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 is a senior staff engineer at Google. He founded and is President of MLCommons, and 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.

Ritika Borkar is a Senior Deep Learning Architect at NVIDIA focusing on HW and SW optimizations for High Performance AI Computing on GPUs and datacenter systems. Previously, she worked on microarchitecture definition, ASIC design, and verification for IPs at Atmel and NVIDIA. Since MLPerf's inception in 2018, Ritika has influenced rules and processes for the training suite of benchmarks.  She holds a master's degree in Electrical Engineering from the University of Minnesota, and a bachelor's degree from the National Institute of Technology at Trichy in India.

Jon Khazam is EVP Strategy and Business Development at Graphcore, a leading startup enabling the next generation of machine intelligence. Prior to joining Graphcore, Jon was Corporate VP and General Manager of the Visual & Parallel Computing Group at Intel Corporation, responsible for the development of 3D graphics, GPU compute, video, and display technologies – integrated into Intel processors for PCs, IOT, and data centers. Jon has served on the boards of public and private/venture-funded companies with more than 13 years of combined board experience. Jon has an MBA from the Haas School of Business at U.C. Berkeley and a B.S.E.E. from Cornell University.

Peter Baldwin is the founder of Myrtle.ai, a company focused on hardware-software co-design for efficient machine learning inference and other core data-center workloads. Through Peter, Myrtle.ai was instrumental in setting up the speech transcription benchmark for MLPerf. Peter previously founded a software development company which wrote distributed data center software. He was also a Senior Technical Director at several multinational visual effects companies. Peter holds a PhD in Pure Mathematics from Cambridge University, UK.

Phil Brown is VP of Product at Graphcore, leading development of Graphcore’s Intelligence Processing Units (IPUs), large scale IPU-PODs, Software Development environments and Machine Learning Applications. Previously he has held a number of different roles at Graphcore including Director of Applications, leading development of Graphcore’s flagship AL/ML models, and Director of Field Engineering, which acts as the focal point for technical engagements with our customers. Prior to joining Graphcore, Phil worked for Cray Inc. leading their engagement with the weather forecasting and climate research customers worldwide and prior to that as a technical architect. Phil holds a PhD in Computational Chemistry and a BSc in Chemistry from the University of Bristol.

James Goel leads the AI/ML standards group at Qualcomm where he is a Director of Technical Standards. He is on the board of the Video Electronics Standards Association (VESA) and is the chair of the MIPI Alliance Technical Steering Group. Previously, he was the VP of Engineering at Silicon Optix before it was acquired by Qualcomm in 2011. He holds a Bachelor’s of Applied Science (B.A.Sc) in Electrical Engineering from the University of Waterloo in Canada and is a licensed Professional Engineer (P. Eng.) in Ontario where he's practiced for the last 31 years.

Aditya Yanamandra is a Principal Engineer at Intel Corporation. He leads the Machine Learning Architecture team focused on DL algorithmic performance analysis, distributed systems and system level performance for Intel’s CPU and GPU architectures across data center and client markets. Prior to his current role, Aditya was technical lead in 3D graphics architecture definition and pre-silicon driver development. Aditya holds a PhD from The Pennsylvania State University and BTech from IIT Madras, India.

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.

Relja Markovic has an extensive career primarily in low-level graphics and performance work. He was formerly Engineering Director at Google working on the Stadia game streaming platform, and the Daydream immersive reality project. Previously he was CTO for Apps on Microsoft HoloLens, tech director on Microsoft Kinect and, among other things, development lead for the HLSL shader compiler. He has also done extensive optimization work on multiple Xbox 360 games including Mass Effect and Gears of War 2. His experience ranges from hands-on engineering to building and running 200+ person globally distributed teams. He current work is primarily in advisory roles helping startups scale.