New York, June 18, 2024 – ACM, the Association for Computing Machinery, today announced the winners of four technical awards.
They are:
David Blei, Columbia University, awarded the ACM – AAAI Allen Newell Award.
Blei is honored for major contributions to machine learning, information retrieval, and statistics. His landmark achievement is in “topic modeling” machine learning, which he introduced in the seminal paper Latent Dirichlet Allocation (LDA). Applications of topic modeling span the social, physical, and biological sciences, including medicine, finance, political science, commerce, and digital humanities.
Blei has also led in variational inference (VI), a research area bridging computer science and statistics. VI is an optimization-based method for approximate probabilistic inference. Blei’s key contribution to VI is the development of a new framework—stochastic variational inference (SVI)—which significantly expanded the size of problems solvable with VI. SVI is widely used in the AI industry and across various sciences.
Additionally, Blei’s work on discrete choice modeling developed a method for answering counterfactual questions about price changes, identifying pairs of complementary and substitutable products. This work has bridged computer science and econometrics and is influential in machine learning modeling.
The ACM – AAAI Allen Newell Award is given for career contributions that are broad within computer science or bridge computer science and other disciplines. The Newell Prize includes a $10,000 award provided by ACM and the Association for the Advancement of Artificial Intelligence (AAAI) and personal contributions.
Guy E. Blelloch, Carnegie Mellon University; Laxman Dhulipala, University of Maryland; and Julian Shun, Massachusetts Institute of Technology, awarded the ACM Paris Kanellakis Award for Theory and Practice for contributions to algorithm engineering, including the Ligra, GBBS, and Aspen frameworks, revolutionizing large-scale graph processing on shared-memory machines.
Starting in 2013, Blelloch, Dhulipala, and Shun explored analyzing large graphs (billions of vertices and hundreds of billions of edges) on relatively inexpensive shared-memory multiprocessors. They created several frameworks (Ligra, Ligra+, Julienne, GBBS, and Aspen) that make it much easier for programmers to efficiently solve various graph problems. They achieved many remarkable results where their provably efficient algorithms on free multi-core shared-memory machines outperformed previous algorithms, even those running on much larger and more expensive systems. Examples include clustering, clique counting, and various forms of connectivity. These ideas and applications are used in industry to solve real-world problems and significantly impact research.
An important outcome of this work was demonstrating that shared-memory computers are ideal for analyzing large graphs. When Ligra was first developed, the dominant approach for large graph analysis was distributed systems like Pregel (developed by Google). This shifted when, for many large real-world graph problems, the Ligra approach proved more efficient regarding energy, cost, and end-to-end runtime.
Their graph processing work also allows algorithms with performance guarantees in the PRAM model to achieve their theoretical performance in practice. Recently, nominees addressed the emerging deployment of streaming graph processing, modeling real-time graph changes, and developed Aspen, a new streaming graph system using purely functional data structures for low-latency updates and snapshots on massive graph datasets.
The ACM Paris Kanellakis Theory and Practice Award honors theoretical achievements with significant and demonstrable effects on the practice of computing. This award includes a $10,000 prize, funded by contributions from the Kanellakis family and additional support from ACM Special Interest Groups.
Prateek Mittal, Princeton University, awarded the 2023 ACM Grace Murray Hopper Award for fundamental contributions to preserving Internet privacy and security using a cross-layer approach. Mittal’s research leverages techniques from network science, including graph theoretic mechanics, data mining, and inferential modeling, to address privacy and security challenges.
Mittal’s research showed that an adversary could exploit Internet routing insecurity to intercept traffic from trusted certificate authorities, enabling an adversary to obtain a cryptographic key from trusted authorities. To mitigate these attacks, Mittal helped develop the idea of trusted certificate authorities authenticating website domain ownership from multiple Internet vantage points. This technology has led to the secure issuance of over 2.5 billion digital certificates used by 350 million websites. His contributions impact the privacy and integrity of global commerce, financial services, online healthcare, and everyday communications.
The ACM Grace Murray Hopper Award is presented to the outstanding young computer professional of the year based on a recent technical or service contribution. The award includes a $35,000 prize, supported by Microsoft.
Andrew S. Tanenbaum, Vrije Universiteit, awarded the ACM Software System Award for MINIX, which influenced the teaching of operating system principles to 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, popular at the time. It became free and open source software in 2000.
MINIX inspired LINUX, the most successful open source operating system powering cloud servers, mobile phones, and IoT devices. MINIX was also the basis for the MeikOS operating system for Meikotransputer-based computers and runs inside popular microchips. MINIX 3.0 is designed for resource-constrained and embedded computers requiring high reliability. Tanenbaum’s advocacy of microkernel design has influenced generations of operating system designers.
The ACM Software System Award recognizes an institution or individual(s) for developing a software system with a lasting impact, reflected in contributions to concepts, commercial acceptance, or both. The award includes a $35,000 prize, supported by IBM.
ACM, the Association for Computing Machinery, is the world’s largest educational and scientific computing society, bringing together educators, researchers, and computing professionals to inspire dialogue, share resources, and address the field’s challenges. ACM strengthens the collective voice of the computing profession through leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports its members’ professional growth by providing opportunities for lifelong learning, career development, and professional networking.