ACM Bestows Honors
Upon Computing Innovators
May 13, 2019
Recipients Made Contributions to Areas Including Artificial
Intelligence, Economics, Genome Research, Geo-Distributed Systems, and
ACM, the Association for Computing Machinery, named the recipients of
four prestigious technical awards. These leaders were selected by their
peers for making contributions that extend the boundaries of research,
advance industry, and lay the foundation for technologies that transform
society. The 2018 recipients will be formally honored at the ACM Awards
Banquet on June 15, 2019 in San Francisco.
Gerald C. Combs receives the ACM Software System Award for creating the
Wireshark network protocol analyzer, an essential tool for nearly anyone
who designs, deploys, analyzes and troubleshoots the wide range of
network protocols that tie the internet together, and for continued
leadership of the international Wireshark developer community.
Combs started Wireshark as an open
source project in 1997 under the name Ethereal. The software quickly
became the most commonly used system for visually analyzing network
protocol traffic. Before the advent of Ethereal and Wireshark, protocol
analyzers were expensive, dedicated pieces of hardware that were only
available to large institutions. The creation of an open source network
protocol analyzer democratized access to network protocol analysis. It
also enabled people to learn about network protocols, as they were able
to visualize the traffic on their own networks. In addition, Wireshark
has also had significant influence on the areas of network engineering
and cybersecurity. Engineers who work alongside security experts in
financial institutions and other high-profile businesses make extensive
use of Wireshark in their ongoing fight against cybercrime.
Combs, who serves as Director of Open Source Projects at Riverbed
Technology, has continued to work on the Wireshark code. He spent 20
years guiding the open source community that has developed around the
software and leading SharkFest, an annual educational conference focused
on sharing knowledge, experience and best practices among the Wireshark
developer and user communities.
The ACM Software System Award is presented to an institution or
individual(s) recognized for developing a software system that has had a
lasting influence, reflected in contributions to concepts, in commercial
acceptance, or both. The Software System Award carries a prize of
$35,000. Financial support for the Software System Award is provided by
Constantinos Daskalakis and Michael J. Freedman receive the ACM Grace
Murray Hopper Award.
Daskalakis, a professor at the
Massachusetts Institute of Technology, is recognized for his seminal
contributions to the theory of computation and economics, particularly
the complexity of Nash Equilibrium.
Strategic interaction greatly complicates behavior in socioeconomic
environments, from traditional markets and offline social networks to
modern technological systems such as online advertising platforms,
kidney exchanges, cryptocurrencies, sharing economy applications, and
online social networks. To analyze behavior in such strategic
environments, economists have long relied on concepts of equilibrium.
Daskalakisís work, with Goldberg and Papadimitriou, has challenged
equilibrium theory by showing that Nash equilibrium is computationally
intractable and thus unattainable, in general. His work has influenced
an ongoing reshaping of the study of strategic behavior, showing that
computation must play an essential role in the foundations of game
theory and economics. Daskalakisís more recent work has resolved
long-standing open problems in multi-dimensional mechanism design, and
advanced several other fields, including machine learning, probability
theory and statistics.
Freedman, a professor at Princeton University, is cited for the design
and deployment of self-organizing geo-distributed systems.
By introducing new algorithms and protocols, Freedman has shown how to
build scalable, performant, and autonomous distributed systems for
modern heterogeneous deployments and realistic workloads. Some of
Freedmanís most popular systems include CoralCDN, a content distribution
infrastructure that has been deployed at hundreds of network sites
worldwide and been used by millions of clients to share images, videos
and other content; the JetStream system, which employs an innovative
approach to data streaming analytics; and TimescaleDB, an open source
time series database that provides complex queries at scale on both
historical and fresh data. Additionally, in more fundamental research,
Freedman and colleagues have demonstrated that theoretically deep cloud
systems need not be slow or scale poorly.
The ACM Grace Murray Hopper Award is given to the outstanding young
computer professional of the year, selected on the basis of a single
recent major technical or service contribution. This award is
accompanied by a prize of $35,000. The candidate must have been 35 years
of age or less at the time the qualifying contribution was made.
Financial support for this award is provided by Microsoft.
Pavel Pevzner, a professor at the University of California San Diego,
receives the ACM Paris Kanellakis Theory and Practice Award for
pioneering contributions to the theory, design and implementation of
algorithms for string reconstruction and to their applications in the
assembly of genomes.
Pevznerís research interests span the
field of computational biology, and his work has been guided by
tailoring algorithmic ideas to biological problems. The life sciences
have been transformed by the ability to rapidly sequence and assemble
genomes for organisms from existing and extant species and use these
assembled genomes to answer fundamental and applied questions in
biology, medicine and other sciences. Pevzner has made fundamental
contributions to the theoretical study of string algorithms and to their
application to scalable reconstruction of genomes and other biological
sequences such as antibodies and antibiotics. Pevznerís algorithms
underlie almost all sequence assemblers used today and were used to
reconstruct the vast majority of genomic sequences available in
The ACM Paris Kanellakis Theory and Practice Award honors specific
theoretical accomplishments that have had a significant and demonstrable
effect on the practice of computing. This award is accompanied by a
prize of $10,000 and is endowed by contributions from the Kanellakis
family, with additional financial support provided by ACM's Special
Interest Groups on Algorithms and Computation Theory (SIGACT), Design
Automation (SIGDA), Management of Data (SIGMOD), and Programming
Languages (SIGPLAN), the ACM SIG Projects Fund, and individual
Henry Kautz receives the ACM - AAAI Allen Newell Award for contributions
to artificial intelligence and computational social science, including
fundamental results on the complexity of inference, planning and media
analytics for public health.
with his doctoral dissertation, Kautz, now a professor at the University
of Rochester, has studied how computers can infer the goals and plans of
people by studying their behavior. He has made a range of fundamental
contributions to theory and practice in knowledge representation and
reasoning, planning and plan recognition and computational social
science. Kautz was one of the pioneers in analyzing the computational
complexity of knowledge representation formalisms. He was also a
co-developer of the first randomized local search algorithms for Boolean
satisfiability testing, which have found practical application in
planning, graphical models, and software verification.
In the area of pervasive computing and social media analytics, his
trailblazing projects have included a system to help cognitively
disabled people find their way by inferring the transportation
destinations of selected groups of people; a project that uncovered the
central role of air travel in the spread of diseases by analyzing social
media data; and an initiative to improve the efficiency of restaurant
health inspections by combining social media reports of food poisoning
with location data.