Julian Shun

Julian Shun joined the Department of Electrical Engineering and Computer Science as an assistant professor in September. He received a bachelor’s degree in computer science from the University of California at Berkeley, and a PhD in computer science from Carnegie Mellon University (CMU). Before coming to MIT, he was a postdoctoral Miller Research Fellow at UC Berkeley. Shun’s research focuses on the theory and practice of parallel algorithms and programming. He is particularly interested in designing algorithms and frameworks for large-scale graph analytics. He is also interested in parallel algorithms for text analytics, concurrent data structures, and methods for deterministic parallelism. Shun received the ACM doctoral dissertation award, the CMU School of Computer Science doctoral dissertation award, a Facebook graduate fellowship, and a best-student-paper award at the Data Compression Conference.

Experience

Massachusetts Institute of Technology Associate Professor of Electrical Engineering and Computer Science (EECS)

Principal Investigator MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)

University of California, Berkeley Miller Postdoctoral Research Fellow 2015 – 2017

Education

Carnegie Mellon University Doctor of Philosophy - PhD, Computer Science

University of California, Berkeley Bachelor of Arts - BA, Computer Science

Publications

Papers

A Parallel Batch-Dynamic Data Structure for the Closest Pair Problem (2021)

Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering (2021)

Parallel Clique Counting and Peeling Algorithms (2021)

Parallel Index-Based Structural Graph Clustering and Its Approximation (2021)

Chiller: Contention-centric Transaction Execution and Data Partitioning for Modern Networks (2021)

Parallel Algorithms for Butterfly Computations (2020)

Sage: parallel semi-asymmetric graph algorithms for NVRAMs (2020)

Exploring the Design Space of Static and Incremental Graph Connectivity Algorithms on GPUs (2020)

Compliation Techniques for Graphs Algorithms on GPUs (2020)

Randomized Incremental Convex Hull is Highly Parallel (2020)

Parallel Batch-Dynamic $k$-Clique Counting (2020)

The Graph Based Benchmark Suite (GBBS) (2020)

Optimizing ordered graph algorithms with GraphIt (2020)

Chiller: Contention-centric Transaction Execution and Data Partitioning for Modern Networks (2020)

Connectlt: A Framework For Static And Incremental Parallel Graph Connectivity Algorithms (2020)

Parallelism in Randomized Incremental Algorithms (2020)

Theoretically-Efficient and Practical Parallel DBSCAN (2020)

Practical parallel hypergraph algorithms (2020)

Improved Parallel Construction of Wavelet Trees and Rank/Select Structures (2020)

Low-Latency Graph Streaming Using Compressed Purely-Functional Trees (2019)