Quick-Start Survey for Research Mentees
Key information about the research mentorship by Zhijing Jin:
1) Usually professors are too busy to directly supervise undergraduate students. For our research project, Zhijing Jin (zhijing-jin.com) will mentor you. She is experienced in mentoring students, and will conduct the interview to check if you are a good fit for the research project.
2) We appreciate candidates with *strong coding skills*, and knowledge of NLP/AI models.
3) Usually the expected output of a research collaboration/internship is a research paper. To target at a paper, you need to devote time and hard work.
4) Additional takeaways from the research internship: If you want to apply for grad schools, you can receive a recommendation letter from the professor. To get a strong recommendation, Zhijing will draft some bullet points to the professor based on your performance, and at the end of the project, we can have a meeting to present our research outputs to the professor. (Or in some cases, if the professor has free time, we can have monthly reports to the prof.)
Email *
Name *
Google Drive account (if different from your email)
Link to your CV (e.g., Google Drive link); and link to your website (if applicable) *
Link to your transcript (for us to check your courses and knowledge background)
GitHub account name *
What is your aim for this research collaboration/internship? (Answer using bullet points.) And if applicable, mention your target grad schools to apply for MSc/PhD to. *
[Your Previous Papers & Coding Skills] Provide your GitHub link; and if applicable, list your past projects/papers, where each line includes (1) link to the paper (published and unpublished writeup), (2) conference and year (if applicable), and (3) part of the code that you programmed (with GitHub links, and notes about which parts you implemented) *
Expected start date of the research collaboration/internship *
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Expected end date of the research collaboration/internship *
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How much time will you contribute every week? Support this number with evidence, e.g., your schedule during the semester break, or your class schedule during the semester and semester start and end time. *
Research Skill 1: What deep learning codebase have you built?
For all the questions afterwards, they are optional, but they can show your research abilities. So the more details you answer, the better we can evaluate whether you are a suitable candidate.
(Put "N/A" if this question does not apply to you.) What deep learning models have you coded before (e.g., adaptations of BERT, Fairseq, etc)? If applicable, support your answer with your GitHub repos. (If you haven't hands-on experience with models yet, tell us some deep learning model codebases that you've read before and are familiar with.) *
(Put "N/A" if this question does not apply to you.) How experienced are you with tuning hyperparameters: Write about your hyperparameter tuning experiences. For seq-to-seq models, what hyperparameters do you look at? And the intuition behind. (If you haven't experience on this before, feel free to tell us some hyperparameters (e.g., learning rate) that you think are important based on your deep learning knowledge.) *
(Put "N/A" if this question does not apply to you.) How long do you configure the deep learning environment: Describe one of your past experience for the most complicated server that you’ve set up the deep learning environment on. What did you do, and how long does the entire configuration take? (Basically configuration here means everything before you can start to run the program.) *
Research Skill 2: Reading papers?
What NLP/general AI tasks do you usually read papers about, e.g., NER, QA, etc. (and how many papers for each task, as a rough estimation)? Put "N/A" if this question is not applicable. *
Have you done surveys/literature reviews? Or show some evidence on how fast you can read papers, and what kind of useful takeaways you usually obtain from papers. Put "N/A" if this question is not applicable. *
What deep learning knowledge are you familiar with? If applicable, share a link of your deep learning course materials/notes/etc. (Put "N/A" if this question does not apply to you.) *
(Optional) Any additional information that you want us to know.
A copy of your responses will be emailed to the address you provided.
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