Julian Shun

Julian Shun is a researcher affiliated with the Massachusetts Institute of Technology. His work focuses on Algorithm Engineering, Parallel Computing, and Shared-Memory Systems, with significant contributions to large-scale graph processing frameworks like Ligra, GBBS, and Aspen. His research has demonstrated that shared-memory multiprocessors can outperform distributed systems in graph analysis while being cost- and energy-efficient.

Shun's work introduces the Ligra parallel programming abstraction, which simplifies graph algorithm implementation through high-level primitives.

• ACM Paris Kanellakis Theory and Practice Award (2023): Recognizing his role in developing frameworks that revolutionized graph processing on shared-memory machines.
• ACM Doctoral Dissertation Award (2015): For his thesis on making shared-memory parallelism simple, fast, and scalable.

Previously, he was a post-doctoral researcher at the University of California, Berkeley, under a Miller Research Fellowship, and earned his PhD from Carnegie Mellon University. He completed his undergraduate studies at UC Berkeley, graduating first in his class in 2008.