If you are reading this, then you probably finished the long and arduous journey to grad school. You emerged victoriously, and this success is well-deserved. But which school should you choose? How to make a right choice if all schools look great in their own way? This blog post is centered around these questions. It is most useful if you are a computer science student aiming to study machine learning and, in particular, natural language processing in the US, but most of the information here is equally valid for any field of research and any country.
The choice of grad school that is right for you can be tricky and confusing. We live in a time of hyper-competitiveness, where even undergrads need to optimize for metrics like paper count to make it to the next level — grad school. This heavily career-centered perspective was probably advantageous to get you into grad school, and it remains crucial to get you to the level after that: a great job in industry or academia. So choosing the school which is best for your career can feel like an obvious choice. However, a PhD is a very long journey, and choosing your grad school based on this perspective alone might make you more vulnerable to burn-out, disillusionment, and general dissatisfaction.
In this blog post, I will discuss this career-centered perspective in detail, but I also provide you with three other views that hopefully help you make a balanced choice that not only leads to academic success but long-term satisfaction and a full and rich life. Balancing your decision based on all four perspectives probably leads to a better choice than looking at one angle alone. Before I go into the details, let me briefly introduce these four perspectives: The Career Perspective, the Identity Perspective, the Stability Perspective, and the Variability Perspective.