How to get a research fellowship

CSSAP Fellowship at Rice University

In the past few years, many of my friends approached me for a piece of advice on how to find, apply, and get a research fellowship in the area of machine learning and artificial intelligence. This an attempt to structure and present this answer to a wider audience!

In this blog, I will share my brief experience and thorough thoughts on the how to apply and get a research opportunity. In addition to that, I will list links to all applications I am familiar with, and feel free to add your own in the comments.

Let’s try to make these opportunities fairer!

The easy start: What is my experience?

During my Bachelor’s and Master’s studies, I’ve had two major research fellowships, one in Houston, USA, and the other in Zurich, Switzerland.

The first fellowship was awarded as part of Computer Science Student Advancement Program, which is an amazing collaboration between the University of Belgrade, the University of Novi Sad, and Rice University. I was lucky enough to be part of the research group of Professor Lidya Kavraki. Although I’ve had a small research project back at home, in Belgrade, where I’ve studied Electrical Engineering, this experience was really a game-changer for my confidence, intellectual curiosity, and maturity. It was also the first time I’ve encountered such a strong female role model as Professor Lidya Kavraki.

The second one was ETH Student Summer Research Fellowship, which gathered students from 17 different countries in Zurich for an exciting and enlightening summer. It was highly competitive in terms of the selection process, being that only 20 students were admitted in the end, but it was perfectly organized at each step. I enjoyed every part of it: the research project, amazing and smart fellows, dedicated mentors and beautiful Switzerland.

Looking from this standpoint, especially having in mind this Covid era, I feel so grateful for these experiences because they were eye broadening, so unique and extremely transformative.

The big question : Why?

During my undergraduate studies, regular curriculum and passing exams seemed pretty manageable, so I began to worry and imagine what life after studies is supposed to look like. I’ve realized that my next big decision will be whether to go to academia or industry. To add a new dimension, I was pretty sure then (and now) that I would be great in both.

Knowing people and being engaged in the community made it possible to hear many times from people how it was during an internship in a company, but not so many stories came from the research. But somehow, listening to all these stories, I’ve heard only personal bias. Also, I was never actually satisfied with only “hearing”.

In general, listening, thinking, analyzing is great stuff, guys, but doing and deciding is what requires courage.

Instead of being confused and deep in the thoughts of what is really good for me, I’ve decided to actually find opportunities, try and maybe customize some of them and feel what best fits me. My advice to all students, but especially the ones who ought to live life proactively instead of reactively, is to try as many things as they can because exploring different opportunities dramatically increases your chances to actually discover your genuine passion.

How to prepare?

Starting an internship in the industry was relatively easy. Many local IT companies were offering positions and the interviewing process was more or less straightforward, In addition, you always had colleagues who already did it and probably are very keen to share tips and tricks (in case you actually run into the guys that don’t want to share, run away from them, they are already losers).

But, the story with academic opportunities was totally different. The process can be different from lab to lab, but most of the fellowships will ask you for:

  • CV (you should have this prepared any time)
  • Motivational letter/Personal Statement
  • Reference Letters
  • Transcripts (University issues this one, be aware to ask for English version)
  • Published papers (No worries if don’t have any, but make sure you mention them, in case you do)

CV

CV is a pretty straightforward part. There are plenty of resources on the internet on how to it so I will keep it short and give my personal opinion. Last year, I was interviewing people for Data Science roles and although this is quite different from research fellowship roles, I do have some experience with reading CVs, not just writing them.

Keep it brief, structured, and relevant.

There is a strong reason why general advice is to keep a CV on one page (and it’s not because interviewers are lazy when it comes to reading it). From my point of view, making yourself brief presents that you can communicate clearly, explain things effectively and that you’ve invested a certain effort in structuring your thoughts and finding the most effective phrases to explain your background/experience. All mentioned skills are true merit.

Structure helps the reader go through the CV easily.

Keeping relevant things is common sense, but let’s cover it. :) Is it really important to list all undergraduate projects you’ve ever worked on? Is this internship as a business analyst which you’ve done because of pocket money something which will convince this professor to grant you fellowship, and to mentor you for three months? If the answer is no, this piece of your CV is just wasting a beautiful white part of the page.

Motivational letter/Personal Statement

While a CV is easy, the second one can be tricky. There are certain differences between personal statement and motivation letter. The prior one should explain why you are the right person and the later one should prove that pursuing the opportunity will lead towards accomplishment of your dreams.

You need to make sure that in your letter is visible your ambition and motivation, your learning capacity and alignment with the vision and area of the lab you’re applying to.

Learning capacity is can be inferred through your transcripts and excellent academic records but also through interesting projects and competitions.

Regarding the alignment, the essential question you need to address in your statement is why do you want to be there? Are projects of the lab that you’ve seen on the website exciting? Do you have skills that are necessary? Do you have the vision of the particular project and do you think it goes along with their mission?

Be very specific and well informed about the existing labs at the University. Read their project page and if you have time, try reading papers they’ve published. If you find it hard to come with a whole new idea, try to think about interesting increments to the existing projects. Don’t be shy and contact one of the younger lab members (PhD students for example) and ask them to share with you their experience. Maybe through the conversation with your peers, you will get a different perspective which can help you eighter explain better your big why or help you realize their stuff is super boring to you. :)

These reflections are not just beneficial for writing a good motivational letter/project proposal, but it is a way for you to show proactiveness, diligence, ambition and creativity. In the end, this is also a kind of research and you’re applying for exactly that.

The second thing is to be true to yourself.

Nowadays, certain areas of science seem awesome (Deep Mind stole the show last week!). But besides listening and hyping about it, did you actually find any time to dig into it? Did you actually develop skills or improve the theoretical background necessary to conduct some experiments? If your answer is no, it is very probable that you’re not true to yourself but you’re wrapped in this circus of hypes.

For example, I’ve started implementing machine learning algorithms during the second year of my studies because it was very fun for me. I was using Matlab ( I hear a laugh and I approve :D ) because I really enjoyed transferring math to code and to see it in action.

I’ve learned Python not because it was cool or it was hype but because I realized that if I want to do some serious stuff with Data Science and to have more fun and less pain, this tool will help me. Just as a comparison, a year ago, I tried to learn Java and I hated it. Because that is not my passion. I don’t enjoy learning new coding languages but I loved learning Python because mastering this tool will help me to actually do things that are fun to me and I started immediately. Nowadays, people are going crazy about so many things. Machine Learning, AI, mindfulness, fasting, yoga… you pick.

It is easy to get caught up in that hype and to start doing something because it is “cool”.

However, this is not sustainable and this is not what brings long term happiness. This is not something that enriches your personality and makes you an independent thinker (better said, independent doer).

Reference Letters

When it comes to reference letters, make sure to pick professors who mentored you during some meaningful project. For example, if you are a master’s student and someone mentored you during writing your undergraduate thesis or if your coursework required working on a project, not just passing exams. It would be perfect if the research area of that professor is matching with the research area of the lab you’re applying to, but even if this is not the case, don’t worry. Sometimes, writing reference letters can take some time, so be mindful of the availability of your referrals and don’t wait until the last minute.

Feel free to watch small demonstration of the system I’ve built in Houston. You won’t understand it because it is not made to be understandable, but you will see the thrill and the excitement on my face while performing seemingly boring actions. That’s the happiness of learning and building something new!

Math and coffee lover, passionate for data science and encouraging girls in STEM