Thoughts of PhD research

At the end of my 3-year PhD, I want to record some of my thoughts on the researches in natural langauge processing and machine learning. Especially, I want to help those new students who are just started their voyage in the (red) ocean of machine learning. Help them to efficiently find their goal of research, and how to reach the goal.

In these three years, I talked to many master and PhD students in this field and I found they generally can be categorized into three groups:

  1. Interested in creating something cool with artificial intelligence
  2. Want to publish a good paper in a good conference
  3. Care more about how the PhD can benefit their careers

I believe that all these motivations are good and the best students can have a combination of them all. However, sadly, some students I’m worrying a lot are exteremly biased in only one motivation. In the long run, such a biased motivation can make the PhD really painful, but not a joyful process.

Group 1. I just want to do something new and cool with AI These students are the best to work with actually, because they are highly motivated and passionate about learning new things. But, one pitfall of such a mindset is that you can be lost in your own fantasy. The academic world is not working like this. Because given the exponentially growing researcher population recently, it’s highly unlikely that you are the first one to think about your idea. In the worst scenario, your idea may be developed for multiple years by others. And you are going to find them anyway if you finally want to publish your paper and compare your method with others. So the best thing to do is not just jumping into experimenting your own idea, but do a survey, at least briefly, on how other people are solving the same or similar problems you want to solve. In some cases, you are thinking that you tackling a brand-new problem, but that’s not true. Many machine learning problems share the same intrinsic property. For example, if you want to generate a future frame in a video clip. Suppose you can’t find a paper on this subject, but it really is a conditional image generation problem.

Another possible scenario is that you are setting a goal way too big for a PhD student. In this case, you may be stuck in the same problem for years without a publication. This is stressful and you may finally give up. So the best thing is to solve a small but crucial problem.

Group 2. My goal is to publish papers in good conference All PhD students need to publish papers in order to gradudate. However, it’s dangerous to chase the trend and attempt to publish a paper with better scores, which is seemingly a shortcut for publication. It is the same reason why the restaurants are do difficult to survive, because there are so many competitors. You will have very limited amount of time to develop your approach, and will be disappointed a lot if someone else published a paper with the same idea as yours. It’s better to find a sweet spot to propose a impactful model and evalaute a thoroughly rather than crunching the numbers. Competing with scores is also stressful anyway.

Group 3. Care more about how the PhD can benefit their careers Internship can cost the will power, especially for those internship not contributing to your PhD research. Try to set a time budget for the internship, then forget it completely when back to school. Don’t try to do the internship in the morning and do research in the afternoon, it will not work. Some students cared too much about their careers, so they finally lose the will power to do their PhD research.

Hope these thoughts can help new students in this field and I’m willing to write more based on my experience.