About ACM Grace Murray Hopper Award

Prateek Mittal, Princeton University, is the recipient of the 2023 ACM Grace Murray Hopper Award for foundational contributions to safeguarding Internet privacy and security using a cross-layer approach.

The unifying theme in Mittal’s research is to leverage foundational techniques from network science, comprising graph-theoretical mechanics, data mining, and inferential modeling for tackling privacy and security challenges. For example, his research orchestrates and exploits graph-theoretic properties of the Internet topology for protecting privacy and detecting attacks. Moreover, Mittal applies these techniques in a manner that allows for complex interactions across traditional layers and boundaries of our networked systems, i.e., a cross-layer approach.

By conducting Internet-scale experiments with over 50,000 routers, Mittal’s research showed that an adversary can exploit the insecurity of internet routing to intercept traffic from trusted certificate authorities, and then allow an adversary to obtain a cryptographic key that is vouch safe by trusted authorities. To mitigate these attacks, Mittal helped develop the ingenuous idea of trusted certificate authorities validating 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. Taken together, his contributions are impacting the privacy and integrity of global commerce, financial services, online healthcare, and everyday communications.

Mohammad Alizadeh, Massachusetts Institute of Technology, is the recipient of the 2022 ACM Grace Murray Hopper Award for pioneering and impactful contributions to data center networks.

Alizadeh has fundamentally advanced how data centers communicate efficiently in transporting data. One of his key contributions is the control of data center network congestion and packet loss with a groundbreaking Data Center Transport Control Protocol (DCTCP). DCTCP significantly increases performance in data center environments where state-of-the-art TCP protocols fall short.

The theoretical foundation upon which DCTCP is built and the empirical analyses, novel algorithms, and explicit congestion notification techniques it leverages enable data packets to circumvent congestion while using significantly less buffer space. In essence, DCTCP changes the way that network endpoints process congestion signals obtained from within the network, enabling traffic bursts to be tolerated better and leading to reduced transport latency, higher data throughput, and greater network utilization.

Raluca Ada Popa, University of California, Berkeley, is the recipient of the 2021 ACM Grace Murray Hopper Award for the design of secure distributed systems. The systems protect confidentiality against attackers with full access to servers while maintaining full functionality.

Popa’s fundamental work of building secure systems focuses on protecting the confidentiality of data stored on remote servers. Cloud computing makes sensitive data more accessible to hackers and insiders, despite the common “faulty” assumption that parts of the server–say the databaseor operating system–are inaccessible and can be “trusted." Popa’s research provides confidentiality guarantees where servers only need to store encrypted data, processing it without decrypting. Thus, hackers see only encrypted data.

Computing on encrypted data, possible in theory, has been prohibitively inefficient in practice. Popa addresses this by replacing generality with building systems for a broad set of applications with common traits, and developing encryption schemes tailored to these application archetypes. In SQL databases, for example, Popa extracts a few primitive operations that support most queries, utilizes encryption schemes that efficiently support these primitives, and thus can perform most computations on encrypted databases.

Popa, as the senior researcher, has designed an astonishing number of prototype systems in different application domains, providing functionality over encrypted data. In Opaque, DORY, Metal, and CryptDB, she showed how the utilization of cryptographic schemes that efficiently support a few carefully identified primitive operations enables performant encrypted databases and file systems. The Helen and Senate prototypes she and her students contributed enable multiple organizations to collaboratively train a machine-learning model or perform data analytics over their combined encrypted data. In Delphi and MUSE, machine learning models execute on the client’s input, without revealing the data to the model provider or leaking the model to the client.

ACM named Shyamnath Gollakota, University of Washington, the recipient of the 2020 ACM Grace Murray Hopper Award for contributions to the use of wireless signals in creating novel applications, including battery-free communications, health monitoring, gesture recognition, and bio-based wireless sensing. His work has revolutionized and reimagined what can be done using wireless systems and has a feel of technologies depicted in science fiction novels.

Gollakota defined the technology referred to today as ambient backscatter—a mechanism by which an unpowered, battery-less device can harvest existing wireless signals (such as broadcast TV or WiFi) in the environment for energy and use it to transmit encoded data. In addition, he has developed techniques that can use sonar signals from smartphones to support numerous healthcare applications. Examples include detection and diagnosis of breathing anomalies such as apnea, detection of ear infections, and even detection of life-threatening opioid overdoses. These innovations have the potential to transform the way healthcare systems will be designed and delivered in the future, and some of these efforts are now being commercialized for real-world use.

Gollakota also opened up a new field of extremely lightweight mobile sensors and controllers attached to insects, demonstrating how wireless technology can stream video data from the backs of tiny insects. Some observers believe this could be a first step to creating an internet of biological things, in which insects are employed as delivery vehicles for mobile sensors.

ACM named Maria Florina “Nina” Balcan of Carnegie Mellon University the recipient of the 2019 ACM Grace Murray Hopper Award for foundational and breakthrough contributions to minimally-supervised learning. Balcan’s influential and pioneering work in machine learning has solved longstanding open problems, enabled entire lines of research crucial for modern AI systems, and has set the agenda for the field for years to come.

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.

“Nina Balcan wonderfully meets the criteria for the ACM Grace Murray Hopper Award, as many of her groundbreaking contributions occurred long before she turned 35,” said ACM President Cherri M. Pancake. “Although she is still in the early stages of her career, she has already established herself as the world leader in the theory of how AI systems can learn with limited supervision. More broadly, her work has realigned the foundations of machine learning, and consequently ushered in many new applications that have brought about leapfrog advances in this exciting area of artificial intelligence.”

