Motivated by the needs of big data and cybersecurity applications, he works at the intersection of algorithms, computer systems and hardware design, with recent research focus on developing high-performance computing and machine learning techniques tailored for large-scale graph datasets. His lab explores novel applications of graph-based knowledge discovery in computer systems, cybersecurity, social networks, biology and health. His work on big graph traversal has ranked highly on both the Graph500 and Green Graph500 benchmarks, which measure the performance and energy efficiency of the most powerful data-intensive supercomputers in the world. His projects were presented in USENIX ATC'17, FAST'17, SC'16, SIGMOD'16, and SC'15, and also featured in GPU Technology Conference and NVIDIA Theater.