Kefu Lu Assistant Professor of Computer Science
Ph.D., Computer Science at Washington University in St. Louis (2019)
B.A. Physics and Computer Science from Washington University in St. Louis (2014)
Research Interests include approximation algorithms, parallel computing, machine learning and data analysis.
As computers become more and more parallel, Professor Lu is interested in how to best harness the power of these complex systems. Professor Lu seeks to develop faster and more efficient algorithms for data analysis methods, especially techniques commonly used in machine learning. His most recent work is on data clustering and other previous work include optimizing the performance of parallel processors for dynamically arriving tasks.
Professor Lu is teaching the introductory computer science course. Aside from this, he will be the instructor for the analysis of algorithms and other courses in the theory of computer science. He also teaches upper level electives in parallel computing and big data analysis from both a data science and computer science perspective.
Silvio Lattanzi, Thomas Lavastida, Kefu Lu, Ben Moseley. A Framework for Parallelizing Hierarchical Clustering Methods, European Conference on Machine Learning - PKDD, 2019.
Kunal Agrawal, Jing Li, Kefu Lu, Ben Moseley. Scheduling Parallelizable Jobs to Maximize Throughput. Latin American Symposium on Theoretical Informatics, 2018.
Gustavo Malkomes, Kefu Lu, Blakely Hoffman, Roman Garnett, Ben Moseley, Richard Mann. Cooperative set function optimization without communication or coordination. Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017.
Shalmoli Gupta, Ravi Kumar, Kefu Lu, Ben Moseley, Sergei Vassilvitskii. Local Search Methods For k-means With Outliers. Proceedings of the Conference on Very Large DataBases, 2017.