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Yunshu Du

dblp:206/6394 · DBLP profile ↗
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2ranked-venue papers
2as first author
1since 2021 · last 2022
0000-0001-9891-211XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › pattern mining
behavioral pattern mining
0.412019
Analysis of University Fitness Center Data Uncovers Interesting Patterns, Enables Prediction · IEEE Trans. Knowl. Data Eng. 2019
Data mining
pattern mining
0.412019
Analysis of University Fitness Center Data Uncovers Interesting Patterns, Enables Prediction · IEEE Trans. Knowl. Data Eng. 2019
Data mining
predictive modeling
0.412019
Analysis of University Fitness Center Data Uncovers Interesting Patterns, Enables Prediction · IEEE Trans. Knowl. Data Eng. 2019

Methods — techniques the papers use, named apart from their topics

predictive modeling · 0.4data science process · 0.4
YearPublicationVenuePosition
2022 Lucid dreaming for experience replay: refreshing past states with the current policy
Yunshu Du, Garrett Warnell, Assefaw Hadish Gebremedhin, Peter Stone 0001, Matthew E. Taylor
Neural Comput. Appl.1
2019 Analysis of University Fitness Center Data Uncovers Interesting Patterns, Enables Prediction
abstract
Data is increasingly being used to make everyday life easier and better. Applications such as waiting time estimation, traffic prediction, and parking search are good examples of how data from different sources can be used to facilitate our daily life. In this study, we consider an under-utilized data source: university ID cards. Such cards are used on many campuses to purchase food, allow access to different areas, and even take attendance in classes. In this article, we use data from our university to analyze usage of the university fitness center and build a predictor for future visit volume. The work makes several contributions: it demonstrates the richness of the data source, shows how the data can be leveraged to improve student services, discovers interesting trends and behavior, and serves as a case study illustrating the entire data science process.
Yunshu Du, Assefaw Hadish Gebremedhin, Matthew E. Taylor
IEEE Trans. Knowl. Data Eng.1