The Association for Computing Machinery (ACM) has recognized four innovators for their contributions to internet privacy, operating system software, graph processing, and AI. Prateek Mittal from Princeton University received the ACM Grace Murray Hopper Award for his work on internet privacy and security. Andrew S. Tanenbaum from Vrije Universiteit was awarded for his work on the MINIX operating system. Guy E. Blelloch, Laxman Dhulipala, and Julian Shun were recognized for their work on large-scale graph processing. David Blei from Columbia University received the ACM – AAAI Allen Newell Award for his contributions to machine learning and statistics.
ACM Honors Innovators for Significant Contributions to Computing
The Association for Computing Machinery (ACM) has announced the recipients of four of its prestigious technical awards. The awards recognize the significant contributions of these innovators in various fields, including internet privacy, operating system software, graph processing, and artificial intelligence.
Internet Privacy and Security: Prateek Mittal
Prateek Mittal from Princeton University has been awarded the 2023 ACM Grace Murray Hopper Award for his foundational work in enhancing internet privacy and security. Mittal’s research focuses on using a cross-layer approach, leveraging techniques from network science, such as graph-theoretical mechanics, data mining, and inferential modeling, to address privacy and security challenges.
Mittal’s research has demonstrated that the internet’s topology can be exploited to protect privacy and detect attacks. His work has shown that an adversary can exploit the insecurity of internet routing to intercept traffic from trusted certificate authorities, leading to the acquisition of a cryptographic key. To counter these attacks, Mittal developed a method for trusted certificate authorities to validate website domain ownership from multiple vantage points on the Internet. This technology has already led to the secure issuance of over 2.5 billion digital certificates used by 350 million websites, significantly impacting the privacy and integrity of global commerce, financial services, online healthcare, and everyday communications.
Operating System Software: Andrew S. Tanenbaum
Andrew S. Tanenbaum from Vrije Universiteit has been awarded the ACM Software System Award for his work on MINIX, an operating system that has influenced the teaching of Operating Systems principles to multiple generations of students and contributed to the design of widely used operating systems, including Linux.
Tanenbaum created MINIX 1.0 in 1987 to accompany his textbook, “Operating Systems: Design and Implementation.” MINIX was a small microkernel-based UNIX operating system for the IBM PC. It became free open-source software in 2000. Beyond enabling the success of Tanenbaum’s textbook, the impact of MINIX has been phenomenal. It was an inspiration for LINUX, which has grown into the most successful open-source operating system powering cloud servers, mobile phones, and IoT devices.
Graph Processing: Guy E. Blelloch, Laxman Dhulipala, and Julian Shun
Guy E. Blelloch from Carnegie Mellon University, Laxman Dhulipala from the University of Maryland, and Julian Shun from the Massachusetts Institute of Technology have been awarded the ACM Paris Kanellakis Theory and Practice Award for their contributions to algorithm engineering. Their work on the Ligra, GBBS, and Aspen frameworks has revolutionized large-scale graph processing on shared-memory machines.
Starting in 2013, the trio began exploring how to analyze huge graphs on relatively inexpensive shared-memory multiprocessors. They built several frameworks that make it much easier for programmers to efficiently solve a wide variety of graph problems. Their work has demonstrated that shared-memory computers are an ideal platform for analyzing large graphs, overturning the predominant approach of using distributed systems such as Pregel, developed by Google.
Machine Learning and Statistics: David Blei
David Blei from Columbia University has been awarded the ACM – AAAI Allen Newell Award for his significant contributions to machine learning, information retrieval, and statistics. Blei’s work in the machine learning area of “topic modeling” has found applications throughout the social, physical, and biological sciences, in areas such as medicine, finance, political science, commerce, and the digital humanities.
Blei has also been a leader in variational inference (VI), an optimization-based methodology for approximate probabilistic inference. His major contribution to VI has been to develop a novel framework—stochastic variational inference (SVI)—that has significantly increased the size of problems that can be solved with VI. SVI is in wide use in the AI industry and across the sciences.
In his work on discrete choice modeling, Blei has developed a methodology for answering counterfactual queries about changes in prices, which helps to identify complimentary and substitutable pairs of products. This work has built a bridge between computer science and econometrics and has been cited for its impactful use of machine learning modeling.
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