Da Yan. Da Yan

Assistant Professor

Research and Teaching Interests: Big Data Management, Distributed Computing, Data Mining

Office Hours: By appointment


  • B.S., Fudan University (FDU), Computer Science
  • Ph.D., The Hong Kong University of Science and Technology (HKUST), Computer Science

Dr. Yan received his Ph.D. degree in Computer Science from the Hong Kong University of Science and Technology in 2014, and his B.S. degree in Computer Science from Fudan University, Shanghai, in 2009. He was the winner of HKIS-Towngas 2015 Young Scientist Award in Physical/Mathematical Science.

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Dr. Yan’s research at UAB focuses on developing scalable systems and algorithms for Big Data analytics. During his postdoctoral research at the Chinese University of Hong Kong he led a research group to develop a comprehensive Big Graph analytics platform BigGraph@CUHK with many open-source subsystems used by both academia and industry, and with many papers published in first-tier conferences in Computer Science. Besides graph-structured data (e.g., online social networks and the Web graph), he also studies the querying and mining of other types of Big Data, such as geo-spatial data (e.g., road networks, trajectory data) and data uncertainty.

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Dr. Yan is interested in solving practical Big Data problems, which have strong real-life applications and meanwhile require non-trivial solutions.

Research Opportunities

Dr. Yan is currently looking for PhD students interested in Big Data research. Please feel free to contact him, and please include your CV.
In recent years, Dr. Yan has been working on improving Google’s Pregel (a distributed graph processing framework with user-friendly API) from various aspects including algorithm design, computation model, messaging mechanism, on-demand querying support, out-of-core support, and fault tolerance. These techniques often improve the performance of Pregel by orders of magnitude. Due to Dr. Yan’s contributions in the field of “Big Graph analytics systems” (including a dozen first-tier conference and journal publications), he was invited as the first author by Foundations and Trends in Databases to write a survey paper in this area, and he also gave a tutorial on the topic in SIGMOD 2016.

Research on Big Graph analytics systems has surged in recent years, but it mostly targets data-intensive graph analytics tasks such as PageRank computation. Limited efforts have been devoted to exploring general-purpose frameworks for computation-intensive graph analytics tasks such as motif mining and frequent subgraph pattern mining, and existing solutions are far from satisfactory. Therefore, Dr. Yan’s research now focuses on developing scalable Big Graph platforms for solving computation-intensive tasks, and he has worked out a few effective prototypes to be further improved upon. He is also interested in applying new hardware to solve Big Data problems.
  • Da Yan, Yingyi Bu, Yuanyuan Tian, Amol Deshpande, and James Cheng. 2016. Big Graph Analytics Systems (Tutorial). ACM International Conference on Management of Data (SIGMOD).
  • Da Yan, James Cheng, M. Tamer Özsu, Fan Yang, Yi Lu, John C.S. Lui, Qizhen Zhang, and Wilfred Ng. A General-Purpose Query-Centric Framework for Querying Big Graphs. Proceedings of the VLDB Endowment. 564-75.
  • Da Yan, James Cheng, Yi Lu, and Wilfred Ng. 2015. Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation. World Wide Web Conference (WWW). 1307-17.
  • Da Yan, Zhou Zhao, Wilfred Ng, and Steven Liu. 2015. Probabilistic Convex Hull Queries over Uncertain Data. IEEE Transactions on Knowledge and Data Engineering 27, 3, 852-65.
  • Da Yan, James Cheng, Yi Lu, and Wilfred Ng. 2014. Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs. Proceedings of the VLDB Endowment 7, 14. 1981-92.
  • Da Yan, James Cheng, Kai Xing, Yi Lu, Wilfred Ng, and Yingyi Bu. 2014. Pregel Algorithms for Graph Connectivity Problems with Performance Guarantees. Proceedings of the VLDB Endowment 7, 14, 1821-32.
  • Da Yan, James Cheng, Wilfred Ng, and Steven Liu. 2013. Finding Distance-Preserving Subgraphs in Large Road Networks. IEEE International Conference on Data Engineering. 625-36.
  • Da Yan, Zhou Zhao, and Wilfred Ng. 2011. Efficient Algorithms for Finding Optimal Meeting Point on Road Networks. Proceedings of the VLDB Endowment. 968-79.
  • Da Yan and Wilfred Ng. 2011. Robust Ranking of Uncertain Data (Best Paper award). Database Systems for Advanced Applications, 254-68.