Columbia Computer Science Instructional Assistant Page

Thank you for your interest in an Instructional Assistant position. Please refer to FAQ for answers to frequently asked questions.


Announcements

Last updated: 01/03/2018


TA/CA Application for Spring 2018

IA applications are open and assignments are underway. You are notified only when you are assigned.



Courses Seeking TA/CAs

Last updated: 01/25/2018

If interested and qualified, please apply on MICE. Contact the instructor for any questions.


TA: Programming for Entrepreneurs

The applicant should have experience in web application development. Knowledge of Javascript, CSS, databases is required. Familiarity with setting up Amazon AWS EC2 servers for the application deployment is a plus. Knowledge of frameworks such as Ruby on Rails, Ember, Django or Zend is a plus. The course runs for a very short period of time (2 weeks), so you do not have to commit for the whole semester. Please apply directly by emailing Sameer Maskey at smaskey@cs.columbia.edu.


TA/CA: MOOC CVN Course - Artificial Intelligence MicroMasters Series on edX

This TA/CA position requires assisting Columbia Video Network (CVN) in the preparation, ongoing delivery and student support activities for the Artificial Intelligence MicroMasters series on edX. Please refer to this doc for more details.

This position is not part of the CS department IA assignment process, and is, instead, handled through CVN. Please contact CVN if you are interested.


TA/CA: COMS 4995: Deep Learning for Computer Vision

Qualifications: TAs needed with experience in Machine Learning, Deep Learning, Python, and cloud computing

Course Description

Instructors: Peter Belhumeur


TA/CA: COMS 4121: Computer Systems for Data Science

Qualifications: Successful completion of W4121 or equivalent course; or experience with Hadoop, and Spark, AWS/Google cloud, database-backed web applications.

Course Description

Instructors: Roxana Geambasu, Sambit Sahu, Eugene Wu


TA/CA: COMS 4203: Graph Theory

Qualifications: Preferably a candidate who has taken a prior class on graph theory, but someone who has taken a few graduate level theory classes and did well (especially in Analysis of Algorithms I) would be suitable.

Course Description

Instructors: Timothy Sun


TA/CA: COMS 6998: Quantum Computing Theory & Practice

Qualifications:

Course Description

Instructors: Lior Horesh, John A Smolin


TA/CA: COMS 6998: Advanced Machine Learning Personalization

Qualifications: Should have at least one previous Machine Learning course with a grade of A or A+.

Course Description

Instructors: Tony Jebara


TA/CA: COMS 6998: Advanced Distributed Systems

Qualifications:

Course Description

Instructors: Roxana Geambasu


TA/CA: COMS 6731: Humanoid Robots

Qualifications: Prospective IA's will need to have taken a robotics course and also have knowledge of the ROS programming system.

Course Description

Instructors: Peter K Allen


TA/CA: COMS 4112: Database System Implementation

Qualifications: Successful completion of 4112 or an equivalent course taught elsewhere. Will also consider students who have completed 4111 with a good grade and have programming experience in C.

Course Description

Instructors: Kenneth A Ross


TA/CA: COMS 4119: Computer Networks

Qualifications: Students who have taken CSEE 4119 (or a similar class elsewhere) or Operating Systems (e.g., COMS 4118) and received a good grade. Fluency in Python or C/C++.

Course Description

Instructors: Henning G Schulzrinne


Grader: Department of Statistics

Graders receives a salary of $1,800 and a tuition fellowship of $1,500. MA students who have taken classes that demonstrate good knowledge of Machine Learning and R program are encouraged to apply. An ideal person should have taken a class comparable to ECBM E4040 – Neural Networks and Deep Learning. Students who wish to be considered for a Grader position should bring an official Columbia University transcript and a brief cover letter to Dood Kalicharan in the Statistics Department office (Room 1003, School of Social Work, 1255 Amsterdam Ave). Awards will be made on the basis of academic performance in general and past performance in courses where there is a need for Graders. Applications must be received by Friday, January 26, 2018.


[FILLED] TA/CA: CSEE 4140: Networking Laboratory

Qualifications: Students that took CSEE 4140 (or a similar class elsewhere) and received a good grade.

Course Description

Instructors: Gil Zussman


[FILLED] TA/CA: COMS 6998: Computational Models of Social Meaning

Qualifications: NLP course with grade at least A (and preferably also ML course taken, with A grade

Course Description

Instructors: Smaranda Muresan


[FILLED] TA/CA: COMS 6998: Introduction to Brain Computer Interaction

Qualifications:

Course Description:

Brain computer interaction (BCI) has recently proven to be a powerful technique to enable humans to directly communicate with computers and other external devices. Through BCI-based neuro-feedback, subjects can be trained to self-regulate/control their own brain activity in targeted brain regions. The therapeutic use of BCI-based neurofeedback has also been positively investigated in clinical application. This course will discuss BCI-related basic technologies, including acquisition of brain data, with a focus on fMRI, data preprocessing, encoding, and decoding using machine learning methods. The basics of neuroscience, task-design, ethical considerations of interaction with the brain, and clinical applications will also be covered. Students will learn the pros and cons of traditional brain data processing and machine learning methods in BCI, and learn how to use and improve these methods on real brain data. The aim of this course is to prepare students with basic knowledge and skills to explore opportunities in the emerging field of BCI.


