Writing a Personal Statement for Grad School Applications
At the end of October, I attended a virtual personal statement workshop hosted by University of Waterloo’s Psychology Department Equity, Diversity, and Inclusion (EDI) Working Group. I went into the workshop not knowing what a personal statement was, much less how to write a successful one or even where to start. But by the end of it, I had a clear idea of the steps I needed to take to write one for my future graduate school applications. Through this post, I hope to share what I learn to those who also may benefit from it.
The workshop was led by two presenters: Abigail Scholer, a social psychology faculty member, and Sarah English, a first year PhD social psychology candidate and MD. Both presenters were very enthusiastic about providing us undergraduate students the information we needed to enhance our success at the graduate school application process.
From attending this personal statement workshop, I learned a) tips on getting the writing processes started and b) specifics on what to include in my personal statement. Personally, I find that with any major task, especially ones that can seem as daunting as this, getting started is often the hardest part. Professor Scholer and Sarah first highlighted that its beneficial to start off with brain storming and creating an outline to help you organize your thoughts to get started. This can consist of figuring out what skills and experiences are the most relevant to your application and then putting together concise statements regarding those skills and experiences that you want to highlight. When you do start putting together your personal statement, these preliminary steps will help you stay focused on the important bits and give you a roadmap to follow.
As for specifics on what to include in your personal statement, Professor Scholer and Sarah recommend providing specific examples highlighting your research experiences and interests. Examples should help illustrate your research process and thinking skills and abilities. The content of your examples could be regarding your honours thesis project or even relevant work/volunteer experiences that showcase how you are an asset to the program of interest.
The presenters also recommended including a section on why you are the right fit for the program or the particular faculty member that you are interested in working with. This may require you to do some background research regarding the program or faculty member’s research. Different levels of specificity may also be required depending on the school’s expectations, so it is important to read the instructions on the application expectations. The presenters emphasized that you will have multiple drafts of your statement and that the first one does not need to be perfect! You may even have to create different versions of your statement for different programs, although in this case it is helpful to have one standard draft that you can then customize towards a specific program.
In the end, get feedback from people you know! This may be from a professor or lab members that you are connected with but having your trusted friends or family members read over your statement can be just as helpful as well.
Future events hosted by the EDI Working Group are posted about on their website. While this workshop focused on applying to graduate school in psychology, I believe the steps and tips discussed are generalizable to other programs as well.
This summer, I participated in Neuromatch Academy (NMA) Summer Online school as a TA for Computational Neuroscience (CN) and a student for Deep Learning (DL). It’s been several months away since I submitted our group’s final DL project for NMA, but what happened those 7 weeks are still vivid today.
Taking the NMA Online course was like eating a box of chocolates with a plethora of flavors. You never know how different tomorrow’s chocolate is going to taste. You must be prepared to learn knowledge from all kinds of areas. For CN course, it covers materials not only in neuroscience but also from information theory, machine learning, signal processing. For DL, knowledge from computational version, natural language processing, reinforcement learning, and so on were digested by us in 3 weeks. You could choose to participate in either CN, DL, or both.
NMA encouraged everybody to finish a project by the end of each course. This might sound intimidating. In fact, I heard that many groups failed to submit a proposal for the final project before the deadline. So, the deadline was extended. Still, many of them ended up completing amazing final projects. Keep in mind that, we do not need to master any of the fields in order to finish the project, NMA also provides nice templates for us to use. Additionally, many project ideas come from the previous project which is organized well on NMA website. Only experts could link knowledge from all kinds of areas together, and it is a lifelong process. For this course we just demand to understand what we need so far.
Suggestions for Beginners
For beginners, it is both rewarding and challenging. You have to be self-motivated enough to finish the course. If you have only a little background in math and computer science, NMA provided us with some precourse material to learn before the summer. I highly recommend any beginner to watch the books, the video and do some practice during your free time. Those materials are fundamental for you to understand the course material. And if you fail to understand some key materials, you are likely to lose your way in the following courses.
I hope this did not scare you away from participating in NMA. That’s all the hard part about NMA. Once you handled it, you are going to have some wonderful swim in the sea of knowledge. You will be split up into different pods paired with one TA. The daily course consists of several short videos followed by some programming exercises. You have 3-4 hours to learn the material within your pod and the next 3-4 hours to do the final project with your group members. NMA provides many interactive demos for you to play in the jupyter notebook. For example, you can visualize what principal features learned by your neural network. Many tutorials included widgets for you to adjust the parameters in one function, and you will see how the output will change immediately. NMA also provides daily quizzes (around 4-5 questions) that help you to check your understanding. Don’t worry, your performance won’t influence whether you would receive certification or not.
Suggestions for TA
As a TA, I knew some of my students might not want to answer questions or even ask questions because they have no clue about what I am talking about. Sometimes, discussion with several random group members before revealing the answer together as a whole group is beneficial. Therefore, I coded up a random group generator to help them. You might say, zoom has this function already. But most of the time, I also use it as a way to generate a schedule of who will answer which question. Just by setting how many groups you want to generate equal to the total number of people, you will have a number before each student’s name. (you can find the code on my GitHub). I showed this schedule to my students before class, so they could prepare the answer well. If they fail to answer the question, I will provide more sub-questions to guide them. It is helpful when we articulate our thinking and sometimes the answer will become oblivious as we are speaking.
When you use the random group generator, make sure to put your students’ names into the variable student_name.
var student_name = "Apple Zeng\n"+ "Mango Wu\n"+ "Pear Zhang\n"+ "Avacado Gao\n"+ "Potato Han\n"+ "Banana Chen\n"+ "Tomato Li\n"
I always remind myself and my students that “You are not alone”! We can also discuss questions with TA or classmates in the zoom breakout room. Plus, there is a discord channel for us to connect with students from all over the world. It was strange for me to have a discussion with students from Iran, Turkey, and Greece about how to be a good TA. Even though some of the countries may have different political ideologies, in that room, we are just students who love computational neuroscience. You can always post questions in the discord, the faculty members who designed those tutorials will sometimes answer your questions, or they may realize there are some mistakes in the material, HAHA.
Suggestions for TA
In the end, I want to say, completing NMA might not provide me the ability to finish one project from scratch. However, it gives me the courage to read papers full of formulas that I don’t understand at all before, and it teach me some skills to search for information that will tackle hard questions bit by bit.
In 2011 I published a screed against the reification of attention in psychology. Basically, the argument was that attention is not a “thing”, and by treating it as such we trick ourselves into thinking we have an explanation when we don’t, and we miss out on the opportunity to pursue the real causal mechanisms.
Recently Wayne Wu (who has a great book on Attention) coordinated a collection of perspectives on “What is Attention?” for Wiley’s Wires Cognitive Science series, and I had a chance to revisit my ideas on this topic. Basically, I feel my original arguments stand, and I didn’t feel like repeating the same ideas. Rather, I took psychology’s failure to use a word like attention in a productive way as a particular example of a general phenomenon: conceptual fragmentation, and to suggest that this justified eliminativism. I also shared an idea (which others have as well) that psychology’s susceptibility to this kind of problem lies in psychology not being theory based. Lastly, I put in a plug for category theory as a language that might help us exit the confusing conceptual soup in which we all work.
Let me know what you think of these ideas.
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