December 1st, 2009 | Published in Google Research
I'm excited to share that the Association for Computing Machinery (ACM) has just announced that four Googlers have been elected ACM Fellows in its class of 2009. Jeff Dean, Tom Dean, Urs Hoelzle and Yossi Matias were chosen for their achievements in computer science and information technology and for their significant contributions to the mission of the ACM. Here at Google, we take great pride in having a tremendously talented workforce, and the talent of our team is exemplified by the addition of Jeff, Tom, Urs and Yossi to the six other ACM Fellows already at Google.
All of these Googlers are being recognized for successes both inside and outside Google. Urs' and Jeff's achievements are most directly related to innovations made while at Google, specifically in our large data centers and in harnessing their inherent parallel computation and vast storage. Tom and Yossi, on the other hand, were elected more for work done prior to Google — respectively, on how to use prediction in planning, control, and decision-making where there is both uncertainty and time constraints, and on theoretically and practically interesting techniques for analyzing and managing large data sets and data streams.
We at Google congratulate our colleagues. They serve as an inspiration to us and to our colleagues in computer science globally and remind us to continue to push the limits of computing, which has enormous benefits to our field and to society at large.
You can read more about Jeff, Tom, Urs and Yossi's achievements and the reasons for this recognition by the ACM below. The citations are the official ones from the ACM.
Jeff Dean, Google Fellow
For contributions to the science and engineering of large-scale distributed computer systems
Dr. Jeff Dean has made great contributions to the programming and use of loosely-coupled multiprocessing systems and cloud computing. Jeff is probably best known for his work (with Sanjay Ghemawat) on the parallel/distribution computing infrastructure called MapReduce, a tremendously influential programming model for batch jobs on loosely coupled multiprocessing systems. Working with others, Jeff has also been a leading contributor to many other Google systems: the BigTable record storage system, which reliably stores diverse record data records (via portioning and replication) in vast quantities, at least two production real-time indexing systems, and several versions of Google's web serving system. The breadth of Jeff's work is quite amazing: At Digital, he co-developed a leading Java compiler and the Continuous Profiling Infrastructure (DCPI). Beyond this core systems work, Jeff has had exceedingly diverse additional activities; for example, he co-designed Google's first ads serving system, made significant quality improvements to the search system, and even has been involved in user-visible efforts such as the first production version of Google News and the production implementation of Google's machine translation system. Despite his primary accomplishments as a designer and implementer of innovative systems that solve hard problems in a practical way, Jeff also has over 20 publications in peer-reviewed publications, more than 25 patents, and is one of Google's most sought-after public speakers.
Thomas L. Dean, Staff Research Scientist
For the development of dynamic Bayes networks and anytime algorithms
Dr. Tom Dean is known in AI for his work on the role of prediction in planning, control and decision-making where uncertainty and the limited time available for deliberation complicate the problem, particularly his work on temporal graphical models and their application in solving robotics and decision-support problems. His temporal Bayesian networks, later called dynamic Bayes networks, made it possible to factor very large state spaces and their corresponding transition probabilities into compact representations, using the tools and theory of graphical models. He was the first to apply factored Markov decision processes to robotics and, in particular, to the problem of simultaneous localization and map building (SLAM). Faced with the need to solve what were essentially intractable problems in real-time, Dean coined the name "anytime algorithm" to describe a class of approximate inference algorithms and the associated (meta) decision problem of deliberation scheduling to address the challenges of bounded-time decision making. These have been applied to large-scale problems at NASA, Honeywell, and elsewhere. At Google, Tom has worked on extracting stable spatiotemporal features from video and developed new, improved features for video understanding, categorization and ranking. During his twenty-year career as a professor at Brown University, he published four books and over 100 technical articles, while serving terms as department chair, acting vice president for computing and information services, and deputy provost.
Urs Hoelzle, Senior Vice President of Engineering
For the design, engineering and operation of large scale cloud computing systems
Dr. Urs Hoelzle has made significant contributions to the literature, theory, and practice in many areas of computer science. His publications are found in areas such as compilers, software and hardware architecture, dynamic dispatch in processing systems, software engineering and garbage collection. Much of this work took place during his time at Stanford and later at UC Santa Barbara as a member of the faculty. Urs' most significant contribution to computer science and its application is found in his work and leadership at Google. Since 1999 he has had responsibility for leading engineering and operations of one of the largest systems of data centers and networks on the planet. That it has been able to scale up to meet the demands of more than a billion users during the past 10 years is an indication of his leadership ability and remarkable design talent. Urs works best in collaborative environments, as evidenced by his publications and in his work at Google. While it would be incorrect to credit Urs alone for the success of the Google computing and communications infrastructure, his ability to lead a large number of contributors to a coherent and scalable result is strong evidence of his qualification for advancement to ACM Fellow. The philosophy behind Google's system of clustered, distributed computing systems reflects a powerful pragmatic: assume things will break; use replication, not gold-plating, for resilience; reduce power requirements where ever possible; create general platforms that can be harnessed in myriad ways; eschew specialization except where vitally necessary (e.g., no commercial products fit the requirement). Much of this perspective can be attributed to Urs Hoelzle.
Yossi Matias, Director of R&D Center in Israel
For contributions to the analysis of large data sets and data streams
Dr. Yossi Matias has made significant contributions to the analysis of large data sets and data streams. He pioneered (with Phillip Gibbons) a new research direction into the study of small-space (probabilistic) “synopses” of large data sets, motivating their study and making key contributions in this area. Yossi’s 1996 paper (with Noga Alon and Mario Szegedy) won the 2005 Gödel Prize, the top ACM prize in Theoretical Computer Science, awarded annually. The award citation describes the paper as having “laid the foundations of the analysis of data streams using limited memory." Further, “It demonstrated the design of small randomized linear projections, subsequently referred to as ‘sketches,’ that summarize large amounts of data and allow quantities of interest to be approximated to user-specified precision.” Additionally, Yossi has made several key contributions to lossless data compression of large data sets, including a “flexible parsing” technique that improves upon the Lempel-Ziv dictionary-based compression algorithm, and novel compression schemes for images and for network packets. Large scale data analysis requires effective use of multi-core processors. For example, his JACM paper (with Guy Blelloch and Phillip Gibbons) provided the first provably memory- and cache-efficient thread scheduler for fine-grained parallelism. In addition to his academic and scientific impact, Yossi has been heavily involved in the high tech industry and in technology and product development, pushing the commercial frontiers for analyzing large data sets and data streams. He is also the inventor on 23 U.S. patents. Yossi joined Google in 2006 to establish the Tel-Aviv R&D Center, and to be responsible for its strategy and operation. Yossi has overall responsibility for Google R&D and technology innovation in Israel.