  After completing their undergraduate studies, many computer science (CS)
students apply for competitive graduate programs in North America. Their
long-term goal is often to be hired by one of the big five tech companies or to
become a faculty member. Therefore, being aware of the role of admission
criteria may help them choose the best path towards their goals. In this paper,
we analyze the influence of students' previous universities on their chances of
being accepted to prestigious North American universities and returning to
academia as professors in the future. Our findings demonstrate that the ranking
of their prior universities is a significant factor in achieving their goals.
We then illustrate that there is a bias in the undergraduate institutions of
students admitted to the top 25 computer science programs. Finally, we employ
machine learning models to forecast the success of professors at these
universities. We achieved an RMSE of 7.85 for this prediction task.
