Ümit V. Çatalyürek

About

Ümit V. Çatalyürek is a Professor in the School of Computational Science and Engineering in the College of Computing at the Georgia Institute of Technology. Prior to joining Georgia Tech, he was a Professor and Vice Chair of the Department of Biomedical Informatics, and a Professor in the Departments of Electrical & Computer Engineering, and Computer Science & Engineering at the Ohio State University. He received his Ph.D., M.S., and B.S. in Computer Engineering and Information Science from Bilkent University, Turkey, in 2000, 1994, and 1992, respectively. Dr. Çatalyürek is a Fellow of IEEE and SIAM, and has served as the elected Chair for IEEE TCPP for 2016-2019 and as Vice-Chair for ACM SIGBio for 2015-2021. He also serves as a member of the Board of Trustees of Bilkent University and as the Editor-in-Chief for Parallel Computing. His past editorial roles include positions on the editorial boards of IEEE Transactions on Parallel and Distributed Computing Systems, SIAM Journal of Scientific Computing, Journal of Parallel and Distributed Computing, and Network Modeling and Analysis in Health Informatics and Bioinformatics. Dr. Çatalyürek is a recipient of an NSF CAREER award and is the primary investigator of several awards from the Department of Energy, the National Institute of Health, and the National Science Foundation. His main research areas include parallel computing, combinatorial scientific computing, and biomedical informatics, and he has co-authored more than 200…

Research topics

• Computer science
• Parallel computing
• Theoretical computer science
• Distributed computing
• Algorithm

Selected publications

• More Recent Advances in (Hyper)Graph Partitioning

  ACM Computing Surveys · 2022-11-23 · 71 citations

  review1st authorCorresponding

  In recent years, significant advances have been made in the design and evaluation of balanced (hyper)graph partitioning algorithms. We survey trends of the past decade in practical algorithms for balanced (hyper)graph partitioning together with future research directions. Our work serves as an update to a previous survey on the topic [ 29 ]. In particular, the survey extends the previous survey by also covering hypergraph partitioning and has an additional focus on parallel algorithms.

  PublisherDOI

• Shared-Memory Scalable k-Core Maintenance on Dynamic Graphs and Hypergraphs

  2021-06-01 · 17 citations

  articleOpen accessSenior author

  Computing k-cores on graphs is an important graph mining target as it provides an efficient means of identifying a graph's dense and cohesive regions. Computing k-cores on hypergraphs has seen recent interest, as many datasets naturally produce hypergraphs. Maintaining k-cores as the underlying data changes is important as graphs are large, growing, and continuously modified. In many practical applications, the graph updates are bursty, both with periods of significant activity and periods of re…

  PublisherOA PDFDOI

• A Unifying Framework to Identify Dense Subgraphs on Streams: Graph Nuclei to Hypergraph Cores

  2021-03-06 · 12 citations

  articleOpen accessSenior author

  Finding dense regions of graphs is fundamental in graph mining. We focus on the computation of dense hierarchies and regions with graph nuclei---a generalization of k-cores and trusses. Static computation of nuclei, namely through variants of 'peeling', are easy to understand and implement. However, many practically important graphs undergo continuous change. Dynamic algorithms, maintaining nucleus computations on dynamic graph streams, are nuanced and require significant effort to port between…

  PublisherOA PDFDOI

• Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs

  arXiv (Cornell University) · 2022-10-24 · 10 citations

  preprintOpen accessSenior author

  Graph Neural Networks (GNNs) have received significant attention recently, but training them at a large scale remains a challenge. Mini-batch training coupled with sampling is used to alleviate this challenge. However, existing approaches either suffer from the neighborhood explosion phenomenon or have poor performance. To address these issues, we propose a new sampling algorithm called LAyer-neighBOR sampling (LABOR). It is designed to be a direct replacement for Neighbor Sampling (NS) with the…

  PublisherOA PDFDOI

• Efficient Hierarchical State Vector Simulation of Quantum Circuits via Acyclic Graph Partitioning

  2022-09-01 · 10 citations

  article

  Early but promising results in quantum computing have been enabled by the concurrent development of quan-tum algorithms, devices, and materials. Classical simulation of quantum programs has enabled the design and analysis of algorithms and implementation strategies targeting current and anticipated quantum device architectures. In this paper, we present a graph-based approach to achieving efficient quantum circuit simulation. Our approach involves partitioning the graph representation of a given…

  PublisherDOI

Frequent coauthors

• David Padua

  747 shared

• Michael Gerndt

  166 shared

• Davide Sangiorgi

  University of Bologna

  152 shared

• Piotr Łuszczek

  110 shared

• Jack Dongarra

  110 shared

• Cevdet Aykanat

  Bilkent University

  93 shared

• George Almási

  IBM (United States)

  89 shared

• John A. Gunnels

  89 shared

Education

• PhD, Computer Engineering and Information Science

  Bilkent Universitesi

  2000

Awards & honors

• Fellow of IEEE
• Fellow of SIAM
• NSF CAREER award

Similar researchers at Georgia Institute of Technology

• Frank Dellaerth-65
• Mark Borodovskyh-64
• Irfan Essah-63