Summary of Research 2001, Department of Computer Science, Graduate School of Operational and Information Sciences

2002 ◽  
Author(s):  
Chris Eagle ◽  
Nell C. Rowe
Author(s):  
Nigel Ward ◽  

Potential applicants to graduate school find it difficult to predict, even approximately, which schools will accept them. We have created a predictive model of admissions decision-making, packaged in the form of a web page that allows students to enter their information and see a list of schools where they are likely to be accepted. This paper explains the rationale for the model’s design and parameter values. Interesting issues include the way that evidence is combined, the estimation of parameters, and the modeling of uncertainty.


AI Magazine ◽  
2020 ◽  
Vol 41 (1) ◽  
pp. 90-100
Author(s):  
Sven Koenig

Begin with the end in mind!1 PhD students in artificial intelligence can start to prepare for their career after their PhD degree immediately when joining graduate school, and probably in many more ways than they think. To help them with that, I asked current PhD students and recent PhD computer-science graduates from the University of Southern California and my own PhD students to recount the important lessons they learned (perhaps too late) and added the advice of Nobel Prize and Turing Award winners and many other researchers (including my own reflections), to create this article.


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