Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10827 publications
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For many practical applications of quantum computing, the slowest and most costly steps involve coherently accessing classical data. We help address this challenge by applying mass production techniques, which can sometimes allow us to perform operations many times in parallel for a cost that is comparable to a single execution[1-3]. We combine existing mass-production results with modern approaches for loading classical data using ``quantum read-only memory.'' We show that quantum mass production techniques offer no benefit when we consider a cost model that focuses purely on the number of non-Clifford gates. However, analyzing the constant factors in a more nuanced cost model, we find that it may be possible to obtain a reduction in cost of an order or magnitude or more for a variety reasonably-sized fault-tolerant quantum algorithms. We present several applications of quantum mass-production techniques beyond naive parallelization, including a strategy for reducing the cost of serial calls to the same data loading step.
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AI coding assistants are rapidly becoming integral to modern software development. A key challenge in this space is the continual need to migrate and modernize codebases in response to evolving software ecosystems. Traditionally, such migrations have relied on rule-based systems and human intervention. With the advent of powerful large language models (LLMs), AI-driven agentic frameworks offer a promising alternative—but their effectiveness remains underexplored. In this paper, we introduce FreshBrew, a novel benchmark for evaluating AI-based agentic frameworks on project-level Java migrations. We benchmark several such frameworks, powered by state-of-the-art LLMs, and compare their performance against established rule-based tools. Our evaluation of AI agents on this benchmark of 228 repositories shows that the top-performing model, Gemini 2.5 Flash, can successfully migrate 56.5% of projects to JDK 17. Our empirical analysis reveals novel insights into the critical strengths and limitations of current agentic approaches, offering actionable insights into their real-world applicability. By releasing FreshBrew publicly upon acceptance, we aim to facilitate rigorous, reproducible evaluation and catalyze progress in AI-driven codebase modernization.
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A Call to Action: Advancing the Conversation Around Neurodivergent Education-Employment Transitions
Dannie Lynn Fountain
Vicki Baker
Kevin Danley
Closing the Gap (2025)
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Neurodiversity is still largely stigmatized and excluded from DEIB frameworks and related organizational initiatives, despite the increased recognition regarding the benefits of neuroinclusion within the education and corporate spheres. We seek to address this knowledge-to-practice gap through the creation of the Neurodiversity Engagement Framework. By highlighting supports needed for neurodivergent individuals, and those that support them, the framework helps neurodivergent individuals navigate within and across higher education and industry contexts. Informed by an interdisciplinary review of literature from higher education, industry, and corporate leadership contexts, the Neurodiversity Engagement Framework brings to light prevailing challenges within practices and policies, serving as a guide for the creation of a more supportive foundation for neurodiverse individuals to thrive. In this manuscript, readers are encouraged to consider the myriad of impacts that neurodiversity has on higher education and industry experiences and the ways that organizations can be more proactive in their support of this growing population. To conclude, we offer a roadmap for future research and practice to further elucidate ways academic and corporation leaders and policymakers can effectively support neurodivergent individuals.
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Although sound information extraction appear distinct across spectrum of sound classes and technologies, all inherently involve creating some form of "embedding"—be it discrete as in textual tokens or continuous vectors—to encapsulate relevant information from the audio signal for downstream utilization. This unifying framework allows us to re-evaluate sound information extraction by researching the optimality of current task-specific representations, the quality headroom and the potential for a single, robust sound embedding to generalize across diverse applications and sound types. To expedite research in these directions, a standardized evaluation benchmark is indispensable, mirroring the established benchmarks in text and image domains. We present the Massive Sound Embedding Benchmark (MSEB) to serve this purpose. MSEB encompasses realistic tasks and datasets that reflect practical applications across diverse technologies and sound categories. Initial experimental findings indicate substantial headroom for enhancing prevalent information extraction methodologies. We encourage the sound processing community to contribute data and tasks to MSEB and employ it to assess their algorithms for improved overall sound encoding.
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Envisioning Aboriginal and Torres Strait Islander AI Futures
Journal of Global Indigeneity (2025)
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In January 2025, over forty Aboriginal and Torres Strait Islander researchers, practitioners, community members, and allies, gathered at the Centre for Global Indigenous Futures at the Wallumattagal Campus of Macquarie University in Sydney to envisage Aboriginal and Torres Strait Islander AI futures. This publication reports on attendees' vision for the future of AI for Aboriginal and Torres Strait Islander people.
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PageFlex: Flexible and Efficient User-space Delegation of Linux Paging Policies with eBPF
Kan Wu
Zhiyuan Guo
Suli Yang
Rajath Shashidhara
Wei Xu
Alex Snoeren
Kim Keeton
2025
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To increase platform memory efficiency, hyperscalers like Google and Meta transparently demote “cold” application data to cheaper cost-per-byte memory tiers like compressed memory and NVMe SSDs. These systems rely on standard kernel paging policies and mechanisms to maximize the achievable memory savings without hurting application performance. Although the literature promises better policies, implementing and deploying them within the Linux kernel
is challenging. Delegating policies and mechanisms to user space, through userfaultfd or library-based approaches, incurs overheads and may require modifying application code. We present PageFlex, a framework for delegating Linux paging policies to user space with minimal overhead and full compatibility with existing real-world deployments. PageFlex uses eBPF to delegate policy decisions while providing low-overhead access to in-kernel memory state and access information, thus balancing flexibility and performance. Additionally, PageFlex supports different paging strategies for distinct memory regions and application phases. We show that
PageFlex can delegate existing kernel-based policies with little (< 1%) application slowdown, effectively realizing the benefits of state-of-the-art policies like Hyperbolic caching and Leap prefetching, and unlocking application-specific benefits through region- and phase-aware policy specialization.
