Frequent answers to questions

Hello people! This page contains some answers to questions that I am frequently asked.

Getting to Columbia

• Q: How do I get to your office?
  • My office is in the “Data Science Institute” Suite on the fourth floor of the Mudd building. My office number is 426.
  • Here are directions to Columbia Morningside Heights campus.
  • Here are directions to Mudd from the subway stop at 116th St. and Broadway.
• Q: How do I get to x at Columbia?
  • Here is a map of Columbia Morningside Heights campus.

Coming to Columbia as a student, postdoc, visitor, researcher, etc.

• Q: I am interested in applying to a computer science or data science degree program at Columbia. Should I do so and what are my chances of getting in?
  • Please see the CS department’s admissions webpage and/or the DSI’s admissions webpage. There, you’ll find information about all aspects of the application process (e.g., deadlines, your chances of being admitted), as well as information about the academic programs themselves.
  • Some frequently asked questions, already answered for your convenience, can be found by clicking the following links:
    • M.S. Program Application FAQ
    • Ph.D. Application FAQ
  • Feel free to mention my name in your application if you are interested in my research. This is a good way to draw my (and others’) attention to your application.
  • I get a lot of emails about admissions, and unfortunately, I cannot write a personal reply to each one.
  • I don’t currently have plans to admit new Ph.D. students to start in Fall 2021. If the plan changes, this page will be updated to reflect it.
• Q: Do you have any internship positions available?
  • I do not have internship positions available (virtual or in-person).
• Q: Do you have any open postdoc positions?
  • I do not have any open postdoc positions.
• Q: Can I do a research visit with you for n months? My company/government/self will cover all of my expenses; I will not require a stipend.
  • Due to time constraints, I cannot commit to hosting external students or visitors whom I’ve never met or worked with.

Getting started in machine learning

• Q: I am interested in taking a course in and/or doing research in machine learning. What courses should I take in preparation?
  • First, it’s great that you are interested in these subjects! Machine learning is a confluence of ideas from many disciplines, including computer science, optimization, physics, and statistics. Because of this, it is a very broad subject that builds on a number of foundations. When getting started in machine learning, it may feel overwhelming—it did for me. At the same time, I hope it also means that there’ll be something particular that’ll “click” with you and that you’ll want to study in depth.
  • A good way to get started is to build up solid mathematical foundations in
    • linear algebra,
    • probability, and
    • multivariable calculus.
  • Comfort in writing code to process and analyze data in Python is also very helpful.
  • The syllabus for COMS 4771 “Machine Learning” has some resources on these prerequisite subjects.
• Q: Same as above, but particularly in learning theory…
  • Learning theory is the mathematical study of models and algorithms for learning. There are several courses related to learning theory at Columbia, including COMS 4252 “Introduction to Computational Learning Theory”, COMS 4773 “Machine Learning Theory”, and IEOR 8100 “Reinforcement Learning”.
  • These courses are “proof-based”, in the sense that the course is largely driven by formal definitions of and mathematical theorems about learning problems and algorithms. Their proofs and the intuitions behind them help solidify our understanding of the phenomenon that we call learning.
• Q: What are good books to read about machine learning and/or learning theory?
  • For general machine learning: A Course in Machine Learning by Hal Daumé III.
  • For learning theory: An Introduction to Computational Learning Theory by Kearns and Vazirani; A Probabilistic Theory of Pattern Recognition by Devroye, Györfi, and Lugosi.

Advice

(These are not frequent answers to questions, but rather just links to useful “advice” that others have written.)

• Mihir Bellare’s views on the Ph.D. experience.
• Fan Chung’s advice for dealing with a math bully and general advice on research for graduate students.
• Jeannette Wing’s tips on the interview process (for academic jobs).
• Charles Isbell’s thoughts on getting admitted to a Ph.D. program.