[FILLED] TA/CA: COMS 4180: Network Security

Qualifications:

Lectures are Fridays 10:10am to 12:40pm. It is preferred that the TA be able to attend most lectures if he/she has not taken 4180. There will be a couple classes where the TA should attend to assist students needing help with a group project.

Course Description

Instructors: Debra L Cook



Eligibility

The Computer Science Department requires that you:

  1. Are a currently registered student at Columbia University
  2. Are in good academic and conduct standing with the University (For more information, refer to Columbia Engineering Policy on Conduct and Discipline and Columbia University Policies and Regulations)

Please note that by submitting your IA application, you grant permission to the Computer Science Department to inquire about your disciplinary and conduct history and also grant permission to the Graduate Student Affairs Office/the Office of Judicial Affairs to release relevant information.

Application Process

You apply for an Instructional Assistant position using the Computer Science Department’s MICE system. If you do not have a MICE account, please email advising@cs.columbia.edu. Please go to the Instructional Assistant menu and select “Instructional Assistant Application” to start the application process.

If you are selected to be an Instructional Assistant, you will receive an email from the MICE system. Please note that while most of the Instructional Assistant assignments are completed by the first week of classes, the selection process may continue until 2 to 3 weeks into the semester.

Anyone can apply, but all else being equal, we will give priority to students (grads and undergrads from CC, SEAS, GS, Barnard) who are majoring in CS or CE, over students from other departments.

Appointment Process

You will be notified by email when you are selected for an Instructional Assistant position. Once you accept the appointment, below are the forms you will have to fill out, depending of your hire/rehire status:

  1. CA/CA FELLOW/TAIII NEW HIRE
    1. If you are a new hire and never worked at the university before, you must complete an I-9 regardless of citizenship or visa status before the start of classes or within 3 days within 3 business days or prior to the beginning of the semester. The official start date to provide the I-9 Processing Center is the date you complete your I-9 form or the beginning of the month (Sept 1st or January 1st). You cannot start working without completing the I-9 process.
    2. Below please find the full employment packet - please read the instructions carefully and come by the CS admin office asap if you do not have a Social Security Number
    3. Employment Packets: (PRINT SINGLE SIDED ONLY)
      1. CA
      2. CA Fellow
      3. TAIII
      4. Payroll Casual Calendar - Spring 2018 (for CA and CA Fellows ONLY)
  2. CA/CA FELLOW/TAIII REHIRE
    1. If you have previously worked in the Computer Science Department as a CA or CA Fellow please click here for reactivation forms
    2. If you were previously a TA III no additional paperwork is necessary unless your I-20 has expired. If your I-20 has expired, take your updated I-20, I-94, visa and passport to the I-9 Processing Center to update your I-9. Bring the updated I-9, I-20, I-94, visa and passport to Chantal Kadhi-Smith (ck2373@columbia.edu) so your personnel record can be updated. You cannot be placed on payroll without completing this process.

    Failure to complete the I-9 process within 3 days of accepting the position in MICE will result in a delay of the position and pay. Submission of employment paperwork is required before you begin your work.

    PAPERWORK PROCESS : Chantal Kadhi-Smith will be assisting with collecting and guiding you through the hiring process. Below are the steps:

    1. Access and fill out the appropriate employment package - (PRINT SINGLE SIDED ONLY)
    2. Chantal will hold walk-in hours in room 457 from 10am-3pm beginning on January 10th until January 31st, then by appointment.
    3. When you meet with Chantal, make sure you have your I-9 and your SSN receipt - if needed and also bring all your documentation (Passport, Visa, I-94, I-20 and all other employment forms)
    4. For additional questions concerning the hiring documents or process please email Chantal at ck2373@columbia.edu
  3. GRA's who also TA

    Students completing PhD teaching requirements do not have to complete any paperwork but are required to accept the position in MICE. All currently funded GRAs who will be paid for TA duties as add-comp must accept the position in MICE and complete the Add Comp Authorization form click here. Upon completion you must forward the form, via email attachment, it to your faculty advisor for electronic approval. The completed form with your faculty's approval must then be emailed to the Department Chair for approval. Please copy Maria Joanta (maria.joanta@columbia.edu) on all Add Comp approval requests emails sent to faculty.

Responsibilities

There are three different types of Instructional Assistant positions: Teaching Assistant, TAIII, and Course Assistant.

Teaching Assistants are mostly doctoral students. For a few first- and second-year undergraduate courses with large enrollments, high-achieving undergraduate students who have performed well in these courses may serve as Teaching Assistants (TAIII) for several semesters. Primary responsibilities include:

Course Assistants are high-achieving MS and advanced UG students who have performed well in the course or a course with similar content. Primary responsibilities are:

Course Assistants do not hold recitation sections or to give lectures. Specific duties may vary based on class and instructor requirements. Course Assistants are assigned as CAs for one or at most two semesters.

Code of Conduct

As an Instructional Assistant, you play a valuable and integral role in shaping the ethical direction of our students. You are ambassadors and role models. As such, the following four principles must be adhered to: respect, trustworthiness, fairness, and responsibility. An Instructional Assistant treats others with respect: An Instructional Assistant acts in a trustworthy manner: An Instructional Assistant treats students fairly: An Instructional Assistant acts in a responsible manner:

Questions?

If you have any questions please contact me at iachair@cs.columbia.edu and the IA coordinators at iacoord@cs.columbia.edu