American graduate admissions: both sides of the table
type: CreativeWork
description: This is a comprehensive study of graduate admission process in American universities. There are multiple entities involved in the process, out of which the most significant ones are: • The candidate applying for admission in a department in a school • The decision-makers acting upon the candidates' application The goal of this study is to understand the admission process from each of these entities' perspective, and provide them decision-support models for their respective tasks. Although both of the entities interact through a common set of datapoints, i.e. candidate admission application, each of them works towards a very different goal. The juxtaposition of these two tasks provides a very interesting challenge which is hard to resolve deterministically. Solution to such a problem requires learning techniques which can find patterns, adapt according to the dynamic nature of problem, and produce results in a probabilistic fashion. We study and model the graduate admission process from a machine learning perspective based on analysis of large amounts of data. The analysis considers factors such as standardized test scores, and GPA, as well as world knowledge such as university similarity, reputation, and constraints. Based on the targeted entity, learning problem is formulated as classification problem or ranking problem. During learning and inference, not only those features are considered which are available from the data directly, but also the hidden features which need to be incorporated generatively. Our experimental study reveals some key factors in the decision process and, consequently, allows us to propose a recommendation algorithm that provides applicants the ability to make an informed decision regarding where to apply, as well as guides the decision-makers towards a more efficient process.
abstract: This is a comprehensive study of graduate admission process in American universities. There are multiple entities involved in the process, out of which the most significant ones are: • The candidate applying for admission in a department in a school • The decision-makers acting upon the candidates' application The goal of this study is to understand the admission process from each of these entities' perspective, and provide them decision-support models for their respective tasks. Although both of the entities interact through a common set of datapoints, i.e. candidate admission application, each of them works towards a very different goal. The juxtaposition of these two tasks provides a very interesting challenge which is hard to resolve deterministically. Solution to such a problem requires learning techniques which can find patterns, adapt according to the dynamic nature of problem, and produce results in a probabilistic fashion. We study and model the graduate admission process from a machine learning perspective based on analysis of large amounts of data. The analysis considers factors such as standardized test scores, and GPA, as well as world knowledge such as university similarity, reputation, and constraints. Based on the targeted entity, learning problem is formulated as classification problem or ranking problem. During learning and inference, not only those features are considered which are available from the data directly, but also the hidden features which need to be incorporated generatively. Our experimental study reveals some key factors in the decision process and, consequently, allows us to propose a recommendation algorithm that provides applicants the ability to make an informed decision regarding where to apply, as well as guides the decision-makers towards a more efficient process.
author: Gupta, Narender
language: en
keywords: Graduate Admissions, Machine Learning, Latent Variable
date published: 2016-11-10T17:55:18Z
date modified: 2026-07-22T22:26:35Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/92866