their tasks for the week based on discussion with the professor and TA.
  • Students will complete their DoDs by attaching links to deliverables in comments on the issue prior to the next feedback/planning session.
  • Students are expected to practice giving a presentation of their material in their group each week prior to the session. This will be self-reported later on, in the peer evaluation section.
  • If students submit a complete, and ontime set of deliverables prior to the session start and present them within the feedback/planning session, they will get 1 point, otherwise, 0.

Feedback/planning sessions

  • Students are expected to attend all feedback/planning sessions.
  • These sessions will go over the previous week's deliverables, and plan the path forwards.
  • Each group will need to have a git issue documenting their upcoming week's worth of work with clear deliverables prior to leaving the session.
  • The TA will 👌 the issuess, assuming he understands the specific definition of done (DoD) of the issue, within 24 hours.
  • Grading is based on attendance showing up on time gives 1 point, with a 0.01 point deduction per minute late.
  • If a group attends but does not define a clear DoD or create their issue, they will lose the point for the deliverable for that week.

Lab/demo sessions

  • Students are expected to attend all lab/demo sessions.
  • These sessions will be largely TA led, and will go over tools and methods often used by students, and aim to help the students accomplish their deliverables.
  • If there are things which students would like to learn about, they are welcome to submit these as requests to the TA and we will do our best to accommodate.
  • Grading is based on attendance showing up on time gives 1 point, with a 0.01 point deduction per minute late.


  • Students will do peer evaluations through the google form (linked below) prior to each feedback/planning session, evaluating themselves and their groupmates for the concluding week.
  • Students will indicate which of their group members did attend the practice presentation session.
  • The peer evaluation will also take the form of a PMF: each student will assign a floating point number to every group member, where the sum of all members contributions is 1.
  • Individual grades will be weighted at the end of the term based on peer evaluation feedback, and to intervene throughout the semester if necessary.
  • Students will also identify properties of the class which gruntle or disgruntle them, so that the prof and TA may make adjustments.
  • If the peer evaluation is completed prior to the start of the feedback/planning session (same time as the deliverables) the student is given 1 point, otherwise, 0.
  • Link to peer evaluation:


  • We will evaluate the students based on 4 facets of communication each week: asking for help, reporting, engaging with collaborators, and clarity.
  • We understand that all of these may not necessarily occur within a given week, and as such grades will be assigned subjectively based on performance in these areas.
  • Asking for help means reaching out to the teaching assistant and instructor when problems arise, and effectively problem solving.
  • Reporting includes documenting work of the previous week with a link for each promised deliverable in the initial issue.
  • Engaging with collaborators includes reporting results and requesting further information about the data responsibly and concisely.
  • Clarity is a measure of how well you communicate in these areas. Strong communication is important for both seeking help and reporting results. Full sentences, punctuation, providing context (i.e. not starting a question with 'so why doesn't this work?' but rather opening with your task, goal, and approach), etc.

Final Project

At the end of each term, the teams will have an additional 5 points they can earn. One of the foremost goals of these projects is that the tools created are reproducible and accessible. Each group will have package their code and provide instructions for others to run it. If the tool is a web service, this includes both running the server hosting the service, and using it through the web. The team will get points if the other groups (summing to 1 point), the TA (1 point), and the professor (1 point) are successful at reproducing the results documented. The final points will be awarded based upon completion of the remaining items in the project checklist.

ABET Student Outcomes

  • (a1) Apply knowledge of advanced mathematics (calculus, differential equations, linear algebra, statistics) to problems at the interface of engineering, biology and medicine
  • (a5) Mathematically model and simulate biological systems using computers
  • (b1) Formulate hypothesis for experiments, including those on living systems
  • (b4) Display, describe, summarize and interpret experimental results in a lab report
  • (c1) Identify a desired need and define the biomedical engineering problem to be solved
  • (c2) Determine the constraints to the problem and assess the successful likelihood for different approaches
  • (d1) Communicate opinions, viewpoints and expertise with other team members
  • (d2) Understand team goals and assume and fulfill individual responsibilities within the team
  • (e3) Solve problem using experimental, mathematical and/or computational tools
  • (g1) Synthesize, summarize and explain technical content in a written report
  • (g2) Synthesize, summarize and explain technical content in an oral presentation
  • (k1) Gain proficiency in computer simulations and mathematical analysis tools