My expertise lies at the interface between systems and machine learning. I serve as the west coast head of engineering for Facebook AI Research (FAIR), as well as the research area lead for the SysML team, where we focus on advancing the state-of-the-art across the full stack for end-to-end pipelines for machine learning. Prior to FAIR, I was part of Facebook AI Infrastructure, where I worked on building efficient and scalable hardware and software platforms for Facebook’s production and mobile use cases of machine learning.
Prior to joining Facebook in 2015, I held positions including tenured Associate Professor at the University of Virginia, Software Engineer at Google, and Director of Systems Research at Yahoo Labs. I received a PhD in computer science from Harvard University, and am the recipient of an NSF CAREER Award, the Anita Borg Early Career Award, the MIT Technology Review Top 35 Innovators under 35 Award, the ACM SIGPLAN 10-Year Test of Time Award, and the 2020 CRA Distinguished Service Award. I have authored over 50 conference papers and one book.
Interests
SysML, AI/ML compilers, frameworks, optimizations, performance analysis, developer tools, HW/SW co-design, datacenter-scale solutions
Latest Publications
HPCA - February 27, 2021
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale
Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim Hazelwood
ISCA - May 22, 2020
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
Liu Ke, Udit Gupta, Benjamin Youngjae Cho, David Brooks, Vikas Chandra, Utku Diril, Amin Firoozshahian, Kim Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Meng Li, Bert Maher, Dheevatsa Mudigere, Maxim Naumov, Martin Schatz, Mikhail Smelyanskiy, Xiaodong Wang, Brandon Reagen, Carole-Jean Wu, Mark Hempstead, Xuan Zhang
HPCA - February 17, 2020
The Architectural Implications of Facebook’s DNN-based Personalized Recommendation
Udit Gupta, Carole-Jean Wu, Xiaodong Wang, Maxim Naumov, Brandon Reagen, David Brooks, Bradford Cottel, Kim Hazelwood, Mark Hempstead, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang
SysML - April 2, 2019
Bandana: Using Non-Volatile Memory for Storing Deep Learning Models
Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim Hazelwood, Asaf Cidon, Sachin Katti
HPCA 2019 - February 16, 2019
Machine Learning at Facebook: Understanding Inference at the Edge
Carole-Jean Wu, David Brooks, Kevin Chen, Douglas Chen, Sy Choudhury, Marat Dukhan, Kim Hazelwood, Eldad Isaac, Yangqing Jia, Bill Jia, Tommer Leyvand, Hao Lu, Yang Lu, Lin Qiao, Brandon Reagen, Joe Spisak, Fei Sun, Andrew Tulloch, Peter Vajda, Xiaodong Wang, Yanghan Wang, Bram Wasti, Yiming Wu, Ran Xian, Sungjoo Yoo, Peizhao Zhang
ArXiv - November 24, 2018
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Khudia, James Law, Parth Malani, Andrey Malevich, Satish Nadathur, Juan Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy
EUROSYS 2018 - April 23, 2018
Reducing DRAM Footprint with NVM in Facebook
Assaf Eisenman, Darryl Gardner, Islam AbdelRahman, Jens Axboe, Siying Dong, Kim Hazelwood, Chris Petersen, Asaf Cidon, Sachin Katti
HPCA 2018 - February 24, 2018
Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective
Kim Hazelwood, Sarah Bird, David Brooks, Soumith Chintala, Utku Diril, Dmytro Dzhulgakov, Mohamed Fawzy, Bill Jia, Yangqing Jia, Aditya Kalro, James Law, Kevin Lee, Jason Lu, Pieter Noordhuis, Misha Smelyanskiy, Liang Xiong, Xiaodong Wang
Latest News
December 13, 2018