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：完成本科学习后，许多计算机科学学生申请北美竞争激烈的研究生项目。他们的长期目标通常是受聘于五大科技公司之一或成为教职人员。因此，了解录取标准的作用可能帮助他们选择达成目标的最佳路径。本文分析了学生先前就读大学对其被北美名校录取及未来重返学术界担任教授的可能性影响。研究发现，先前大学的排名是实现其目标的关键因素。我们进一步表明，排名前25的计算机科学项目录取学生中存在本科院校偏见。最后，我们运用机器学习模型预测这些大学教授的成功率，在该预测任务中取得了7.85的均方根误差。