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Google’s Approach to Protecting Privacy in the Age of AI
Google, , 1600 Amphitheatre Parkway, Mountain View, CA, 94043 (2025)
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AI products introduce new privacy challenges. Finding the right privacy solution is central to developing innovative products, especially as AI models increasingly handle user data. In this paper, we propose a framework to reason about privacy in AI, and discuss how Privacy Enhancing Technologies (PETs) enable novel user experiences by reducing privacy risks in the AI development lifecycle. We argue that privacy protections are not inherently at odds with utility; in contrast, we discuss how building privacy into products from the start can create better, more trustworthy experiences for everyone.
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Invisible labor is work that is either not fully visible or not appropriately compensated. In open source software (OSS) ecosystems, essential tasks that do not involve code (like content moderation) often become invisible to the detriment of individuals and organizations. However, invisible labor is sufficiently difficult to measure that we do not know how much of OSS activities are invisible. Our study addresses this challenge, demonstrating that roughly half of OSS work is invisible. We do this by developing a cognitive anchoring survey technique that measures OSS developer self-assessments of labor visibility and attribution. Survey respondents (n=142) reported that their work is more likely to be invisible (2 in 3 tasks) than visible, and that half (50.1%) is uncompensated. Priming participants with the idea of visibility caused participants to think their work was more visible, and that visibility was less important, than those primed with invisibility. We also found evidence that tensions between attribution motivations probably increase how common invisible labor is. This suggests that advertising OSS activities as "open" may lead contributors to overestimate how visible their labor actually is. Our findings suggest benefits to working with varied stakeholders to make select, collectively valued activities visible, and increasing compensation in valued forms (like attribution, opportunities, or pay) when possible. This could improve fairness in software development while providing greater transparency into work designs that help organizations and communities achieve their goals.
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Perceptual Audio Coding: A 40-Year Historical Perspective
Juergen Herre
Schuyler Quackenbush
Minje Kim
2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2025)
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In the history of audio and acoustic signal processing perceptual audio coding has certainly excelled as a bright success story by its ubiquitous deployment in virtually all digital media devices, such as computers, tablets, mobile phones, set-top-boxes, and digital radios. From a technology perspective, perceptual audio coding has undergone tremendous development from the first very basic perceptually driven coders (including the popular mp3 format) to today’s full-blown integrated coding/rendering systems. This paper provides a historical overview of this research journey by pinpointing the pivotal development steps in the evolution of perceptual audio coding. Finally, it provides thoughts about future directions in this area.
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Databases in the Era of Memory-Centric Computing
Yannis Chronis
Anastasia Ailamaki
Lawrence Benson
Jana Gičeva
Eric Seldar
Lisa Wu Wills
2025
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The increasing disparity between processor core counts and memory bandwidth, coupled with the rising cost and underutilization of memory, introduces a performance and cost Memory Wall and presents a significant challenge to the scalability of database systems. We argue that current processor-centric designs are unsustainable, and we advocate for a shift towards memory-centric computing, where disaggregated memory pools enable cost-effective scaling and robust performance. Database systems are uniquely positioned to leverage memory-centric systems because of their intrinsic data-centric nature. We demonstrate how memory-centric database operations can be realized with current hardware, paving the way for more efficient and scalable data management in the cloud.
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Agents based on large language models (LLMs) for machine learning engineering (MLE) can automatically implement ML models via code generation. However, existing approaches to build such agents often rely heavily on inherent LLM knowledge and employ coarse exploration strategies that modify the entire code structure at once. This limits their ability to select effective task-specific models and perform deep exploration within specific components, such as experimenting extensively with feature engineering options. To overcome these, we propose MLE-STAR, a novel approach to build MLE agents. MLESTAR first leverages external knowledge by using a search engine to retrieve effective models from the web, forming an initial solution, then iteratively refines it by exploring various strategies targeting specific ML components. This exploration is guided by ablation studies analyzing the impact of individual code blocks. Furthermore, we introduce a novel ensembling method using an effective strategy suggested by MLE-STAR. Our experimental results show that MLE-STAR achieves medals in 64% of the Kaggle competitions on the MLE-bench Lite, significantly outperforming the best alternative.
