2017 David J. Kuck Outstanding Thesis Awards
These awards were established by alumni, former students, and friends in recognition of Professor Kuck's intellectual and leadership contributions. Each year, two awards are given: one for an outstanding doctoral thesis and one for an outstanding master's thesis.
Kuck was professor of computer science from 1965-1993. In 1977, he developed the Parafrase compiler system, which is used as a test bed for the development of many new ideas on vectorization and program transformation. He led the construction of Cedar in 1985, a 32-processor SMP supercomputer built at Illinois. He was founder of Kuck and Associates and won numerous awards, including the Eckert-Mauchly Award from ACM/IEEE, the IEEE Computer Society's Computer Pioneer Award, the Charles Babbage Outstanding Scientist Award, and the CS @ ILLINOIS Distinguished Educator Award. In 2015, Kuck was inducted into the College of Engineering Hall of Fame.
Andrei Ştefănescu is a research scientist at Facebook working on machine learning techniques for code analysis.
Ştefănescu’s PhD work included techniques for automatically building efficient correct-by-construction program verifiers from operational semantics, and techniques for automated reasoning with a focus on proving data-structure properties. His efforts culminated in his thesis, "Runtime Software Verification Using Separation Logic."
Ştefănescu has been recognized with the Association for Computing Machinery’s Distinguished Paper Award. He also was part of the Illinois Distinguished Fellowship program, which recognizes exceptional applicants to the University of Illinois Graduate College.
Last updated 2017
Focused on data mining, machine learning and natural language processing, Xiang Ren is a doctoral student working with Abel Bliss Professor Jiawei Han and the Data and Information System Research Lab. Ren will join the Computer Science Department at University of Southern California as an assistant professor in the Spring of 2018.
Ren develops data-driven and machine learning methods for turning unstructured text data into machine-actionable structures. His master’s thesis explored a heterogeneous information network based clustering approach, called ClusCite, for research citation recommendations based on the contents, authors, and targeted venue of a working paper. The method learns group memberships for objects and the significance of relevance features for each interest group, while also propagating relative authority between objects, by solving a joint optimization problem.
The thesis advances the frontiers of clustering analysis in heterogeneous information networks, demonstrating a double-digit improvement over conventional methods when analyzing two large, real datasets: DBLP (a computer science bibliographic database) and PubMed (a biomedical bibliography database).
Ren’s research has been recognized with awards that include a Google PhD Fellowship, an Association for Computing Machinery SIGKDD Scholarship, an Outstanding Reviewer Award from the 2017 World Wide Web Conference, a Yahoo!-DAIS Research Excellence Award, and the 2016 C. W. Gear Outstanding Graduate Student Award from CS @ ILLINOIS.
Technologies that Ren has developed have been used by the U.S. Army Research Laboratory, the National Institutes of Health, Microsoft, Yelp, and TripAdvisor.
Last updated 2017