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.

翻译：完成本科阶段学习后，许多计算机科学（CS）学生将申请北美竞争激烈的研究生项目。他们的长期目标通常是进入五大科技公司之一或成为高校教师。因此，了解招生标准的作用有助于他们选择实现目标的最佳路径。本文分析了学生先前就读院校对其被北美顶尖大学录取以及未来重返学术界担任教授的可能性的影响。研究结果表明，先前院校的排名是实现目标的关键因素。我们进一步揭示，排名前25的计算机科学项目录取的学生在本科院校背景上存在偏差。最后，我们采用机器学习模型预测这些大学教授的成功前景，在该预测任务中实现了7.85的均方根误差（RMSE）。