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Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs
Bowen Tan
Zheng Xu
Eric Xing
Zhiting Hu
International Conference on Machine Learning (ICML) (2025)
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Synthetic data offers a promising path to train models while preserving data privacy. Differentially private (DP) finetuning of large language models (LLMs) as data generator is effective, but is impractical when computation resources are limited. Meanwhile, prompt-based methods such as private evolution depend heavily on the manual prompts, and ineffectively use private information in their iterative data selection process. To overcome these limitations, we propose CTCL (Data Synthesis with ConTrollability and CLustering), a novel framework for generating privacy-preserving synthetic data without extensive prompt engineering or billion-scale LLM finetuning. CTCL pretrains a lightweight 140M conditional generator and a clustering-based topic model on large-scale public data. To further adapt to the private domain, the generator is DP finetuned on private data for fine-grained textual information, while the topic model extracts a DP histogram representing distributional information. The DP generator then samples according to the DP histogram to synthesize a desired number of data examples. Evaluation across five diverse domains demonstrates the effectiveness of our framework, particularly in the strong privacy regime. Systematic ablation validates the design of each framework component and highlights the scalability of our approach.
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Performance of a Deep Learning Diabetic Retinopathy Algorithm in India
Arthur Brant
Xiang Yin
Lu Yang
Divleen Jeji
Sunny Virmani
Anchintha Meenu
Naresh Babu Kannan
Florence Thng
Lily Peng
Ramasamy Kim
JAMA Network Open (2025)
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Importance: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and diabetic macular edema (DME), to date, little published data exist on the clinical performance of these algorithms.
Objective: To evaluate the clinical performance of an automated retinal disease assessment (ARDA) algorithm in the postdeployment setting at Aravind Eye Hospital in India.
Design, Setting, and Participants: This cross-sectional analysis involved an approximate 1% sample of fundus photographs from patients screened using ARDA. Images were graded via adjudication by US ophthalmologists for DR and DME, and ARDA’s output was compared against the adjudicated grades at 45 sites in Southern India. Patients were randomly selected between January 1, 2019, and July 31, 2023.
Main Outcomes and Measures: Primary analyses were the sensitivity and specificity of ARDA for severe nonproliferative DR (NPDR) or proliferative DR (PDR). Secondary analyses focused on sensitivity and specificity for sight-threatening DR (STDR) (DME or severe NPDR or PDR).
Results: Among the 4537 patients with 4537 images with adjudicated grades, mean (SD) age was 55.2 (11.9) years and 2272 (50.1%) were male. Among the 3941 patients with gradable photographs, 683 (17.3%) had any DR, 146 (3.7%) had severe NPDR or PDR, 109 (2.8%) had PDR, and 398 (10.1%) had STDR. ARDA’s sensitivity and specificity for severe NPDR or PDR were 97.0% (95% CI, 92.6%-99.2%) and 96.4% (95% CI, 95.7%-97.0%), respectively. Positive predictive value (PPV) was 50.7% and negative predictive value (NPV) was 99.9%. The clinically important miss rate for severe NPDR or PDR was 0% (eg, some patients with severe NPDR or PDR were interpreted as having moderate DR and referred to clinic). ARDA’s sensitivity for STDR was 95.9% (95% CI, 93.0%-97.4%) and specificity was 94.9% (95% CI, 94.1%-95.7%); PPV and NPV were 67.9% and 99.5%, respectively.
Conclusions and Relevance: In this cross-sectional study investigating the clinical performance of ARDA, sensitivity and specificity for severe NPDR and PDR exceeded 96% and caught 100% of patients with severe NPDR and PDR for ophthalmology referral. This preliminary large-scale postmarketing report of the performance of ARDA after screening 600 000 patients in India underscores the importance of monitoring and publication an algorithm's clinical performance, consistent with recommendations by regulatory bodies.
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We study the allocation of indivisible goods under conflicting constraints, represented by a graph. In this framework, vertices correspond to goods and edges correspond to conflicts between a pair of goods. Each agent is allocated an independent set in the graph. In a recent work of [Kumar et al., 2024], it was shown that a maximal EF1 allocation exists for interval graphs and two agents with monotone valuations. We significantly extend this result by establishing that a maximal EF1 allocation exists for *any graph* when the two agents have monotone valuations. To compute such an allocation, we present a polynomial-time algorithm for additive valuations as well as a pseudo-polynomial time algorithm for monotone valuations. Moreover, we complement our findings by providing a counter example demonstrating a maximal EF1 allocation may not exist for three agents with monotone valuations. Additionally, we establish NP-hardness of determining the existence of such allocations for every fixed number n of agents.
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Avoid global outages by partitioning cloud applications to reduce blast radius
Karan Anand
https://cloud.google.com/ (2025)
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Cloud application development faces the inherent challenge of balancing rapid innovation with high availability. This blog post details how Google Workspace's Site Reliability Engineering team addresses this conflict by implementing vertical partitioning of serving stacks. By isolating application servers and storage into distinct partitions, the "blast radius" of code changes and updates is significantly reduced, minimizing the risk of global outages. This approach, which complements canary deployments, enhances service availability, provides flexibility for experimentation, and facilitates data localization. While challenges such as data model complexities and inter-service partition misalignment exist, the benefits of improved reliability and controlled deployments make partitioning a crucial strategy for maintaining robust cloud applications
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