For prospective students and postdocs

We always look for graduate (or internship) students and postdoc collaborators with a strong interest in the area of machine/deep learning.

If you are interested in joining our lab, send an email to me ( with your transcript or CV. I can advise students in Kim Jaechul Graduate School of AI and the School of Electrical Engineering at KAIST: you can apply to a graduate program of one of them.

Some tips for KAIST undergraduate students

  • Decide first what kinds of fields you love to investigate, instead of how to optimize your career on some “hot” fields. For example, read “Should I do a PhD?”.

  • Take as many mathematics courses (e.g., MAS212, MAS241, MAS250, MAS311, MAS477, CS206, CS300, EE205, EE213, EE326) as possible.

  • Improve your programming skills (e.g., C, Python, not necessarily though machine/deep learning platforms) through courses (e.g., EE209, EE324, EE415) or industrial internships (e.g., apply EE Co-op program).

  • Do not consider too much why the above courses are useful for machine/deep learning. Take any courses if they look interesting to you.

  • Apply (by sending an email to me) an individual study in our lab, typically at your 3rd (or early 4th) year (since we typically have many such candidates).

  • Do not worry about your technical background/experience: your passion, attitude and intellectual curiosity matter (more than your talent) for understanding fundamentals of the area.

  • Nevertheless, if you are a beginner for machine/deep learning, I recommend to take some online courses (e.g., machine learning).

Mathematics is important for machine/deep learning?

This video about “machine learning is just mathematics!” is quite useful for undergraduate students who are interested in machine learning (ML) or deep learning (DL).

Learning good mathematical skills is important, but sometimes not an optimal choice for graduate students, e.g., who want to submit papers in the next month or get a job in the next year. This is because there can be a bunch of other non-mathematical options to improve your ML/DL papers or jobs more easily. Consequently, learning mathematics becomes more costy and less efficient, and your results often do more fragile without provable guarantees. At least, machine/deep learners with strong mathematical skills (a) are not easily replaced by other people and (b) can survive more easily even after ML/DL dies or their trends change.

On the other hand, if you are already a PhD student, completely ignore the above: emphasizing mathematical aspects too much beyond necessity (depending on your topics) in PhD life can be often intellectual vanity and waste of time (unless you are in charge of it). You should spend most time on something specific and directly related to your theses, irrespectively whether mathematical or not. Mathematics is useful for ML/DL, but definitely not all.