Grigori Fursin
Here you can learn more about my educational initiatives, open-source tools, industrial projects
and startups to help researchers and engineers automate their repetitive and tedious tasks,
improve their productivity, unleash creativity,
accelerate innovation, reduce all R&D costs, and make AI accessible to everyone in collaboration with MLCommons,
IBM, Intel, General Motors, Arm, Google, Amazon, OctoML, HiPEAC, ACM, IEEE
and many others:
ACM TechTalk'21,
keynote at ACM REP'23,
invited talk at MLPerf@HPCA'24,
overview in Philosophical Transactions of the Royal Society'21
and my reproducibility initiatives at ML and Systems conferences since 2014.
I enjoy spending my spare time traveling, reading, playing soccer, brainstorming crazy projects with my friends,
investing into open-source startups, supporting open-science initiatives,
helping the community automate, reproduce and validate cool deep tech experiments,
and raising my kids.
I am a British computer scientist, software engineer, business executive, angel investor,
educator, lifelong learner, and adventurer with 15+ years
of professional experience.
While pioneering 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 cKnowledge.io and VP of
MLOps at OctoML with a PhD degree in computer science
from the University of Edinburgh.
We managed to start
artifact evaluation and reproducibility initiatives
at ACM/IEEE conferences and prepare an artifact appendix to describe experiments in a unified way
that is a must at most systems and AI conferences nowadays.
We 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 this research and open-source tools received European technology transfer award, ACM CGO test of time award,
INRIA award of scientific excellence and was adopted by MLCommons (125+ AI companies).
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 and MLCommons (125+ AI organizations)
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 - you can reach me
via
Discord server or
email!
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.