Dr. Grigori Fursin: bio and CV

Understanding Complex Systems. Building Better Ones.

British citizen with permanent residency and full work authorization in France, based near Paris, and working internationally across Europe and North America, with frequent engagements in the UK and the US.

I draw on my interdisciplinary background in physics, computer engineering, machine learning, and full-stack software–hardware system design, plus 20+ years of impactful R&D experience across academia, open source, startups, and industry, to help AI labs, data-center teams, startups, enterprises, and investors de-risk emerging AI infrastructure and accelerator opportunities through first-principles systems thinking, cost-aware automation, hands-on prototyping, and reproducible experimentation and optimization.

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Grigori Fursin working on cost-aware computing Co-designing Hopfield neural network

Detailed bio

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I am an interdisciplinary scientist, computer systems researcher, systems architect, inventor, entrepreneur, educator, mentor, strategic advisor, long-time open-source contributor, and advocate for open science and reproducible R&D.

Driven by curiosity and a passion for understanding complex systems, I apply first-principles thinking, machine learning, automation, and software–hardware co-design to build efficient, self-optimizing systems and transform emerging ideas into practical technologies, products, methodologies, and communities with lasting impact.

I hold a PhD in self-optimizing compilers and systems from the University of Edinburgh, and my interdisciplinary background spans computer engineering with expertise in co-designing full software–hardware stacks for computing, AI, ML, and other emerging workloads from cloud to edge, as well as machine learning, AI systems, data analytics, workflow automation, knowledge management, physics, and electronics.

I am passionate about building innovative solutions to real-world problems and about unifying and automating R&D processes to enhance efficiency and reduce costs.

Fascinated by the prospects of AI and robotics, I began my R&D career in the mid-1990s as an undergraduate, taking on a technical leadership role to develop Hopfield-based analog semiconductor neural networks from scratch. This included complete development and automation of software, hardware, models, and datasets for training, inference, electronic simulation, and prototyping—since nothing existed at the time.

This project took much longer than I originally expected and revealed numerous issues in R&D methodologies, tools, and inefficiencies in computer engineering. As a result, I decided to switch to computer science and pursue PhD research to address these challenges. This interdisciplinary foundation and experience enabled me to pioneer and champion visionary uses of machine learning, AI, crowd-tuning, and crowd-learning to co-design more efficient, cost-effective, and scalable computer systems—including compilers, runtimes, software, and hardware—during my PhD at the University of Edinburgh and postdoctoral research at Inria.

I initiated and led R&D efforts that addressed the growing complexity of modern systems and served as a precursor to self-optimizing and agent-based systems, AutoML, workflow automation, agent-based optimization, federated learning, reproducible experimentation, and universal, efficient, technology-agnostic compute.

It also enabled me to initiate and support open science and reproducibility initiatives starting in 2008, when I launched cTuning.org (followed by cKnowledge.org with my Collective Knowledge technology, aka CK, in 2014) and released all my research code, data, models, and experiments for our ML-based self-optimizing compiler—considered the first of its kind (ACM TechTalk'21).

I was honored to receive two Test of Time Awards (ACM/IEEE CASES'25 and ACM CGO'17), multiple Best Paper Awards, the Inria Award for Scientific Excellence, and the EU HiPEAC Technology Transfer Award for this research and open-source tools.

After serving as a senior tenured research scientist at Inria, an adjunct professor at the University of Paris-Saclay, and co-director of the Intel Exascale Lab, I transitioned my research and open-source tools into industry.

I established the non-profit cTuning Foundation and co-founded a successful engineering company to automatically benchmark and optimize deep learning across diverse software and hardware stacks, with a focus on mobile phones and edge devices. I helped bootstrap it as CTO and Chief Architect, quickly growing it to multi-million revenue with just four people, thanks to my CK automation technology.

I then joined Entrepreneur First, a highly selective company-building program for scientists and technologists, where I learned to build lean startups and avoid common pitfalls. As a result, I founded and bootstrapped two startups in the fields of performance optimization, MLOps automation, and knowledge management—the latter of which was acquired by OctoAI, now part of NVIDIA.

