H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 55 Citations 9,124 232 World Ranking 2174 National Ranking 1182

Research.com Recognitions

Awards & Achievements

2016 - IEEE Fellow For contributions to combinatorial scientific computing and parallel computing

Overview

What is he best known for?

The fields of study he is best known for:

  • Operating system
  • Artificial intelligence
  • Algorithm

The scientist’s investigation covers issues in Parallel computing, Hypergraph, Sparse matrix, Theoretical computer science and Distributed computing. His studies deal with areas such as Scheduling, Processor scheduling and Scalability as well as Parallel computing. The various areas that Ümit V. Çatalyürek examines in his Hypergraph study include Computer cluster, Vertex, Graph theory, Parallel algorithm and Load balancing.

His Sparse matrix research incorporates elements of Matrix decomposition, Matrix multiplication and Graph partition. His Theoretical computer science research includes elements of Combinatorics and Graph. Within one scientific family, Ümit V. Çatalyürek focuses on topics pertaining to Data mining under Distributed computing, and may sometimes address concerns connected to Parallel processing, Range and Reference genome.

His most cited work include:

  • Hypergraph-partitioning-based decomposition for parallel sparse-matrix vector multiplication (422 citations)
  • A Scalable Distributed Parallel Breadth-First Search Algorithm on BlueGene/L (224 citations)
  • Parallel hypergraph partitioning for scientific computing (191 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Parallel computing, Distributed computing, Theoretical computer science, Data mining and Algorithm. His study in Parallel computing is interdisciplinary in nature, drawing from both Scheduling, Load balancing and Scalability. His work in Distributed computing addresses subjects such as Grid computing, which are connected to disciplines such as Distributed database.

The Theoretical computer science study combines topics in areas such as Hypergraph, Graph, Graph coloring, Graph and Computation. Ümit V. Çatalyürek has researched Hypergraph in several fields, including Sparse matrix, Graph theory and Graph partition. His biological study spans a wide range of topics, including Parallel algorithm and Greedy coloring.

He most often published in these fields:

  • Parallel computing (27.71%)
  • Distributed computing (18.79%)
  • Theoretical computer science (17.20%)

What were the highlights of his more recent work (between 2014-2021)?

  • Graph (10.19%)
  • Parallel computing (27.71%)
  • Theoretical computer science (17.20%)

In recent papers he was focusing on the following fields of study:

Ümit V. Çatalyürek spends much of his time researching Graph, Parallel computing, Theoretical computer science, Algorithm and Computation. His research on Graph also deals with topics like

  • Directed acyclic graph which intersects with area such as Directed graph and Partition,
  • Heuristics, which have a strong connection to Square matrix, Matrix, Space partitioning and Sparse matrix. Many of his research projects under Parallel computing are closely connected to Linear algebra with Linear algebra, tying the diverse disciplines of science together.

Ümit V. Çatalyürek has included themes like Hypergraph, Betweenness centrality, Centrality, Graph and Load balancing in his Theoretical computer science study. The concepts of his Algorithm study are interwoven with issues in Field-programmable gate array, Vertex, Thread and Memory bandwidth. His Computation study incorporates themes from Scheduling and Multi-core processor.

Between 2014 and 2021, his most popular works were:

  • Tracing Origins of the Salmonella Bareilly Strain Causing a Food-borne Outbreak in the United States. (95 citations)
  • Finding the Hierarchy of Dense Subgraphs using Nucleus Decompositions (77 citations)
  • Hypergraph partitioning for multiple communication cost metrics (33 citations)

In his most recent research, the most cited papers focused on:

  • Operating system
  • Artificial intelligence
  • Algorithm

Ümit V. Çatalyürek mainly investigates Theoretical computer science, Parallel computing, Graph, Algorithm and Betweenness centrality. His study looks at the relationship between Theoretical computer science and fields such as Graph, as well as how they intersect with chemical problems. The study incorporates disciplines such as Load balancing and Distributed computing in addition to Hypergraph.

Ümit V. Çatalyürek combines subjects such as Sparse matrix, Scalability and Partition with his study of Parallel computing. In general Sparse matrix, his work in Sparse matrix-vector multiplication is often linked to Performance improvement linking many areas of study. Ümit V. Çatalyürek focuses mostly in the field of Algorithm, narrowing it down to matters related to Vertex and, in some cases, Matching, Complement graph, Pseudoforest and Graph power.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Hypergraph-partitioning-based decomposition for parallel sparse-matrix vector multiplication

U.V. Catalyurek;C. Aykanat.
IEEE Transactions on Parallel and Distributed Systems (1999)

660 Citations

A Scalable Distributed Parallel Breadth-First Search Algorithm on BlueGene/L

Andy Yoo;Edmond Chow;Keith Henderson;William McLendon.
conference on high performance computing (supercomputing) (2005)

349 Citations

Parallel hypergraph partitioning for scientific computing

Karen D. Devine;Erik G. Boman;Robert T. Heaphy;Rob H. Bisseling.
international parallel and distributed processing symposium (2006)

290 Citations

Benchmarking short sequence mapping tools

Ayat Hatem;Doruk Bozdağ;Amanda E Toland;Ümit V Çatalyürek.
BMC Bioinformatics (2013)

266 Citations

Distributed processing of very large datasets with DataCutter

Michael D. Beynon;Tahsin Kurc;Umit Catalyurek;Chialin Chang.
parallel computing (2001)

264 Citations

A comparative analysis of biclustering algorithms for gene expression data

Kemal Eren;Mehmet Deveci;Onur Küçüktunç;Ümit V. Çatalyürek.
Briefings in Bioinformatics (2013)

252 Citations

Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development

O. Sertel;J. Kong;H. Shimada;U. V. Catalyurek.
Pattern Recognition (2009)

240 Citations

Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading

Olcay Sertel;Jun Kong;Umit V. Catalyurek;Gerard Lozanski.
signal processing systems (2009)

208 Citations

Hypergraph-based Dynamic Load Balancing for Adaptive Scientific Computations

U.V. Catalyurek;E.G. Boman;K.D. Devine;D. Bozdag.
international parallel and distributed processing symposium (2007)

200 Citations

Performance Evaluation of Sparse Matrix Multiplication Kernels on Intel Xeon Phi

Erik Saule;Kamer Kaya;Ümit V. Çatalyürek.
international conference on parallel processing (2013)

197 Citations

Editorial Boards

Parallel Computing
(Impact Factor: 0.983)

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