Grigori Fursin

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I am a computer scientist, system architect, research director, software engineer, educator, open source contributor, open science supporter, reproducibility champion and community leader with an interdisciplinary background in computer systems, compilers, machine learning, physics and electronics. I hold a PhD degree in computer engineering from the University of Edinburgh and I am a founder of (non-profit foundation, 2014+) and (research and engineering company, 2019+).

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 democratize AI research, development and education. I am very glad and proud that my technology is trusted by MLCommons (150+ AI companies), AVCC (the Autonomous Vehicle Computing Consortium), ACM/IEEE and other organizations to help researchers and engineers build and run AI, ML and other emerging workloads in the most efficient and cost-effective way across diverse and rapidly evolving models, datasets, software and hardware from different vendors.

You can learn more about my long-term vision, projects and community efforts to make AI accessible to everyone from the following presentations and papers: 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, traveling, reading, playing soccer, supporting open-science initiatives, helping the community reproduce ML, AI and systems research and bring it to the real world, giving invited talks and guest lectures about my technology and research, and supporting projects that improve everyone's life.

My current activities: My past activities:
  • vice president of MLOps at OctoML;
  • founder and chief architect of acquired by OctoML;
  • author of the Collective Knowledge technology (CK) powering;
  • co-founder of a CodeReef platform for universal MLOps;
  • founder in residence at Enterpreneur First;
  • co-director of 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;
  • PhD in computer science from the University of Edinburgh with the Overseas Research Student Award;
  • author of the Artifact Evaluation and Reproducibility checklist for ACM conferences;
  • 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.
My brief biography:

I am a British computer scientist, software engineer, research director, business executive and lifelong learner with interdisiplinary backround in computer engineering, compilers, machine learning, physics and electronics.

While pionnering the use of ML, AI, federated learning and collective tuning (cTuning) to automatically co-design software and hardware for high-performance and cost-effective computer systems in the past 15 years, I faced numerous issues to run, share and reproduce experiments across diverse and rapidly evolving models, data, software and hardware provided by collaborators and volunteers.

That tedious experience motivated me to establish the non-profit cTuning foundation in 2014 and for-profit cKnowledge Ltd in 2019 to develop open-source tools, invest into startups and sponsor reproducibility initiatives that help researchers and engineers automate their tedious and repetitive tasks, improve productivity, unleash creativity, accelerate innovation, reduce all R&D costs and make AI accessible to everyone.

During that time, I was very fortunate to collaborate with the community and many companies, startups, and non-profits including ACM, IEEE, IBM, Intel, General Motors, Arm, Amazon, Google, OctoML, INRIA, HiPEAC and MLCommons. I was also a tenured senior research scientist at INRIA, co-director of Intel Exascale Lab, architect of and VP of MLOps at OctoML with a PhD degree in computer science from the University of Edinburgh.

I spent many years working with the community to bootstrap the artifact evaluation and reproducibility initiatives at ACM/IEEE conferences and prepare an artifact appendix to describe experiments in a unified way that is now a must at most systems and AI conferences.

I also developed open-source automation and productivity tools and platforms including Collective Knowledge (CK) and Collective Mind (CM) that I donated to MLCommons in 2022 to benchmark and optimize AI systems across different models, data sets, software and hardware from different vendors while benefiting everyone.

I am very honored that my research and open-source tools received European technology transfer award, ACM CGO test of time award, INRIA award of scientific excellence and were adopted by MLCommons (150+ AI companies), the Autonomous Vehicle Computing Consortium (AVCC), ACM and IEEE.

I continue leading educational initiative and open-source projects to solve the growing complexity of software projects and AI systems; make it easier to prototype, validate and reproduce research ideas across rapidly evolving models, data, software, and hardware; automate the development of fast and efficient computer systems using AI; accelerate innovation and technology transfer; and make AI more ecological accessible to everyone.

At this moment, I focus on the development of the open-source Collective Mind workflow automation language and Collective Knowledge Playground with the community, MLCommons (150+ AI organizations), AVCC and ACM/IEEE to bridge the growing gap between AI research and production, facilitate reproducible research, and accelerate AI innovation. Our goal is to help everyone benchmark and optimize numerous AI systems, and validate them in the real world across diverse and rapidly evolving AI/ML models, data, software and hardware from the cloud to the edge.

In my spare time, I continue helping ML and Systems conferences organize and automate artifact evaluation and reproducibility initiatives at AI, ML and systems conferences. I also use my experience to help investors and startups avoid numerous pitfalls and reduce risks and costs when bringing complex ML/AI systems to production via my cKnowledge Ltd. Finally, I invest into a few stealth startups that should go live at some point in 2024.

When I have time and resources, I am glad to give invited talks and lectures, and help new projects and initiatives related to automating and simplifying development and deployment of efficient AI systems, supporting reproducible research, enabling open-science, and making AI more ecological and accessible to everyone.

I am very grateful to my fantastic colleagues and collaborators who helped me to develop Collective Knowledge framework (2014-2022) and Collective Mind technology (2022-cur.) to benefit everyone!

You can find more details about my R&D timeline, professional career and projects in the extended version of this page.