During that time, I invented the Collective Mind automation language (CM/CMX), which was adopted by MLCommons—a consortium of over 100 AI and systems companies—to test and benchmark a wide range of AI models and datasets across diverse hardware and software platforms, from cloud to edge. My CM technology has automated thousands of MLPerf submissions and enabled the discovery of some of the most performance- and cost-efficient AI solutions using commodity-server configurations competitive with, and in selected cases outperforming, high-end systems. I am now developing the next generation of this automation.

At the same time, I remained actively involved in community service and open-source initiatives. I helped establish MLCommons and launch reproducibility efforts at ACM and IEEE conferences: cTuning.org/ae. I also introduced a unified Artifact Appendix, which has since been adopted and extended by major conferences such as ASPLOS, CGO, PPoPP, SuperComputing, and MICRO. Finally, I co-organized several successful Quantum Hackathons, including one at Ecole 42 in Paris, where we utilized my CK workflow automation and platform for collaborative benchmarking and optimization of Quantum workloads (Hackathon page and a list of my events).

Throughout my career, I’ve been honored to collaborate with and learn from brilliant minds across leading universities, non-profits, startups, and companies—including Google, Amazon, Meta, Arm, AMD, Intel, IBM, Qualcomm, NVIDIA, Raspberry Pi, OpenAI, Tesla, OctoAI, Neural Magic, Red Hat, Dell, HPE, Lenovo, Apple, Inria, ACM, IEEE, HiPEAC, MLCommons, and the Linux Foundation: Acknowledgments (1), Acknowledgments (2), and Acknowledgments (3).

My passion lies in applying my knowledge, experience, and tools to accelerate the journey from deep tech research to real-world production—while building intelligent, self-optimizing systems. I regularly support startups, enterprises, universities, non-profits, researchers, students, and investors in rapidly prototyping novel ideas, launching innovative deep-tech projects, reducing time to market, and delivering tangible impact through collaborative, reproducible, interdisciplinary, quantifiable, and automated R&D methodologies.

While I actively prototype full-stack projects and contribute hands-on, I bring the most value in roles such as strategic advisor, R&D lab director, complex systems architect, educator, and community leader. I use my experience to bridge interdisciplinary research, engineering, and product teams—helping them navigate complex, rapidly evolving technology landscapes, manage project complexity, avoid common pitfalls, and achieve meaningful outcomes efficiently, even with very limited resources and time.

In late 2025, I founded cTuning Labs and began prototyping the next generation of my Collective Knowledge platform, aimed at addressing the growing cost, complexity, and fragmentation of AI systems. My goal is to develop a universal compute automation layer that helps teams evaluate, deploy, benchmark, and optimize models across diverse hardware and software stacks while improving performance and reducing energy, deployment, and operational costs.

This initiative builds on my past work (white paper 1 and white paper 2), including Collective Mind, virtualized MLOps, agent-based R&D and MLPerf automations, the Collective Knowledge Playground, and reproducible optimization tournaments, as well as new R&D validated through public and private projects.

In my spare time, I enjoy spending time with my two children, reading, learning about complex systems and new skills, playing soccer (having competed semi-professionally), hiking, traveling, teaching, developing agent-based automations and platforms for collaborative and reproducible R&D, and brainstorming future deep-tech projects.



