World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
67
Citations
23752
World Ranking
2156
National Ranking
1081

Research.com Recognitions

  • 2018 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2017 - Member of the National Academy of Engineering For software innovation and leadership in high-performance computing.
  • 2017 - Fellow of the American Academy of Arts and Sciences
  • 2015 - ACM - IEEE CS Ken Kennedy Award For advancing the programmability of HPC systems, strategic national leadership, and mentorship in academia and government labs.
  • 2013 - ACM Athena Lecturer Award For contributions to improving fundamental understanding and practice of parallel programming.
  • 2012 - ACM Fellow For contributions to parallel languages that improve programmer productivity.

Overview

Katherine Yelick is affiliated with the University of California, Berkeley in the United States. The main field of study is Computer Science, with numerous contributions in several subfields, including Artificial Intelligence, Molecular Biology, Computer Networks and Communications, Information Systems, and Hardware and Architecture.

Their research work spans several topics, focusing primarily on Parallel Computing and Optimization Techniques, Genomics and Phylogenetic Studies, as well as Caching and Content Delivery. Additional areas covered include Algorithms and Data Compression, Cloud Computing and Resource Management, Bioinformatics and Genomic Networks, and Data Stream Mining Techniques.

Recent papers by Katherine Yelick include:

  • "ADEPT: a domain independent sequence alignment strategy for gpu architectures," 2020, BMC Bioinformatics
  • "CloudBank: Managed Services to Simplify Cloud Access for Computer Science Research and Education," 2021, Practice and Experience in Advanced Research Computing
  • "QFAST: Quantum Synthesis Using a Hierarchical Continuous Circuit Space," 2020, arXiv (Cornell University)
  • "The parallelism motifs of genomic data analysis," 2020, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
  • "PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction," 2020, arXiv (Cornell University)

Frequent publication venues include:

  • arXiv (Cornell University)
  • BMC Bioinformatics
  • Practice and Experience in Advanced Research Computing
  • Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
  • Journal of Logic and Analysis

Notable frequent co-authors are:

  • Aydın Buluç
  • Steven Hofmeyr
  • Oğuz Selvitopi
  • Leonid Oliker
  • Benjamin Brock

Katherine Yelick has received several professional recognitions, including:

  • Fellow of the American Association for the Advancement of Science (AAAS), 2018
  • Fellow of the American Academy of Arts and Sciences, 2017
  • Member of the National Academy of Engineering, 2017, for software innovation and leadership in high-performance computing
  • ACM - IEEE CS Ken Kennedy Award, 2015, for advancing the programmability of HPC systems, strategic national leadership, and mentorship in academia and government labs
  • ACM Athena Lecturer Award, 2013, for contributions to improving fundamental understanding and practice of parallel programming
  • ACM Fellow, 2012, for contributions to parallel languages that improve programmer productivity

Best Publications

  • The Landscape of Parallel Computing Research: A View from Berkeley

    Krste Asanovic;Ras Bodik;Bryan Christopher Catanzaro;Joseph James Gebis

  • A case for intelligent RAM

    D. Patterson;T. Anderson;N. Cardwell;R. Fromm

  • Optimization of sparse matrix-vector multiplication on emerging multicore platforms

    Samuel Williams;Leonid Oliker;Richard Vuduc;John Shalf

  • Optimization of sparse matrix-vector multiplication on emerging multicore platforms

    Samuel Williams;Leonid Oliker;Richard Vuduc;John Shalf

  • The International Exascale Software Project roadmap

    Jack Dongarra;Pete Beckman;Terry Moore;Patrick Aerts

  • A view of the parallel computing landscape

    Krste Asanovic;Rastislav Bodik;James Demmel;Tony Keaveny

  • Parallel programming in Split-C

    A. Krishnamurthy;D. E. Culler;A. Dusseau;S. C. Goldstein

  • Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures

    Kaushik Datta;Mark Murphy;Vasily Volkov;Samuel Williams

  • Titanium: a high-performance Java dialect

    Katherine A. Yelick;Katherine A. Yelick;Luigi Semenzato;Luigi Semenzato;Geoff Pike;Geoff Pike;Carleton Miyamoto;Carleton Miyamoto

  • OSKI: A Library of Automatically Tuned Sparse Matrix Kernels

    Richard Vuduc;James W Demmel;Katherine A Yelick

  • The potential of the cell processor for scientific computing

    Samuel Williams;John Shalf;Leonid Oliker;Shoaib Kamil

  • Introduction to UPC and Language Specification

    William W. Carlson;Jesse M. Draper;David E. Culler;Kathy Yelick

  • Sparsity: Optimization Framework for Sparse Matrix Kernels

    Eun-Jin Im;Katherine Yelick;Richard Vuduc

  • UPC: Distributed Shared-Memory Programming

    Tarek El-Ghazawi;William Carlson;Thomas Sterling;Katherine Yelick

  • Optimization and Performance Modeling of Stencil Computations on Modern Microprocessors

    Kaushik Datta;Shoaib Kamil;Samuel Williams;Leonid Oliker

  • A whole-genome shotgun approach for assembling and anchoring the hexaploid bread wheat genome

    Jarrod A Chapman;Martin Mascher;Aydın Buluç;Kerrie Barry

  • Scalable processors in the billion-transistor era: IRAM

    C.E. Kozyrakis;S. Perissakis;D. Patterson;T. Anderson

  • Self-Adapting Linear Algebra Algorithms and Software

    J. Demmel;J. Dongarra;V. Eijkhout;E. Fuentes

  • Cluster I/O with River: making the fast case common

    Remzi H. Arpaci-Dusseau;Eric Anderson;Noah Treuhaft;David E. Culler

  • UPC Language Specifications V1.1.1

    Tarek A. El-Ghazawi;George Washington;William W. Carlson;Jesse M. Draper

  • Optimization of Sparse Matrix-Vector Multiplication on EmergingMulticore Platforms

    Samuel W. Williams;Leonid Oliker;Richard Vuduc;John Shalf

  • Titanium: A High Performance Java Dialect.

    Arvind Krishnamurthy;Alexander Aiken;Phillip Colella

Frequent Co-Authors

Leonid Oliker
Leonid Oliker Lawrence Berkeley National Laboratory
Aydin Buluc
Aydin Buluc Lawrence Berkeley National Laboratory
James Demmel
James Demmel University of California, Berkeley
Samuel Williams
Samuel Williams Lawrence Berkeley National Laboratory
John Shalf
John Shalf Lawrence Berkeley National Laboratory
Arvind Krishnamurthy
Arvind Krishnamurthy University of Washington
David A. Patterson
David A. Patterson University of California, Berkeley
Jack Dongarra
Jack Dongarra University of Tennessee at Knoxville
Richard Vuduc
Richard Vuduc Georgia Institute of Technology
Tarek El-Ghazawi
Tarek El-Ghazawi George Washington University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science doesn’t stop with a traditional degree. Today, there are numerous flexible and affordable online options designed to meet a wide range of career goals. For those interested in data-driven roles, the cheapest masters in data science programs can open doors to high-growth tech sectors, combining affordability with quality.

If you prefer managing projects and teams, programs like an accelerated construction management degree online provide a fast track to leadership positions within booming industries. For aspiring business leaders, pursuing one of the cheapest mba programs online equips you with broad managerial skills while keeping costs in check.

Time-conscious learners can also benefit from 1 year master programs, which help students accelerate their studies and quickly enter the workforce. Regardless of your pathway, online degrees make it easier than ever to advance your education and career prospects in computer science and related fields.

Best Scientists Citing Katherine Yelick

Trending Scientists