Note for Prospective GRAs

> A Note for Prospective BS/MS/PhD Students <

You are interest in a Research Assistant (RA) position in my team. Please read this note carefully, and if you think you qualify and you would enjoy being on my team, please follow the instruction at the end of this page.

What is this for? It has always been a joy for me to work with graduate students. However, I've been receiving an overwhelming number of emails requesting RA positions. To be frank, many of these emails are just generic expressions of passion and interest in my research, leading me to treat them as spam. To minimize my false-negative rate, however, I am trying to build a bridge for those who are a good fit for my projects to reach me more easily. I hope that this effort also makes your life a little easier.

For M.S./Ph.D. Students:

Being fluent in Python — you can easily work with popular libraries such as numpy, pandas, scikit-learn, opencv, pillow, and matplotlib.
Being comfortable with statistics — you have a working knowledge of the basic concepts such as correlation, regression, probability distribution, and hypothesis testing.
Being familiar with the basics of data mining and machine learning — you have a good understanding of the basic concepts such as supervised and unsupervised learning, model fitting, bias-variance trade-off, classification loss, and class imbalance.
[mostly for PhD students] Being fluent in reading/writing scientific articles in English — you can learn the new techniques directly from published papers, and you can write down your own scientific methods in the form of a scientific paper.

*Knowledge of database systems, deep learning, time series analysis, front-end and back-end systems are big pluses.

For B.S. students:

- Being comfortable with Python — you can easily implement a simple idea into a working code. For example, write a method that takes a string and reverse it, with O(1) extra space.
- Being comfortable with algorithms and data structures — you can reason for choosing one data structure over another, and one algorithm design over another, based on the time and space complexity analysis.

*Knowledge of database systems, front-end, and back-end systems are big pluses.

So you've read this and you feel good about it. What's next?

There will be three simple steps:

Application: Email me your CV/Resume, research sample, TOEFL/IELTS/GRE certificates (only for non-native English speakers), and anything that could exhibit your skill, experience, and determination.
Technical Interview: I will get back to you and schedule an interview. The interview will have 3 sections: self-introduction, a technical conversation about your knowledge of Math/Stat/ML/DL (only for MS/PhD), followed by a coding interview.
Decision: I will send you an email if you cleared the technical interview, and you will join my team.

Google Sites

Report abuse