Curriculum Vitae

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Selected highlights
  • Interdisciplinary scientist, systems architect, entrepreneur, educator, and R&D leader working across physics, computer engineering, machine learning, AI infrastructure, software–hardware co-design, reproducible R&D, and open-source ecosystems.
  • Helped pioneer ML-based self-optimizing compilers and systems, collaborative benchmarking, artifact evaluation, reproducible R&D, workflow and agent-based R&D automation, and cloud-to-edge software–hardware co-design.
  • Founder, Chief Scientist, and Chief Architect of cTuning Labs; founder of cTuning.org, Collective Knowledge, Collective Mind, and the Collective Knowledge Playground.
  • Co-founder of MLCommons through the cTuning Foundation and founder of the MLCommons Task Force on Automation and Reproducibility.
  • Creator of the Collective Knowledge concept and platform, Collective Mind automation language, and MLPerf automations for reproducible experimentation, optimization, benchmarking, and full-stack software–hardware co-design of AI systems.
  • Top open-community MLPerf Inference submitter for several years and creator of automations that helped the community generate 10,000+ reproducible AI benchmark submissions and identify Pareto-efficient systems in terms of performance, cost, energy, and scalability across diverse cloud-to-edge CPUs, GPUs, and software stacks.
  • Creator of the Open MLPerf Dataset and FlexBoard at Hugging Face, an open dataset and interactive analysis tool for using AI, data analytics, and reproducible experimentation to improve AI system design, deployment, performance, cost, and energy efficiency.
  • Founder and Chief Architect of the Collective Knowledge / cKnowledge.io platform acquired by OctoAI, now part of NVIDIA.
  • Former Strategic Advisor to Lumai; Head of AI Systems R&D at FlexAI; VP of MLOps at OctoAI; Co-Director of the Intel Exascale Lab; Senior Research Scientist at Inria; and Adjunct Professor at Paris-Saclay University.
  • Author of the unified Artifact Appendix and reproducibility checklist adopted and extended by major ACM and IEEE systems conferences.
  • Recipient of two ACM/IEEE Test of Time Awards, multiple Best Paper Awards, Inria Scientific Excellence Award, and HiPEAC Technology Transfer Award.
  • Collaborated on R&D and technology-transfer projects involving Arm, General Motors, Amazon, Google, Qualcomm, Intel, IBM, MLCommons, ACM, and others.
Current focus and activities
  • cTuning Labs / cTuning.ai: building an independent R&D and advisory initiative focused on self-optimizing AI infrastructure, software–hardware co-design, benchmarking, simulation, digital twins, and cost-aware automation for emerging AI and computing systems.
  • Advisory for startups, enterprises, and investors: helping leadership teams evaluate emerging AI infrastructure, accelerator, data-center, and software–hardware co-design opportunities; identify integration barriers and technical risks; and accelerate the path from deep-tech ideas to working systems.
  • Next-generation CK/CMX automation: prototyping a universal compute automation layer and open datasets/dashboards to evaluate, deploy, benchmark, optimize, and reproduce AI/ML workloads across rapidly changing models, datasets, frameworks, runtimes, hardware, and cloud-to-edge environments.
  • Collective Knowledge Playground and reproducible optimization: developing educational and community platforms for reproducible optimization tournaments, reusable MLOps workflows, open benchmarking, and practical software–hardware co-design.
  • cTuning.org and reproducible R&D: continuing long-term open-science work with ACM, IEEE, MLCommons, and the systems community on artifact evaluation, reproducibility checklists, and reusable research workflows.
Professional career
  • 2025-cur.: Founder of cTuning Labs, enabling self-optimizing AI infrastructure, software–hardware co-design, R&D automation, benchmarking, simulation, digital twins, and efficient computing systems.
  • 2025-2026: Strategic Advisor to Lumai on AI infrastructure strategy, software–hardware co-design, benchmarking, simulation, and performance modelling for emerging optical AI acceleration technologies.
  • 2024-2025: Head of R&D Lab at FlexAI, coordinating efforts to leverage AI for co-designing more efficient and cost-effective AI systems — see this white paper for more details.
    Core technologies used: Hugging Face models and datasets, vLLM, PyTorch, Triton, TensorRT, Nsight, MLPerf, OpenSearch, MLCommons CMX, FastAPI, Docker, Bayesian search, reinforcement learning and LLMs, NVIDIA and AMD GPUs.
  • 2023-2024: Coordinator and developer of MLPerf automations at MLCommons, bootstrapping the development of Collective Mind automation recipes for MLOps, MLPerf, and the ABTF (Automotive Benchmarking Task Force).
