Spring 2022 PhD Course Info
- The official Spring 2022 Class Roster is now available. You will be able to review this listing of courses offered for Spring 2022 by filtering by Location for “Cornell Tech”. If any additional courses are available, we will announce this to students on an ongoing basis!
- Spring 2022 Pre-Enrollment begins on November 3rd at 8:00am (ET) through November 4th at 11:59pm (ET).
- Regular Enrollment will allow you a second opportunity to enroll in classes and make schedule changes closer to the start of classes. Spring 2022 Regular Enrollment begins on January 18th at 8:00am through 11:59pm on February 8th, 2022.
- Class meeting patterns, times, grading basis, prerequisites, etc. are published on the official Class Roster. Classrooms will be posted closer to the start of the term in January. All classes are to be held in person unless otherwise noted!
- Future Action Needed – A Spring 2022 Attestation Form will be required to submit in order to qualify for Regular Enrollment in January. Please stay tuned as we will announce when this is available for completion!
ACTION REQUIRED: Complete the Spring 2022 Cornell University Enrollment Attestation. You MUST complete the Spring 2022 Cornell University Enrollment Attestation before you are eligible to enroll in courses. Once you complete all necessary steps, the enrollment hold will be removed automatically within approximately one hour. To verify the hold has been removed, please visit Student Center and review your holds in the upper right-hand corner.
- Course Enrollment information such as how to add/drop/swap, cross listing enrollment tips, wait list processing, etc are all indicated on our Course Enrollment Page. Please make sure to review this as well!
- Further updates will be announced and updated on this page as we approach the Spring semester.
PhD Course Listing
Below is the confirmed PhD Courses offered to Cornell Tech PhD Students. There will be more CS and ORIE classes offered, however this is still in the process of finalization. You will be able to enroll in those classes during Regular Enrollment in January, we will announce those new classes closer to the enrollment period as well.
|INFO 6250||PhD Design Research Studio||Wendy Ju||Taught in NYC|
|INFO 6600||Tech for Underserved Communities||Aditya Vashistha||Streamed from Ithaca to NYC|
|INFO 6940||Special Topics: Red Tape||Gili Vidan||Streamed from Ithaca to NYC|
|INFO 6940||Special Topics:
Rural Computing and Rural Infrastructure
|Phoebe J. Sengers||Streamed from Ithaca to NYC|
|CS 6741||Topics in Natural Language Processing and Machine Learning||Alexander Rush||Taught in NYC|
|CS 6785||Deep Probabilistic and Generative Models||Volodymr Kuleshov||Taught in NYC|
|ORIE 6170||Engineering Societal Systems||Nikhil Garg||Taught in NYC|
Tentative Classes in Progress of Being Approved (not on class roster yet/not available for Pre Enrollment)
ORIE Class TBD: Optimization Under Uncertainty: Robust and Online Models
Taught by: Omar El Housni
Taught on: Tuesday/Thursdays 1:00pm-2:15pm
In most sequential decision problems, uncertainty evolves over time and we need to make decisions in the face of uncertainty. This is a fundamental problem arising in almost every business application where real-time decisions are based on the information revealed thus far. The uncertainty in the problem can be modeled in a number of ways (e.g., a probability distribution over some parameters or an uncertainty set for some variables) and the selection of an appropriate framework is purely a choice of the decision-maker. Such a selection depends on various considerations ranging from the availability of historical data to the tractability of the resulting optimization problem and the robustness of resulting solutions. In the first part of the class, we primarily focus on robust optimization which is a widely used paradigm to handle adversarial models of uncertainty. We also contrast robust optimization with various other paradigms such as stochastic optimization and distributionally robust optimization. In the second part of the class, we focus on discrete optimization problems under uncertainty such as two-stage facility location and sequential matching problems. We will discuss these classes of discrete problems under both the paradigm of robust optimization (worst-case scenario analysis) as well as online optimization (competitive ratio analysis).
CS Class TBD: Data Science for Social Change
Taught by: Emma Pierson
Taught on: TENTATIVELY Mondays/Wednesdays 2:45pm-4:00pm
This seminar will discuss 1) how to do academic research which accomplishes social change and 2) how to increase the impact of academic research by writing about findings to a mass audience and to policymakers. Each week, we will spend one lecture discussing academic papers which accomplished social change and one lecture discussing writing for a mass audience. The seminar will feature guest lecturers from academia and journalism, and students will work on a final writing project which communicates research findings to a mass audience.