PhD Alum Earns ACM SIGKDD Doctoral Dissertation Award

PhD Alumi, now a research scientist at Google, may be spending part of his summer in Halifax, Nova Scotia as he accepts the Association of Computing Machinery (ACM) SIGKDD, Doctoral Dissertation award for his work at Stony Brook. The annual award acknowledges excellent doctoral research in the field of data mining and knowledge discovery.

This award recognizes Bryan’s thesis, , as the best dissertation of the year in the data science community. Bryan's thesis revolved around graph embeddings, ways of representing the knowledge encoded in the structure of networks to make them accessible for machine learning models.

Focused on developing scalable algorithms and models for attributed graphs, Bryan presented an online learning algorithm utilizing recent advances in deep learning to result in rich graph embeddings. The applications of this research are far reaching for the fields of data mining, information retrieval, profiling and demographic inference, online advertising and fraud detection.

Upon learning of the award Bryan, who defended his thesis in May 2016 and was advised by Professor Steven Skiena, was thrilled and said, “Wow, what an honor! I'm humbled to have my work recognized by this prestigious early career award and I am looking forward to giving a talk during the Doctoral Dissertation Award session on Aug. 15th."

Professor Skiena is especially proud of Bryan’s research accomplishments. In fact, they are collaborators on a number of published works. Their paper on has already been cited 270 times in Google Scholar since its publication in 2014.


“Bryan was a very creative, hardworking, and independent graduate student here at Stony Brook, and his work on DeepWalk has proven extremely influential in the data science and machine learning communities. They got the right man for this award”, said Skiena. ​

At Google, Bryan is working on research related to the intersection of data mining, machine learning, graph theory, and network science with a particular focus on local graph algorithms. Most recently in January 2017, he published and presented which he collaborated on with (a current PhD student at Stony Brook) and Leman Akoglu (CMU faculty).

Founded in 1947, Association for Computing Machinery (ACM) is the largest and oldest scientific and industrial computing society. SIGKDD is the ACM’s Special Interest Group on Knowledge Discovery and Data Mining. SIGKDD selects one winner and two runner-ups each year and last year they selected student Danai Koutra for her thesis, Exploring and Making Sense of Large Graphs. Selections are based on the relevance to KDD, originality, scientific significance, technical depth and soundness, and overall presentation and readability.

Bryan is the first PhD student in the Department of Computer Science, which is part of the at Stony Brook University, to receive this award. Congratulations, Bryan!