Leadership

David Kanter is a Founder and the Executive Director of MLCommons where he helps lead the MLPerf benchmarks and other initiatives. He has 16+ years of experience in semiconductors, computing, and machine learning. He founded a microprocessor and compiler startup, was an early employee at Aster Data Systems, and has consulted for industry leaders such as Intel, Nvidia, KLA, Applied Materials, Qualcomm, Microsoft and many others. David holds a Bachelor of Science degree with honors in Mathematics with a specialization in Computer Science, and a Bachelor of Arts with honors in Economics from the University of Chicago.

Peter Mattson leads ML Metrics at Google. He co-founded and is President of MLCommons, and co-founded and was General Chair of the MLPerf consortium that preceded it. Previously, he founded the Programming Systems and Applications Group at NVIDIA Research, was VP of software infrastructure for Stream Processors Inc (SPI), and was a managing engineer at Reservoir Labs. His research focuses on understanding machine learning models and data through quantitative metrics and analysis. Peter holds a PhD and MS from Stanford University and a BS from the University of Washington.

Vijay Janapa Reddi is an Associate Professor at Harvard University. Before joining Harvard, he was an Associate Professor at The University of Texas at Austin in the Department of Electrical and Computer Engineering. His research interests include computer architecture and runtime systems, specifically in the context of autonomous machines and mobile and edge computing systems. Dr. Janapa Reddi has received multiple honors and awards, including the National Academy of Engineering (NAE) Gilbreth Lecturer Honor and has been inducted into the MICRO and HPCA Halls of Fame. He received a Ph.D. in computer science from Harvard University, M.S. from the University of Colorado at Boulder and B.S from Santa Clara University.

Carole-Jean Wu is a Research Scientist at Facebook AI Research. Her research focus lies in the domain of computer system architecture with particular emphasis on energy- and memory-efficient systems. Her recent research has pivoted into designing systems for machine learning execution at-scale and tackling system challenges to enable efficient, responsible AI execution. Carole-Jean chairs the MLPerf Recommendation Benchmark Advisory Board and co-chaired MLPerf Inference. 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 holds tenure from ASU and received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell.

Christine Cheng is one of the engineering leads for deep learning benchmarking and optimization at Intel. Christine has been involved with shaping MLPerf benchmarks from the beginning and she also led teams at Intel to submit results to every MLPerf training and inference round. Before joining MLPerf/MLCommons, she worked as a data scientist in sports analytics. She received her M.S. from Stanford University and B.S from Caltech.

Ross Cunniff has several decades of experience in the computer industry, including extensive experience in compiler and graphics technology. He is a software engineering manager at NVIDIA, responsible for various performance-related objectives. He is also the chair of the SPEC/GWPG Graphics Performance Committee, which produces the SPECviewperf benchmark. He brings extensive experience in governance from his 16 years as an elected official in Fort Collins, Colorado. He received a B.S. in Mathematics and a B.S. in Computer Science from New Mexico State University.

Weifeng Zhang is the Chief Scientist of Heterogeneous Computing at Alibaba Cloud Infrastructure, responsible for performance optimization of large scale distributed applications at the data centers. Weifeng also leads the effort to build the acceleration platform for various ML workloads via heterogeneous resource pooling based on the compiler technology. Prior to joining Alibaba, Weifeng was a Director of Engineering at Qualcomm Inc, focusing on GPU compiler and performance optimizations. Weifeng received his B.Sc. from Wuhan University, China and PhD in Computer Science from University of California, San Diego.