Quick access to cM-generated Curriculum Vitae:
[ Institution building (I) ],
[ Keynotes (K) ],
[ Social activities (O) ],
[ Startups (C) ],
[ Examiner ],
[ Expert service (E) ],
[ Major research achievements (M) ],
[ Public or in-house repositories of knowledge (R) ],
[ Awards, prizes, and fellowships (A) ],
[ Major funding (F) ],
[ Professional experience (J) ],
[ Education (Z) ],
[ Major software and datasets (S) ],
[ Hardware (H) ],
[ Talks (T) ],
[ Participating in program committees and reviewing ],
[ Teaching and organizing courses (L) ],
[ Advising/collaborating (Q) ],
[ Organizing/chairing events (E) ],
[ Publications (P) ]
Grigori Fursin is a computer scientist, engineer and manager with an interdisciplinary background in computer engineering, electronics, physics, mathematics, and machine learning, and with more than 20 years of research and development experience. He is always interested to lead highly challenging, innovative and interdisciplinary research projects particularly related to a development of faster, smaller, cheaper, more power efficient and reliable self-tuning computer systems; collaborative discovery, systematization, sharing and reuse of knowledge; machine learning; big data analytics; artificial brain and collective intelligence.
In 1993, at the age of 16, Grigori joined his first official R&D project as a Research Assistant designing and optimizing semiconductor neural network accelerators for a possible brain-inspired supercomputer computer [M9, M8]. After tedious attempts to optimize and parallelize his neural network modeling software for several supercomputers, Grigori decided to switch to computer engineering to automate and systematize this process using his interdisciplinary knowledge. Eventually, Grigori was one of the first researchers to radically change ad-hoc, error prone, time consuming and costly process of designing, benchmarking and optimizing the next generation of computer systems across all software and hardware layers into a unified big data problem [P1,
P1, M2, P23, P14, P16, P5]. He then started tackling, systematizing and speeding it up using his open source plugin-based cTuning and Collective Mind multi-dimensional and multi-objective auto-tuning infrastructure and repository, statistical analysis, machine learning (classification and predictive analytics), data mining (finding missing features), run-time adaptation with static multi-versioning, adaptive exploration of large optimization spaces, online tuning, differential analysis, crowdsourcing using commodity mobile phones and collective intelligence.
Besides publishing in major conferences and journals including PLDI, MICRO, CGO, TACO, IJPP, CASES and HiPEAC, Grigori spends considerable effort to release all his code and data including tools, benchmarks, data sets and predictive models along with his articles at cTuning.org and c-mind.org/repo to ensure reproducibility. As a side effect, this approach initiated a new open publication model in computer engineering where experimental results and all research artifacts are continuously shared along with articles to be validated and improved by the community [P2, E1,E2,P2, M1,E9,P5].
Eventually, most of my techniques became a mainstream, have been included into mainline GCC, validated and extended by industry with IBM, ARC (Synopsys), Intel, Google, STMicroelectronics and ARM. These techniques enabled practical open-source machine learning based self-tuning compiler (MILEPOST GCC) [M3,R3,R1] considered by IBM to be the first in the world [P22]. They also dramatically reduced development costs and time to market of the new embedded reconfigurable devices from ARC (Synopsys) while improving their performance, power consumption, time, size and ROI [X8]. Finally, Grigori's techniques were included in the European Union's long-term IT research vision for 2012-2020 [X7, X2] In 2008, Grigori established international nonprofit cTuning.org foundation for community-driven systematization, automation and acceleration of the design, benchmarking and optimization of all existing computer systems across the whole software and hardware stack using his repository of knowledge, open source tools and big data analytics. My foundation and community-driven approach has been referenced in press-releases from IBM in 2008-2009 and from Fujitsu in 2014.
Grigori delivered more than 60 regular and invited talks, lectures and keynotes in the major international companies and universities in Europe, USA, China, Canada and Russia; founded SMART and ADAPT workshops that ran consecutively for 8 years sponsored by Google, NVidia, Intel and Microsoft; prepared and taught advanced MS course on future self-tuning computer systems in the Paris South University. In 2010-2011, he was on industrial leave invited to help establish Intel Exascale Lab in France while preparing long-term research directions and serving as the head of program optimization and characterization group [I1]. In 2012, Grigori rejoined INRIA and received a personal award and 4-year fellowship for "making an outstanding contribution to research" [A2]. Grigori hopes his research will help boost innovation in science and technology.