World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
6150
World Ranking
12037
National Ranking
4910

Overview

Carole-Jean Wu is affiliated with Meta Platforms, Inc. in the United States, contributing extensively to the field of computer science. Their work spans multiple subfields including artificial intelligence, computer networks and communications, electrical and electronic engineering, information systems, and computer vision and pattern recognition.

The primary research topics covered in Carole-Jean Wu's publications include green IT and sustainability, recommender systems and techniques, advanced neural network applications, privacy-preserving technologies in data, parallel computing and optimization techniques, stochastic gradient optimization techniques, and cloud computing and resource management.

Throughout their career, Carole-Jean Wu has contributed to a significant number of publications, with notable frequent venues being arXiv (Cornell University), IEEE Micro, ACM Transactions on Architecture and Code Optimization, ACM SIGEnergy Energy Informatics Review, and IEEE Transactions on Computers.

Selected recent papers authored or co-authored by Carole-Jean Wu include:

  • Sustainable AI: Environmental Implications, Challenges and Opportunities, 2021, arXiv (Cornell University)
  • MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance, 2020, IEEE Micro
  • Chasing Carbon: The Elusive Environmental Footprint of Computing, 2022, IEEE Micro
  • DataPerf: Benchmarks for Data-Centric AI Development, 2022, arXiv (Cornell University)
  • DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference, 2020, arXiv (Cornell University)

Frequent collaborators in Carole-Jean Wu's research include Bilge Acun, Gu-Yeon Wei, Udit Gupta, David Brooks, and Newsha Ardalani. These coauthors have contributed to numerous joint publications, reflecting ongoing collaborative research relationships.

Best Publications

  • Deep Learning Recommendation Model for Personalization and Recommendation Systems

    Maxim Naumov;Dheevatsa Mudigere;Hao-Jun Michael Shi;Jianyu Huang

  • Machine Learning at Facebook: Understanding Inference at the Edge

    Carole-Jean Wu;David Brooks;Kevin Chen;Douglas Chen

  • MLPerf inference benchmark

    Vijay Janapa Reddi;Christine Cheng;David Kanter;Peter Mattson

  • Sustainable AI: Environmental Implications, Challenges and Opportunities.

    Carole-Jean Wu;Ramya Raghavendra;Udit Gupta;Bilge Acun

  • SHiP: signature-based hit predictor for high performance caching

    Carole-Jean Wu;Aamer Jaleel;Will Hasenplaugh;Margaret Martonosi

  • Chasing Carbon: The Elusive Environmental Footprint of Computing

    Udit Gupta;Young Geun Kim;Sylvia Lee;Jordan Tse

  • The Architectural Implications of Facebook's DNN-Based Personalized Recommendation

    Udit Gupta;Carole-Jean Wu;Xiaodong Wang;Maxim Naumov

  • RecNMP: accelerating personalized recommendation with near-memory processing

    Liu Ke;Udit Gupta;Benjamin Youngjae Cho;David Brooks

  • MCM-GPU: Multi-Chip-Module GPUs for Continued Performance Scalability

    Akhil Arunkumar;Evgeny Bolotin;Benjamin Cho;Ugljesa Milic

  • MLPerf Training Benchmark.

    Peter Mattson;Christine Cheng;Cody Coleman;Greg Diamos

  • ACT: designing sustainable computer systems with an architectural carbon modeling tool

    Unknown

  • DeepRecSys: a system for optimizing end-to-end at-scale neural recommendation inference

    Udit Gupta;Samuel Hsia;Vikram Saraph;Xiaodong Wang

  • MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance

    Peter Mattson;Hanlin Tang;Gu-Yeon Wei;Carole-Jean Wu

  • PACMan: prefetch-aware cache management for high performance caching

    Carole-Jean Wu;Aamer Jaleel;Margaret Martonosi;Simon C. Steely

  • MLPerf Training Benchmark

    Peter Mattson;Christine Cheng;Gregory F. Diamos;Cody Coleman

  • Carbon Explorer: A Holistic Framework for Designing Carbon Aware Datacenters

    Unknown

  • CAWA: coordinated warp scheduling and cache prioritization for critical warp acceleration of GPGPU workloads

    Shin-Ying Lee;Akhil Arunkumar;Carole-Jean Wu

  • CAWS: criticality-aware warp scheduling for GPGPU workloads

    Shin-Ying Lee;Carole-Jean Wu

  • Understanding data storage and ingestion for large-scale deep recommendation model training: industrial product

    Unknown

  • DataPerf: Benchmarks for Data-Centric AI Development

    Unknown

  • Performance, energy characterizations and architectural implications of an emerging mobile platform benchmark suite - MobileBench

    Dhinakaran Pandiyan;Shin-Ying Lee;Carole-Jean Wu

  • RecSSD: near data processing for solid state drive based recommendation inference

    Mark Wilkening;Udit Gupta;Samuel Hsia;Caroline Trippel

  • AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning

    Young Geun Kim;Carole-Jean Wu

  • The Architectural Implications of Facebook's DNN-based Personalized Recommendation

    Udit Gupta;Carole-Jean Wu;Xiaodong Wang;Maxim Naumov

Frequent Co-Authors

David Brooks
David Brooks Harvard University
Kim Hazelwood
Kim Hazelwood Facebook (United States)
Gu-Yeon Wei
Gu-Yeon Wei Harvard University
Hsien-Hsin S. Lee
Hsien-Hsin S. Lee Intel (United States)
Mikhail Smelyanskiy
Mikhail Smelyanskiy Nvidia (United States)
Vijay Janapa Reddi
Vijay Janapa Reddi Harvard University
Patrick E. Phelan
Patrick E. Phelan Arizona State University
Margaret Martonosi
Margaret Martonosi Princeton University
Trevor Mudge
Trevor Mudge University of Michigan–Ann Arbor
Sarma Vrudhula
Sarma Vrudhula Arizona State University

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