My timeline (may not be up-to-date):

My community service (collaboration with MLCommons, ACM, IEEE, HiPEAC and other organization)

2024: Co-organizer of the artifact evaluation at IEEE/ACM MICRO'24
2024: MLPerf liaison at the Student Cluster Competition at SuperComputing'24
2022-2024.: Founder of the MLCommons taskforce on automation and reproducibility - I donated my open-source Collective Mind technology to MLCommons to benefit everyone and continue developing it as a community effort to modularize and automate MLPerf benchmarks (see my ACM REP'23 keynote and white paper for more details).
2020-cur.: A founding member of MLCommons to accelerate ML and systems innovation.
2020-cur.: A founding member of the ACM SIG on reproducibility.
2019-cur.: An early member of the MLPerf.org.
2017-cur.: A founding member of the ACM taskforce on reproducibility.
2015-cur.: Author of the unified artifact appendix and reproducibility checklist now used and extended by many ACM and IEE conferences;
2015-cur.: co-organizer of many reproducible optimization tournaments and hackathons to co-design efficient AI and ML systems powered by my CK&CM technology;
2014-2016: co-author of the ACM artifact review and badging policy.
2014_cur.: helped to reproduce 150+ research papers from ML and systems conferences;
2014-cur.: Introduced reproducibility initiatives and artifact checklists at MLSys, ASPLOS, MICRO, CGO, PPoPP and other ML and systems conferences to validate results from published papers (see white paper and ACM TechTalk for more details);
2014-cur.: Founder and president of the cTuning foundation, France.
2008-cur.: Founder and the architect of cTuning.org.
1999-cur.: Evangelist of collaborative and reproducible research and experimentation in computer engineering.

Academic research (tenured research scientist at INRIA with PhD in CS from the University of Edinburgh)

I prepared the 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 to benefit everyone;
prepared and tought M.S. course at the Paris-Saclay University on using ML to co-design efficient software and hardare (self-optimizing computing systems);
gave 100+ invited talks about my R&D;
honored to receive the ACM CGO test of time award, several best papers awards, INRIA award of scientific excellence and EU HiPEAC technology transfer award.

Project management and open-source development

2024-cur: leading the development of the next generation of my Collective Mind and Collective Knowledge technology to help researchers, engineers and students co-design software and hardware for more efficient and cost-effective AI;
2023-2024: led the development of the Collective Knowledge playground to benchmark and optimize AI/ML Systems via reproducible optimization challenges and tournaments;
2022-2024: led the development of the Collective Mind automation framework (CM) to modularize AI/ML systems and make it easier to benchmark and optimize them across diverse and rapidly evolving models, data sets, software and hardware from different vendors - I donated CK and CM to MLCommons to benefit everyone and continue developing at as a community effort (see white paper);
2014-2019: developed the Collective Knowledge framework to automate and accelerate design space exploration of AI/ML/SW/HW stacks while balancing speed, accuracy, energy and costs;
2010-2011: led the development of the cTuning 2 automation framework to benchmark emerging workloads across diverse hardware at Intel Exascale Lab;
2007-2009: led the development of the ML-based compiler and the cTuning.org platform across 5 teams to automate and crowdsource optimization of computer systems - this technology is considered to be the first in the world;
2007-2009: led the development of the compiler plugin framework in collaboration with Google and Mozilla that was added to the mainline GCC powering all Linux-based computers and helped to convert production compilers into research toolsets for machine learning;
co-founded an engineering company (dividiti) and led it to $1M+ in revenue with Fortune 50 customers using my CK technology; donated CK technology to MLCommons in 2021;

Entrepreneurship

2019: founded and developed the cKnowledge.io platform acquired by OctoML.ai;
2019: was selected for the 2nd Enterprenuer First cohourt in Paris to learn how to create startups and avoid numerous pitfalls.

