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Computer Science

D-Index
35
Citations
7427
World Ranking
11506
National Ranking
4729

Overview

Trishul Chilimbi is affiliated with Amazon in the United States, where their research primarily focuses on the field of computer science. Their contributions span a significant number of publications in various subfields and topics mainly related to artificial intelligence and machine learning.

Within computer science, Chilimbi's work covers the following subfields:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Information Systems
  • Materials Chemistry

Their research addresses diverse topics including:

  • Multimodal Machine Learning Applications
  • Topic Modeling
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques
  • Machine Learning and Extreme Learning Machines (ELM)
  • Stochastic Gradient Optimization Techniques

Chilimbi has contributed to several publications, with a notable presence in both conference proceedings and preprint archives. Recent papers include:

  • Vision-Language Pre-Training with Triple Contrastive Learning, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Multi-modal Alignment using Representation Codebook, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Vision-Language Pre-Training with Triple Contrastive Learning, 2022, arXiv (Cornell University)
  • MiCS, 2022, Proceedings of the VLDB Endowment
  • MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud, 2022, arXiv (Cornell University)

The frequent venues where their work has appeared include:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Proceedings of the VLDB Endowment
  • Companion Proceedings of the Web Conference 2022

Chilimbi collaborates regularly with several researchers, with the most frequent coauthors being:

  • Belinda Zeng
  • Yi Xu
  • Son N. Tran
  • Li-Qun Chen
  • Tal Neiman

Best Publications

  • Project Adam: building an efficient and scalable deep learning training system

    Trishul Chilimbi;Yutaka Suzue;Johnson Apacible;Karthik Kalyanaraman

  • Green: a framework for supporting energy-conscious programming using controlled approximation

    Woongki Baek;Trishul M. Chilimbi

  • Cache-conscious structure layout

    Trishul M. Chilimbi;Mark D. Hill;James R. Larus

  • Vision-Language Pre-Training with Triple Contrastive Learning

    Unknown

  • SPEED: precise and efficient static estimation of program computational complexity

    Sumit Gulwani;Krishna K. Mehra;Trishul Chilimbi

  • Cache-conscious structure definition

    Trishul M. Chilimbi;Bob Davidson;James R. Larus

  • HOLMES: Effective statistical debugging via efficient path profiling

    Trishul M. Chilimbi;Ben Liblit;Krishna Mehra;Aditya V. Nori

  • Low-overhead memory leak detection using adaptive statistical profiling

    Matthias Hauswirth;Trishul M. Chilimbi

  • Dynamic hot data stream prefetching for general-purpose programs

    Trishul M. Chilimbi;Martin Hirzel

  • Web search using mobile cores: quantifying and mitigating the price of efficiency

    Vijay Janapa Reddi;Benjamin C. Lee;Trishul Chilimbi;Kushagra Vaid

  • Using generational garbage collection to implement cache-conscious data placement

    Trishul M. Chilimbi;James R. Larus

  • Efficient representations and abstractions for quantifying and exploiting data reference locality

    Trishul M. Chilimbi

  • Inferring locks for atomic sections

    Sigmund Cherem;Trishul Chilimbi;Sumit Gulwani

  • An efficient profile-analysis framework for data-layout optimizations

    Shai Rubin;Rastislav Bodík;Trishul Chilimbi

  • Data structure partitioning with garbage collection to optimize cache utilization

    Trishul M. Chilimbi;James R. Larus

  • Making pointer-based data structures cache conscious

    T.M. Chilimbi;M.D. Hill;J.R. Larus

  • Cache-conscious coallocation of hot data streams

    Trishul M. Chilimbi;Ran Shaham

  • Performance Modeling and Scalability Optimization of Distributed Deep Learning Systems

    Feng Yan;Olatunji Ruwase;Yuxiong He;Trishul Chilimbi

  • System and method for using data address sequences of a program in a software development tool

    Trishul M. Chilimbi

  • Dynamic prefetching of hot data streams

    Trishul Chilimbi;Martin Hirzel

  • SPEED: Precise and Efficient Static Estimation of Program Computational Complexity (Full Version)

    Sumit Gulwani;Krishna K. Mehra;Trishul Chilimbi

Frequent Co-Authors

James R. Larus
James R. Larus École Polytechnique Fédérale de Lausanne
Sumit Gulwani
Sumit Gulwani Microsoft (United States)
Aditya V. Nori
Aditya V. Nori Microsoft (United States)
Yuxiong He
Yuxiong He Microsoft (United States)
Martín Abadi
Martín Abadi Google (United States)
Galen C. Hunt
Galen C. Hunt Microsoft (United States)
Vijay Janapa Reddi
Vijay Janapa Reddi Harvard University
Onur Mutlu
Onur Mutlu ETH Zurich
Mark D. Hill
Mark D. Hill University of Wisconsin–Madison
Todd C. Mowry
Todd C. Mowry Carnegie Mellon University

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