Apply

Our lab adheres to the admissions policy of the Graduate School of Data Science (GSDS) at SNU. To join the SKIML lab as a graduate student, a prospective student must follow these steps: 

1. Successfully pass GSDS's 2-stage admission process (resume screening and interview). 

2. Obtain an Application for Advisor (지도교수 신청서) signed by Professor Jay-Yoon Lee. 

Please note that: 

• For M.S. & M.S. + Ph.D. students, you must complete Step 1 before proceeding to Step 2 (Step 1 ⇒ Step 2). 

• For Ph.D. students, you must complete Step 2 before proceeding to Step 1 (Step 2 ⇒ Step 1). 

If you are interested in joining our lab as a graduate student and want to discuss openings and opportunities, please submit 📄📄 this Google Form 📄📄. and send an email to notify Prof. Jay-Yoon Lee.

For further details on the admissions policy, please refer to the official GSDS website 🔗.

⭐️ We are currently receiving applications for summer internship.
Please submit 📄📄 this Google Form 📄📄 by 11:59 PM KST on June 22nd, 2024. ⭐️

After the deadline for summer internship, new interns will be recruited on a rolling basis, i.e., we will check the inbox for the applications regularly and contact candidates whom we're interested in. After resume screening, you may be invited for a technical and/or general interview. 

More details on internship at SKIML Lab

• You will be matched with a research project led by graduate students, or assigned to tasks attached to multiple research projects. (We will consider your research interests for matching.) 

• Although rare, if you have a concrete research idea and the idea is compatible with the lab's research direction, you may be able to conduct your own independent research.

• You will also attend weekly reading groups where lab members present their own research or related papers.

• You will be required to submit a term report summarizing your work each semester.

• We typically do not provide financial compensation to interns, but in special cases, we may do so.

• Please note that we may terminate internship with 2 weeks' notice, if we find that an intern is not the right fit with our lab. 

We look for following characters in future members of SKIML Lab:

• self-motivation, diligence, clear communication

• preferably technical skills (or willingness to learn technical skills)

Notes on prior research experience

• For MS and M.S.+Ph.D. candidates, research experience is not required. It can be viewed favorably. 

• For Ph.D. candidates, prior research experience is preferred.

Here are some aspects that we think are beneficial for successful graduate study in NLP, AI, and ML:

Clear Communication

Writing Skills

Since your work will ultimately be presented in the form of a research paper, clear and logical writing is essential. While most research papers are written in English, we emphasize the importance of clear thinking and structured writing over language proficiency alone.

Verbal Skills

Additionally, being able to express your ideas clearly through presentations and discussions is vital. This skill will help you promote your research at conferences and facilitate vibrant discussions and idea development within the research community.

Programming Skills & Mathematical Foundations

Programming Skills

Familiarity with Python 3 is highly recommended. Many NLP research projects utilize Python and libraries such as PyTorch and Hugging Face. Proficiency in these tools will enable you to effectively implement and test your research ideas.
    Additionally, understanding data structures and algorithms can be beneficial. While not mandatory, this knowledge provides a solid foundation for solving complex problems. Practice can be gained through platforms like LeetCode or Baekjoon.

Mathematical Foundations

A strong foundation in linear algebra, vector calculus, and probability is beneficial. However, most importantly, we seek individuals who are enthusiastic about learning and enjoy building a robust mathematical foundation.

Machine Learning & NLP

Understanding of key ML/DL models

Familiarity with key machine learning and deep learning algorithms will support your research endeavors. The curriculum at the Graduate School of Data Science (GSDS) at SNU will also provide you with such a background.

Current Knowledge and Critical Thinking

It's essential to stay informed about the latest developments in NLP, as well as to foster your own critical thinking about the problems that interest you.