Select Technical Contributions

Semi-supervised Learning
Semi-supervised learning is an approach to machine learning in which algorithms use large amounts of easily available unlabeled data to augment small amounts of labeled data to improve predictive accuracy. When semi-supervised learning was first explored, early research suggested some promising results. However, prior to Balcan’s work, there were no general principles for designing and providing formal guarantees for algorithms that leverage both labeled and unlabeled data. By introducing the first general theoretical framework, Balcan showed how to achieve provable guarantees on the performance of such techniques with concrete implications for many different types of semi-supervised learning methods. Her foundational principles for learning from limited supervision were instrumental in advancing this important tool in machine learning and supporting the subsequent work of many other researchers in this area.

Active Learning/Noise Tolerant Learning
Balcan also made significant contributions in the related area of active learning. In active learning, the algorithm processes large volumes of data and intelligently chooses the datapoints to be labeled. Balcan established performance guarantees for active learning that hold even in challenging cases when “noise” is present in the data. These guarantees hold under arbitrary forms of noise, that is, anything that distorts or corrupts the data. This can include anything from a blurry photo, a unit of data that is improperly labeled, meaningless information, or data that the algorithm cannot interpret. Building on this work, Balcan and her collaborators also developed algorithms that can learn more efficiently under more specialized forms of “label noise.” Examples of label noise might include a researcher not being given all of the health symptoms when annotating data to make predictions about a disease, or the data being encoded incorrectly. Her work in active learning in the presence of noise was regarded as a breakthrough in the field.

Clustering
Clustering is an unsupervised learning technique in which an algorithm groups datapoints with similar properties. One goal of clustering is to find meaningful structure in data. An early challenge in the field, however, was to establish a theoretical foundation for what constituted a “meaningful structure” in a dataset. In her early work, Balcan proposed a theoretical foundation for understanding the general kinds of structures that can be detected by clustering, as well as characterizing the functionality of specific clustering algorithms. As she developed her theoretical framework further, she also devised novel clustering algorithms that were derived from these theoretical foundations, and showed applications of these algorithms to computational biology and web search.

ACM named Constantinos Daskalakis and Michael J. Freedman recipients of 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. Daskalakis’s 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. Daskalakis’s 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 Freedman’s 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.

ACM named Amanda Randles recipient of the ACM Grace Murray Hopper Award for developing HARVEY, a massively parallel fluid dynamics simulation capable of modeling the full human arterial system at subcellular resolution and fostering discoveries that will serve as a basis for improving the diagnosis, prevention, and treatment of human diseases. A focus of Randles’s research has been in developing and applying high performance computing to biomedical problems. With HARVEY, she combined her knowledge of applied physics, computational methods and parallel computing to develop a physiologically accurate model of the movement of red blood cells throughout the body. The simulation mapped 500 billion fluid points using a supercomputer with 1.6 million cores (individual processors). HARVEY marked the first time a researcher had been able to effectively model the flow of blood at the cellular level. Randles is presently working with collaborators at the Dana Farber Cancer Institute and Harvard Medical School to extend the use of HARVEY to cancer biology and cardiovascular treatment planning. Randles’s cross-disciplinary approach has helped to bridge the gap between the computer and the clinic—translating computational results into actionable data physicians can use to improve patient outcomes.

Jeffrey Heer was honored for developing visualization languages that have changed the way people build and interact with charts and graphs across the Web. With the meteoric increase of data collection in recent years, tools are urgently needed to understand and see patterns within data. Jeffrey Heer, a Professor at the University of Washington, has been a leader in developing computer languages to create charts, graphs and other visualizations that help people explore and understand data. Heer’s earliest project, Prefuse, was developed in 2004 and became one of the first developer-friendly software packages for producing interactive visualizations. His later work with Mike Bostock on Protovis (2009) contributed a groundbreaking high-level language for Web-native data visualization. Heer then contributed to the development of Bostock’s subsequent D3.js (2011). D3.js quickly became the primary tool for creating interactive visualizations on the Web, and is used every day by thousands of Web developers worldwide. The Vega project, Heer's most recent contribution, builds on this earlier work to develop new representations that enable both human designers and automated algorithms to rapidly produce a wide range of interactive graphics. Data analytics firms and major media companies regularly publish visualizations created with these languages to engage millions of viewers. Importantly, Heer and his collaborators have made all of their visualization tools widely available as open source software.

 Martin Casado helped create the Software Defined Networking (SDN) movement, an approach that provides a software alternative to hardware-based network components. He introduced an open interface (OpenFlow) and open-source software components, which uncouple the network from its hardware. This level of abstraction creates virtual networks that are able to deliver the same features as physical networks, but with the operational flexibility of virtualization. These innovations, readily and widely adopted by industry, have spawned a burgeoning SDN research community with the potential to change the field. Casado was Co-founder and Chief Technology Officer of Nicira, which was acquired by VMware in 2012. He is currently the Chief Network Architect of VMware as well as a consulting assistant professor at Stanford University.

Dina Katabi initiated a new approach to network design using an explicit Control Protocol (XCP) that minimizes network congestion and maximizes utilization efficiency. Her research addressed a strategic technological problem of Internet growth, which requires extreme scalability and robustness.  She developed XCP, an algorithm to ensure fair allocation of capacity among different flows that compete for the same Internet bandwidth. Her scheme is the first protocol to achieve both goals simultaneously without imposing excessive per-flow overhead on Internet routers. The design separated the efficiency and fairness policies of congestion control, which delivered the highest possible application performance over a broad range of network infrastructure. Katabi is a professor at MIT and a member of its Computer Science and Artificial Intelligence Laboratory (CSAIL). She leads the NetworksMIT research group, and is director of WirelessMIT, the MIT center for wireless networks and mobile computing.

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