Carole-Jean Wu
RESEARCH SCIENTIST | CAMBRIDGE, UNITED STATES
Carole-Jean Wu is a Research Scientist at Facebook’s AI Research. Carole's research focuses in the area of Computer and System Architectures, in particular, on designing high-performance and energy-efficient computer systems through domain-specific architectures, heterogeneity-aware optimization, energy harvesting techniques, temperature and energy management for portable electronics, and memory subsystem design and optimization. More recently, Carole-Jean's research has pivoted into designing systems for machine learning execution at-scale, such as for personalized recommender systems and mobile deployment. She chairs the MLPerf Recommendation Benchmark Advisory Board and co-chairs MLPerf Inference, a multi-industry benchmarking consortium for machine learning. She completed her M.A. and Ph.D. degrees from Princeton University and received a B.Sc. degree from Cornell University. She is the recipient of the NSF CAREER Award, Facebook AI Infrastructure Mentorship Award, the IEEE Young Engineer of the Year Award, the Science Foundation Arizona Bisgrove Early Career Scholarship, and the Intel PhD Fellowship, among a number of Best Paper awards.
Carole-Jean's Publications
December 12, 2024
CORE MACHINE LEARNING
SYSTEMS RESEARCH
Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Luca Foschini, Pieter Gijsbers, Joan Giner-Miguelez, Sujata Goswami, Nitisha Jain, Michalis Karamousadakis, Satyapriya Krishna, Michael Kuchnik, Sylvain Lesage, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Hamidah Oderinwale, Pierre Ruyssen, Tim Santos, Rajat Shinde, Elena Simperl, Arjun Suresh, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Susheel Varma, Jos van der Velde, Steffen Vogler, Carole-Jean Wu, Luyao Zhang
December 12, 2024
June 14, 2024
NLP
SYSTEMS RESEARCH
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Bilge Acun, Ahmed Aly, Beidi Chen, Carole-Jean Wu, Ahmed Roman, Nas Mahmoud, Saurabh Agarwal
June 14, 2024
June 07, 2024
CORE MACHINE LEARNING
SYSTEMS RESEARCH
Beyond Efficiency: Scaling AI Sustainably
Carole-Jean Wu, Bilge Acun, Ramya Raghavendra, Kim Hazelwood
June 07, 2024
June 03, 2024
SYSTEMS RESEARCH
CHAI: Clustered Head Attention for Efficient LLM Inference
Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Carole-Jean Wu
June 03, 2024
April 26, 2023
CORE MACHINE LEARNING
SYSTEMS RESEARCH
Green Federated Learning
Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk Krüger, Mike Rabbat, Carole-Jean Wu, Ilya Mironov
April 26, 2023
July 12, 2022
Carbon Dependencies in Datacenter Design and Management
Bilge Acun, Aditya Sundarrajan, Benjamin Lee, Carole-Jean Wu, David Brooks, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy
July 12, 2022
June 13, 2022
ACT: Designing Sustainable Computer Systems With An Architectural Carbon Modeling Tool
Carole-Jean Wu, David Brooks, Hsien-Hsin Sean Lee, Udit Gupta, Gage Hills, Gu-Yoen Wei, Mariam Elgamal
June 13, 2022
April 26, 2022
PAPAYA: PRACTICAL, PRIVATE, AND SCALABLE FEDERATED LEARNING
Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, Kaikai Wang, Anthony Shoumikhin, Jesik Min, Mani Malek
April 26, 2022
April 02, 2022
Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation
Liu Ke, Carole-Jean Wu, Hsien-Hsin Sean Lee, Udit Gupta, Mark Hempstead, Xuan Zhang
April 02, 2022
Foundational models
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