Grigori Fursin
My name is Grigori Fursin and I am an active open science advocate, reproducibility champion and open source contributor since 2007! I am also a computer scientist, R&D director, system architect, software engineer, educator and life-long learner with 15+ years of professional experience. I have an interdisciplinary background in computer systems, compilers, machine learning, physics and electronics. I also hold a PhD degree in computer engineering from the University of Edinburgh and I am a founder of cTuning.org (non-profit open science organization, 2014+) and cKnowledge.org (open science company, 2019+).
My passion is to help researchers, engineers and students understand the SOTA AI, ML and Systems R&D and learn how to use it in the real world across rapidly evolving AI/ML models, data sets, software and hardware from different vendors - please see my ACM TechTalk and white paper to learn more about my vision. That's why I am glad to lead community developments of open-source tools, automation frameworks and platforms to fix the software/hardware mess, modularize complex AI systems, make them easier to use and automate their benchmarking, optimization and co-design to run AI, ML and other emerging workloads in the most efficient and cost-effective way in collaboration with MLCommons, ACM, IEEE and other organizations.
Here you can learn about my community initiatives, open-source tools, Collective Knowledge Playground, Collective Mind workflow automation framework and portable, reusable and technology-agnostic automation recipes (CM4MLOps, CM4MLPerf and CM4ABTF) to support open science, reproducible research and artifact evaluation.
I am very glad and proud that my technology is trusted by MLCommons (125+ AI companies), AVCC (the Autonomous Vehicle Computing Consortium), ACM, IEEE and other organizations to help researchers and engineers automate all their repetitive, tedious and time consuming R&D tasks: ACM TechTalk'21, keynote at ACM REP'23, ArXiv white paper'24, overview in Philosophical Transactions of the Royal Society'21 and my reproducibility initiatives at ML and Systems conferences since 2014.
I spend my spare time raising my kids, learning, reading, traveling, playing soccer and ping-pong,
supporting open-science initiatives, helping the community reproduce ML, AI and systems research and bring it to the real world,
giving guest lectures and supporting community projects that improve everyone's life.
My current activities:
- organizer of reproducibility initiatives and artifact evaluation for AI, ML and Systems conferences and MLPerf benchmarks in collaboration with ACM, IEEE and MLCommons since 2013. I am leading the development of a common interface and automation language to make it easier to rerun and reuse code, data and experiments from published papers - see my ACM Tech Talk'21, ACM REP'23 keynote and white paper'24 for more details.
- author and tech. lead of the Collective Mind automation framework (CM), reusable automation recipes (CM4MLOps scripts), and a unified interface for MLPerf benchmarks (CM4MLPerf) adopted by MLCommons (125+ AI organizations) and AVCC (the Autonomous Vehicle Computing Consortium) to modularize AI/ML systems, automate their benchmarking, optimization and co-design across diverse and rapidly evolving models, data sets, software and hardware from different vendors (Nvidia, Intel, Qualcomm, AMD ...) and make it easier to reproduce MLPerf results. I donated this open-source CM technology to MLCommons to benefit everyone and continue developing it as a community effort - check this white paper for more details. We are prototyping the next generation of CK & CM technology based on user feedback - please get in touch for more details!
- founder and architect of the Collective Knowledge Playground - an automation platform to support open science, reproducibility initiatives and collaborative benchmarking, optimization and co-design of software and hardware to run AI, ML and other emerging workloads in the most efficient and cost-effective way across diverse models, datasets, software and hardware (trading off performance, power consumption, accuracy, cost and other characteristics). Please check this ArXiv white paper, AVCC/MLCommons press-release, MLPerf press release (1), MLPerf press release (2) and get in touch to learn about our plans.
- founder of cKnowledge.org - a research, engineering and educational company to simplify deep-tech R&D, accelerate innovation, enable open science and help students, researchers and engineers learn and apply the SOTA techniques by automating and unifying all their repetitive, tedious and time consuming DevOps and MLOps when building, optimizing and deploying complex applications and systems.
- founder and co-chair of the MLCommons Task Force on Automation and Reproducibility to modularize and automate MLPerf benchmarks using my CM framework (white paper);
- vice president of MLOps at OctoML;
- founder and chief architect of cKnowledge.io acquired by OctoML;
- author of the Collective Knowledge technology (CK) powering cKnowledge.io;
- author of the Artifact Evaluation and Reproducibility checklist (Unified Artifact Appendix) for ACM/IEEE conferences (see example of my artifact appendix at the end of this ASPLOS'24 paper "PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation");
- co-founder of a CodeReef platform for universal MLOps;
- founder in residence at Enterpreneur First;
- co-director of the Intel Exascale Lab and tech.lead for performance analysis, optimization and co-design of high-performance and cost-effecitve computer systems;
- senior tenured scientist at INRIA;
- research associate at the University of Edinburgh;
- holder of the PhD in computer science from the University of Edinburgh with the Overseas Research Student Award (compilers, run-time systems and software/hardware co-design);
- recipient of the European technology transfer award, ACM CGO test of time award and INRIA award of scientific excellence for my original research to use AI, ML, federated learning and collective tuning (cTuning) to automate development of high-performance and cost-effective computer systems and reduce R&D costs and time to market by an order of magnitude.
You can find some more details in my timeline.