CSE Doctoral Student Seminar: Wint Hnin and Kefu Lu

Sep 29, 2017
12:30 p.m.
2 p.m.
Lopata Hall, Room 101

"An Exploratory Study of the Usage of Different Educational Resources in an Independent Context"

Wint Hnin
Adviser: Caitlin Kelleher

There are a variety of learning resources with the potential to support children in learning programming independently. While many of them have been evaluated in laboratory settings, we know little about how children choose to use these resources on their own. We conducted a study organized around a film festival to explore children’s open-ended use of four different learning supports: tutorials, code puzzles, in-application documentation and code suggestions. The study began with a workshop to introduce the programming environment and available tools, continued through two weeks of home use, and culminated in a film festival. Results suggest that participants leveraged in-context forms of help most frequently, but valued documentation for question-answering and suggestions for opportunistic learning.


"Local Search Methods for k-means with Outliers"

Kefu Lu

We study the problem of k-means clustering in the presence of outliers. The goal is to cluster a given set of data points to minimize the variance of the points assigned to the same cluster, with the freedom of ignoring a small set of data points that can be labeled as outliers. Clustering with outliers has received a lot of attention in the data processing community, but practical, efficient, and provably good algorithms remain unknown for the most popular k-means objective. Our work proposes a simple local search-based algorithm for k-means clustering with outliers. We prove that this algorithm achieves constant-factor approximate solutions and can be combined with known sketching techniques to scale to large data sets. Using empirical evaluation on both synthetic and real-world data, we demonstrate that the algorithm performs strongly compared to recently proposed heuristic approaches for the problem.