World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
8931
World Ranking
12883
National Ranking
5196

Overview

Shivaram Venkataraman is affiliated with the University of Wisconsin-Madison in the United States. Their research primarily focuses on the domain of computer science with an emphasis on artificial intelligence, computer vision and pattern recognition, and computer networks and communications.

The scientist's work spans a variety of subfields, including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Information Systems
  • Electrical and Electronic Engineering

Key research topics covered in their publications include:

  • Cloud Computing and Resource Management
  • Stochastic Gradient Optimization Techniques
  • Advanced Neural Network Applications
  • Advanced Graph Neural Networks
  • Advanced Data Storage Technologies
  • Data Stream Mining Techniques
  • Machine Learning and Data Classification

Shivaram Venkataraman has published extensively, with frequent appearances in several publication venues such as:

  • arXiv (Cornell University)
  • Proceedings of the VLDB Endowment
  • IEEE Journal on Selected Areas in Information Theory
  • SAE technical papers on CD-ROM/SAE technical paper series
  • Journal of Power Sources

Some recent notable papers include:

  • LlamaTune, 2022, Proceedings of the VLDB Endowment
  • Learning-Based Coded Computation, 2020, IEEE Journal on Selected Areas in Information Theory
  • On the Utility of Gradient Compression in Distributed Training Systems, 2021, arXiv (Cornell University)
  • AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning, 2021, arXiv (Cornell University)
  • Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification, 2020, arXiv (Cornell University)

Frequently collaborating authors include:

  • Jason Mohoney
  • Theodoros Rekatsinas
  • Aditya Akella
  • Konstantinos Kanellis
  • Roger Waleffe

Best Publications

  • Apache Spark: a unified engine for big data processing

    Matei Zaharia;Reynold S. Xin;Patrick Wendell;Tathagata Das

  • MLlib: machine learning in apache spark

    Xiangrui Meng;Joseph Bradley;Burak Yavuz;Evan Sparks

  • Occupy the cloud: distributed computing for the 99%

    Eric Jonas;Qifan Pu;Shivaram Venkataraman;Ion Stoica

  • Consistent and durable data structures for non-volatile byte-addressable memory

    Shivaram Venkataraman;Niraj Tolia;Parthasarathy Ranganathan;Roy H. Campbell

  • Ernest: efficient performance prediction for large-scale advanced analytics

    Shivaram Venkataraman;Zongheng Yang;Michael Franklin;Benjamin Recht

  • Cherrypick: adaptively unearthing the best cloud configurations for big data analytics

    Omid Alipourfard;Hongqiang Harry Liu;Jianshu Chen;Shivaram Venkataraman

  • MLlib: Machine Learning in Apache Spark

    Xiangrui Meng;Joseph Bradley;Burak Yavuz;Evan Sparks

  • Probabilistically bounded staleness for practical partial quorums

    Peter Bailis;Shivaram Venkataraman;Michael J. Franklin;Joseph M. Hellerstein

  • Drizzle: Fast and Adaptable Stream Processing at Scale

    Shivaram Venkataraman;Aurojit Panda;Kay Ousterhout;Michael Armbrust

  • Analysis of Large-Scale Multi-Tenant {GPU} Clusters for {DNN} Training Workloads

    Myeongjae Jeon;Shivaram Venkataraman;Amar Phanishayee;Junjie Qian

  • Focus: querying large video datasets with low latency and low cost

    Kevin Hsieh;Ganesh Ananthanarayanan;Peter Bodik;Shivaram Venkataraman

  • KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics

    Evan R. Sparks;Shivaram Venkataraman;Tomer Kaftan;Michael J. Franklin

  • Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure

    Qifan Pu;Shivaram Venkataraman;Ion Stoica

  • Presto: distributed machine learning and graph processing with sparse matrices

    Shivaram Venkataraman;Erik Bodzsar;Indrajit Roy;Alvin AuYoung

  • Cake: enabling high-level SLOs on shared storage systems

    Andrew Wang;Shivaram Venkataraman;Sara Alspaugh;Randy Katz

  • The power of choice in data-aware cluster scheduling

    Shivaram Venkataraman;Aurojit Panda;Ganesh Ananthanarayanan;Michael J. Franklin

  • Matrix Computations and Optimization in Apache Spark

    Reza Bosagh Zadeh;Xiangrui Meng;Alexander Ulanov;Burak Yavuz

  • The case for tiny tasks in compute clusters

    Kay Ousterhout;Aurojit Panda;Joshua Rosen;Shivaram Venkataraman

  • SparkR: Scaling R Programs with Spark

    Shivaram Venkataraman;Zongheng Yang;Davies Liu;Eric Liang

  • numpywren: serverless linear algebra

    Vaishaal Shankar;Karl Krauth;Qifan Pu;Eric Jonas

  • Serverless linear algebra

    Vaishaal Shankar;Karl Krauth;Kailas Vodrahalli;Qifan Pu

  • Quantifying eventual consistency with PBS

    Peter Bailis;Shivaram Venkataraman;Michael J. Franklin;Joseph M. Hellerstein

  • Blink: Fast and Generic Collectives for Distributed ML

    Guanhua Wang;Shivaram Venkataraman;Amar Phanishayee;Jorgen Thelin

  • Matrix Computations and Optimization in Apache Spark

    Reza Bosagh Zadeh;Xiangrui Meng;Aaron Staple;Burak Yavuz

Frequent Co-Authors

Ion Stoica
Ion Stoica University of California, Berkeley
Michael J. Franklin
Michael J. Franklin University of Chicago
Benjamin Recht
Benjamin Recht University of California, Berkeley
Aditya Akella
Aditya Akella The University of Texas at Austin
Roy H. Campbell
Roy H. Campbell University of Illinois at Urbana-Champaign
Matei Zaharia
Matei Zaharia University of California, Berkeley
Peter Bailis
Peter Bailis Stanford University
Joseph M. Hellerstein
Joseph M. Hellerstein University of California, Berkeley
Aurojit Panda
Aurojit Panda New York University
Scott Shenker
Scott Shenker University of California, Berkeley

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