  • 2023-cur.: Founder of the Collective Knowledge Playground, a free, open-source, and technology-agnostic platform for collaborative benchmarking, optimization, and comparison of AI and ML systems via open and reproducible challenges powered by CK/CM technology.
  • 2021-2023: Vice President at OctoAI (now part of NVIDIA), leading the development of the second generation of my open-source CK workflow automation technology, aka Collective Mind, and connecting it with TVM.
  • 2019-2021: Founder and developer of the cKnowledge.io platform to organize AI, ML, and systems knowledge and enable efficient computing based on FAIR principles; acquired by OctoAI, now part of NVIDIA.
  • 2019: Founder in Residence at Entrepreneur First, learning how to build deep-tech startups and MVPs from scratch while avoiding common pitfalls and minimizing risk.
  • 2015-2019: Co-founder and CTO of dividiti, a commercial engineering company based on my Collective Knowledge framework; led the company to $MM+ revenue with Fortune 50 customers.
  • 2016-2018: R&D project partner with General Motors (AI/ML/SW/HW co-design project).
  • 2017-2018: R&D project partner with the Raspberry Pi Foundation (crowd-tuning and machine learning).
  • 2015-2016: Subcontractor for Google on performance autotuning and software–hardware co-design.
  • 2014-2015: R&D project partner with Arm (EU H2020 TETRACOM project).
  • 2012-2014: Tenured Research Scientist (associate professor level) at Inria.
  • 2010-2011: Co-director of the Intel Exascale Lab (France) and head of the software–hardware optimization and co-design group while on sabbatical from Inria.
  • 2007-2010: Guest lecturer at the University of Paris-Sud / Paris-Saclay University.
  • 2007-2010: Tenured Research Scientist (assistant professor level) at Inria.
  • 1999-2006: Research Associate at the University of Edinburgh.
Awards and recognition
  • 2025: ACM/IEEE CASES Test of Time Award for our CASES'15 paper “Practical aggregation of semantical program properties for machine learning based optimization.”
  • 2017: ACM CGO Test of Time Award for my R&D on ML-based self-optimizing compilers.
  • 2016-cur.: Microsoft Azure Research Award to support cTuning.org and Collective Knowledge.
  • 2015: European Technology Transfer Award for my Collective Knowledge automation technology.
  • 2012: Inria Scientific Excellence Award and personal fellowship.
  • 2010: HiPEAC Award for PLDI paper.
  • 2009: HiPEAC Award for MICRO paper.
  • 2006: CGO Best Paper Award.
  • 2000: Overseas Research Student Award for my PhD.
Open-source platforms and software developments
Selected presentations and publications to understand my projects and long-term vision
Community service, reproducibility, and open science
Entrepreneurship and technology transfer
  • 2025-cur.: Founder of cTuning Labs.
  • 2024-2025: Founder of the Collective Knowledge Playground hosted by MLCommons.
  • 2020: Founded and developed the cKnowledge.io platform with virtual MLOps, acquired by OctoAI, now part of NVIDIA.
  • 2019: Prototyped the CodeReef platform with Nicolas Essayan.
  • 2019: Joined Entrepreneur First, a highly selective company-building program for scientists and technologists, where I learned to build lean startups and avoid common pitfalls.
  • 2015-2019: Co-founded and served as CTO of dividiti, commercializing CK-based benchmarking and optimization technologies for AI/ML systems across cloud-to-edge platforms.
Academic research and teaching
  • Prepared foundations to combine machine learning, autotuning, knowledge sharing, and federated learning to automate and accelerate the development of efficient software and hardware by several orders of magnitude (Google Scholar).
  • Developed Collective Knowledge and Collective Mind technology and started educational initiatives with ACM, IEEE, HiPEAC, Raspberry Pi Foundation, and MLCommons to bring my research and expertise to the real world.
  • Prepared and taught an M.S. course at Paris-Saclay University on using machine learning to co-design efficient software and hardware for self-optimizing computing systems.
  • Gave 100+ invited talks about my R&D.
  • Received two Test of Time Awards, several Best Paper Awards, Inria Award of Scientific Excellence, and EU HiPEAC Technology Transfer Award.
Main scientific contributions
  • Developed foundational methodologies and tools for the automatic co-design of software and hardware from diverse vendors, enabling efficient execution of emerging workloads with optimal speed, accuracy, energy, and cost by leveraging machine learning, crowd-tuning, and crowd-learning.
  • This work anticipated advances in AutoML, workflow automation, agent-based optimization, federated learning, reproducible experimentation, and cloud-to-edge AI systems optimization.
Education

