Kim Hazelwood, Ph.D.

I am an engineering and applied research executive with deep technical expertise at the intersection of artificial intelligence and computer systems. Throughout my career, I’ve led technical strategy and execution for large-scale software and AI infrastructure programs serving billions of users—building high-performing teams, evangelizing emerging technologies, and driving organization-wide vision across applied AI.

I thrive on bringing cutting-edge research into production, optimizing complex AI platforms, and fostering collaborative engineering cultures. I am also an active communicator, speaker, and author passionate about technology's broader societal impact.

I hold a PhD in Computer Science from Harvard University and have authored over 70 peer-reviewed publications and a book. My research and leadership contributions have been recognized with the MIT Technology Review "TR35" (Top 35 Innovators Under 35), the ACM SIGPLAN Test of Time Award, the Anita Borg Early Career Award, and an NSF CAREER Award.

Honors & Awards

• Board of Directors, Computing Research Association, 2017-2025.

• ACM SIGPLAN Most Influential PLDI Paper Award (10-Year Test of Time Award), 2015.

• Promoted to Associate Professor (with tenure) effective August 25, 2011.

• Buy My Book on Amazon! Dynamic Binary Modification: Tools, Techniques, and Applications, 2011.

• Top 35 Innovators under 35, MIT Technology Review, 2010.

• NASA New Investigator Award, Virginia Space Grant Consortium, 2010.

• Anita Borg Early Career Award, Computing Research Association, 2009.

• Career Enhancement Fellowship, Woodrow Wilson National Fellowship Foundation, 2008.

• CAREER Award, National Science Foundation, 2007.

• FEST Distinguished Young Investigator Award, Fund for Excellence in Science and Technology, 2007.

Selected Publications

1. Sustainable AI: Environmental implications, challenges and opportunities. C-J Wu, R Raghavendra, et. al (last author). Proceedings of Machine Learning and Systems 4, 795-813. 2022. (760 citations)

2. MLPerf training benchmark. P Mattson, C Cheng, et al. Proceedings of Machine Learning and Systems 2, 336-349. 2020 (388 citations)

3. The architectural implications of Facebook's DNN-based personalized recommendation. U Gupta et al, 2020 IEEE International Symposium on High Performance Computer Architecture. 2020. (387 citations)

4. RecNMP: Accelerating personalized recommendation with near-memory processing. L Ke et al. ACM 47th Annual International Symposium on Computer Architecture. 2020 (300 citations)

5. Deep learning inference in facebook data centers: Characterization, performance optimizations and hardware implications. J Park et al., ArXiv:1811.09886 (238 citations)

6. Machine learning at Facebook: Understanding inference at the edge. CJ Wu, D Brooks, et al. 2019 IEEE international symposium on high performance computer architecture. (627 citations)

7. Applied machine learning at Facebook: A datacenter infrastructure perspective. K Hazelwood, et al. IEEE international symposium on high performance computer architecture 2018. (825 citations)

8. Profiling a warehouse-scale computer. S Kanev et al. Proceedings of the 42nd annual international symposium on computer architecture. 2015. (653 citations)

9. Where is the data? Why you cannot debate CPU vs. GPU performance without the answer. C Gregg, K Hazelwood. IEEE Int’l Symposium on Performance Analysis of Systems and Software. 2011. (432 citations)

10. Pin: Building customized program analysis tools with dynamic instrumentation. CK Luk, et al. ACM SIGPLAN Notices 40 (6), 190-200. 2005. (5800 citations)