Professional Career

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 my CK/CM technology. Our technology was successfully validated by the community and MLCommons members by automating, unifying and reproducing more than 80% of all MLPerf inference benchmark submissions (and 98% of power results) with very diverse technology from Neural Magic, Qualcomm, DELL, HPE, Lenovo, Hugging Face, Nvidia, AMD, Intel and Apple across diverse CPUs, GPUs and DSPs with PyTorch, ONNX, QAIC, TF/TFLite, TVM and TensorRT using popular cloud providers (GCP, AWS, Azure) and individual servers and edge devices provided by our volunteers and contributors.
2021-2023: Vice President at OctoML.ai leading the development of the 2nd generation of my open-source CK workflow automation technology (aka Collective Mind) and connecting it was TVM. Our technology was adopted by MLCommons (125+ AI software and hardware companies) to modularize AI/ML Systems and automate their development, optimization and deployment from the cloud to the edge.
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 OctoML.ai).
2019: Founder in residence at Entrepreneur First learning how to build deep tech startups and MVPs from scratch while avoiding numerous pitfalls and minimizing all risks. This knowledge and experience helped me to meet many amazing people and create the cKnowledge.io platform acquired by OctoML.ai.
2015-2019: Co-founder and CTO of a commercial engineering company (dividiti) testing my CK framework in industry.
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 (performance autotuning and SW/HW co-design).
2014-2015: R&D project partner with Arm (EU H2020 TETRACOM project).
2012-2014: Tenured Research Scientist (associate professor) at INRIA.
2010-2011: Co-director of the Intel Exascale Lab (France) and a head of the software/hardware optimization and co-design group (on sabbatical).
2007-2010: Guest lecturer at the University of Paris-Sud.
2007-2010: Tenured Research Scientist (assistant professor) at INRIA.
1999-2006: Research Associate at the University of Edinburgh.

Awards

2017: ACM CGO test of time award for my research on ML-based self-optimizing compilers.
2016-cur.: Microsoft Azure Research award to support cKnowledge.org.
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 Ph.D.

Education

2004: PhD in computer science with the ORS award from the University of Edinburgh.
1999: MS in computer engineering with a golden medal (summa cum laude) from MIPT.
1997: BS in electronics, mathematics and machine learning (summa cum laude) from MIPT.

Main scientific contributions

Prepared the foundations, scientific methodology, and tools to automatically co-design software and hardware from different vendors that can run emerging workloads in the unified and most efficient way in terms of speed, accuracy, energy and other costs.