I am passionate about lifelong learning and regularly take online courses, learn emerging tools, and test new technologies to acquire new skills or refresh existing knowledge: LinkedIn certifications.

  • 2019: Entrepreneur First, second cohort in Paris.
  • 2004: PhD in Computer Science with the Overseas Research Student Award, University of Edinburgh.
  • 1999: MS in Computer Engineering with a gold medal / summa cum laude, MIPT.
  • 1997: BS in Electronics, Mathematics, and Machine Learning, summa cum laude, MIPT.
Licenses and certifications
  • 2025: MCP: Build Rich-Context AI Apps with Anthropic (DeepLearning)
  • 2025: AI Agents and Agentic AI with Python & Generative AI (Coursera)
  • 2025: Foundations of Project Management (Coursera / Google)
  • 2024: Generative AI with Large Language Models (Coursera)
  • 2024: Efficiently Serving LLMs (DeepLearning)
  • 2024: Intro to Federated Learning (DeepLearning)
  • 2024: Quantization Fundamentals with Hugging Face (DeepLearning)
  • 2023: Learning How to Learn (Coursera)
  • 2021: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization (Coursera)
  • 2021: Structuring Machine Learning Projects (Coursera)
  • 2021: Neural Networks and Deep Learning (Coursera)
  • 2021: AI for Everyone (Coursera)
  • 2020: Machine Learning (Coursera)
Professional memberships
  • Founding Member, MLCommons
  • Reproducibility Champion, ACM
  • Member, IEEE Computer Society
  • Member, HiPEAC
Detailed software and project history
  • 2025-cur.: Developing a new workflow automation framework to co-design efficient and cost-effective AI systems from cloud to edge.
  • 2023-2025: Developed a prototype of the Collective Knowledge Playground to collaboratively benchmark and optimize AI, ML, and other emerging applications in an automated and reproducible way via open challenges.
  • 2022-2024: Prototyped the Collective Mind automation framework using virtual MLOps scripts and MLPerf automations to run MLPerf and other benchmarks and workloads in a unified and automated manner across diverse models, datasets, software, and hardware.
  • 2020-2022: Developed a prototype of cKnowledge.io to organize knowledge about AI, ML, systems, and other technologies in the form of portable CK workflows, automation actions, and reusable artifacts.
  • 2018-2025: Enhanced and stabilized main CK components, including software detection, package installation, benchmarking pipelines, autotuning, reproducible experiments, and visualization.
  • 2017-2018: Developed CK workflows and live dashboards for the first open ACM REQUEST tournament to co-design Pareto-efficient software–hardware stacks for ML and AI in terms of speed, accuracy, energy, and cost.
  • 2017-2018: Developed an example of an autogenerated and reproducible paper with a Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques in collaboration with the Raspberry Pi Foundation.
  • 2015-2025: Developed the Collective Knowledge framework (CK) to automate common tasks in ML and systems R&D, provide a common format and APIs for shared research projects, enable portable workflows, and improve reproducibility and reusability in computational research.
  • 2012-2014: Prototyped the Collective Mind framework, a prequel to CK.
  • 2010-2011: Helped create KDataSets (1000 datasets for CPU benchmarks) (PLDI paper, repo).
  • 2008-2010: Developed the machine-learning-based self-optimizing compiler connected with cTuning.org in collaboration with IBM, Arc / Synopsys, Inria, and the University of Edinburgh.
  • 2008-2009: Added the function cloning process to GCC to enable run-time adaptation for statically compiled programs (report).
  • 2008-2009: Developed the Interactive Compilation Interface, now available in mainline GCC, in collaboration with Google and Mozilla.
  • 2008-2013: Developed the cTuning.org portal to crowdsource training of the ML-based MILEPOST compiler and automate software–hardware co-design similar to SETI@home.
  • 2009-2010: Created cBench, a collaborative CPU benchmark to support autotuning R&D.
  • 2005-2009: Created MiDataSets, multiple datasets for MiBench to support autotuning R&D.
  • 1999-2004: Developed a collaborative infrastructure to autotune HPC workloads, Edinburgh Optimization Software, for the EU MHAOTEU project.
  • 1999-2001: Developed a polyhedral source-to-source compiler for memory hierarchy optimization in HPC used in the EU MHAOTEU project.
  • 1998-1999: Developed a web-based service to automate the submission and execution of tasks to supercomputers via the Internet, used in the Russian Academy of Sciences.
  • 1993-1998: Developed an analog semiconductor neural network accelerator based on Hopfield architecture, including design, simulation, data preparation, training, benchmarking, and optimization.
  • 1991-1993: Developed and sold software to automate financial operations in small and medium-sized enterprises.
My favorite story about Ernest Rutherford and Niels Bohr