Professional memberships

ACM
IEEE
HiPEAC

Main software developments

2023-cur.: 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 - see white paper for more details.
I used Streamlit; my CM/CM4MLOps/CM4MLPerf/CM4ABTF automation; Python; MLPerf benchmarks .
2022-2024.: Prototyped the Collective Mind automation framework with CM4MLOps automation scripts, CM4MLPerf interface to run MLPerf benchmarks and CM4ABTF to run automotive benchmarks across diverse models, data sets, software and hardware from different vendors in a unified and automated way
I used Python; loadgen; Docker/Podman; HuggingFace/Transformers/PyTorch/ONNX/TF; Ubuntu/Windows/RHEL/MacOS; Nvidia/Intel/AMD/Qualcomm/Arm ; AWS/Azure/Scaleway .
2020-cur.: Developed a prototype of the cKnowledge.io to organize all knowledge about AI, ML, systems, and other innovative technology from my academic and industrial partners in the form of portable CK workflows, automation actions, and reusable artifacts. I use it to automate co-design and comparison of efficient AI/Ml/SW/HW stacks from data centers and supercomputers to mobile phones and edge devices in terms of speed, accuracy, energy, and various costs. I also use this platform to help organizations reproduce innovative AI, ML, and systems techniques from research papers and accelerate their adoption in production. I collaborate with MLPerf.org to automate and simplify ML&systems benchmarking and fair comparison based on the CK concept and DevOps/MLOps principles.
I used the following technologies: Linux/Windows/Android; Python/JavaScript/CK; apache2; flask/django; ElasticSearch; GitHub/GitLab/BitBucket; REST JSON API; Travis CI/AppVeyor CI; DevOps; CK-based knowledge graph database; TensorFlow; Azure/AWS/Google cloud/IBM cloud .
2018-cur.: Enhanced and stabilized all main CK components (software detection, package installation, benchmarking pipeline, autotuning, reproducible experiments, visualization) successfully used by dividiti to automate MLPerf benchmark submissions.
I used the following technologies: Linux/Windows/Android; CK/Python/JavaScript/C/C++; statistical analysis; MatPlotLib/numpy/pandas/jupyter notebooks; GCC/LLVM; TensorFlow/PyTorch; Main AI algorithms, models and data sets for image detection and object classification; Azure/AWS/Google cloud/IBM cloud; mobile phones/edge devices/servers; Nvidia GPU/EdgeTPU/x86/Arm architectures .
2017-2018: Developed CK workflows and live dashboards for the 1st open ACM REQUEST tournament to co-design Pareto-efficient SW/HW stacks for ML and AI in terms of speed, accuracy, energy, and costs. We later reused this CK functionality to automate MLPerf submissions.
I used the following technologies: CK; LLVM/GCC/iCC; ImageNet; MobileNets, ResNet-18, ResNet-50, Inception-v3, VGG16, SSD, and AlexNet; MXNet, TensorFlow, Caffe, Keras, Arm Compute Library, cuDNN, TVM, and NNVM; Xilinx Pynq-Z1 FPGA/Arm Cortex CPUs/Arm Mali GPGPUs (Linaro HiKey960 and T-Firefly RK3399)/a farm of Raspberry Pi devices/NVIDIA Jetson TX2/Intel Xeon servers in Amazon Web Services, Google Cloud and Microsoft Azure .
2017-2018: Developed an example of the autogenerated and reproducible paper with a Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques (collaboration with the Raspberry Pi foundation).
I used the following technologies: Linux/Windows; LLVM/GCC; CK; C/C++/Fortran; MILEPOST GCC code features/hardware counters; DNN (TensorFlow)/KNN/SVM/decision trees; PCA; statistical analysis; crowd-benchmarking; crowd-tuning .
2015-cur.: Developed the Collective Knowledge framework (CK) to help the community automate typical tasks in ML&systems R&D, provide a common format, APIs, and meta descriptions for shared research projects, enable portable workflows, and improve the reproducibility and reusability in computational research. We now use it to automate benchmarking, optimization and co-design of AI/ML/SW/HW stacks in terms of speed, accuracy, energy and other costs across diverse platforms from data centers to edge devices.
I used the following technologies: Linux/Windows/Android/Edge devices; Python/C/C++/Java; ICC/GCC/LLVM; JSON/REST API; DevOps; plugins; apache2; Azure cloud; client/server architecture; noSQL database (ElasticSearch); GitHub/GitLab/BitBucket; Travis CI/AppVeyor CI; main math libraries, DNN frameworks, models, and datasets .
2012-2014: Prototyped the Collective Mind framework - prequel to CK. I focused on web services but it turned out that my users wanted basic CLI-based framework. This feedback motivated me to develop a simple CLI-based CK framework.
2010-2011: Helped to create KDataSets (1000 data sets 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. This technology is considered to be the first in the world;
I used the following technologies: Linux; GCC; C/C++/Fortran/Prolog; semantic features/hardware counters; KNN/decision trees; PCA; statistical analysis; crowd-benchmarking; crowd-tuning; plugins; client/server architecture .
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 (collaboration with Google and Mozilla).
2008-cur.: Developed the cTuning.org portal to crowdsource training of ML-based MILEPOST compiler and automate SW/HW co-design similar to SETI@home. See press-releases from IBM and Fujitsu about my cTuning concept.
I used the following technologies: Linux/Windows; MediaWiki; MySQL; C/C++/Fortran/Java; MILEPOST GCC; PHP; apache2; client/server architecture; KNN/SVM/decision trees; plugins .
2009-2010: Created cBench (collaborative CPU benchmark to support autotuning R&D) and connected it with my cTuning infrastructure from the MILEPOST project.
2005-2009: Created MiDataSets - multiple datasets for MiBench (20+ datasets per benchmark; 400 in total) to support autotuning R&D.
1999-2004: Developed a collaborative infrastructure to autotune HPC workloads (Edinburgh Optimization Software) for the EU MHAOTEU project.
I used the following technologies: Linux/Windows; Java/C/C++/Fortran; Java-based GUI; client/server infrastructure with plugins to integrate autotuning/benchmarking tools and techniques from other partners .
1999-2001: Developed a polyhedral source-to-source compiler for memory hierarchy optimization in HPC used in the EU MHAOTEU project.
I used the following technologies: C++; GCC/SUIF/POLARIS .
1998-1999: Developed a web-based service to automate the submission and execution of tasks to supercomputers via Internet used in the Russian Academy of Sciences.
I used the following technologies: Linux/Windows; apache/IIS; MySQL; C/C++/Fortran/Visual Basic; MPI; Cray T3D .
1993-1998: Developed an analog semiconductor neural network accelerator (Hopfield architecture). My R&D tasks included the NN design, simulation, development of an electronic board connected with a PC to experiment with semiconductor NN, data set preparation, training, benchmarking, and optimization of this NN.
I used the following technologies: MS-DOS/Windows/Linux; C/C++/assembler for NN implementation; MPI for distributed training; PSpice for electronic circuit simulation; ADC, DAC, and LPT to measure semiconductor NN and communicate with a PC; Visual Basic to visualize experiments .
1991-1993: Developed and sold software to automate financial operations in SMEs.
I used the following technologies: MS-DOS; Turbo C/C++; assembler for printer/video drivers; my own library for Windows management .

My favorite story about Ernest Rutherford and Niels Bohr

Link