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 10822 publications
mmMUSE: An mmWave-based Motion-resilient Universal Speech Enhancement System
Chenming He
Yanyong Zhang
Kai Wang
Dequan Wang
Lingyu Wang
the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), ACM (2026) (to appear)
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Voice-based smart systems can greatly enhance user experiences by allowing higher-quality interactions through better voice perception. Speech enhancement can benefit such systems by isolating noise from speech. Recently, integrating millimeter-wave (mmWave) with audio for speech perception has gained increasing attention due to microphones' limitations in noisy environments. However, mmWave-based vocal extraction is severely affected by motion, which disperses vocal signals across ranges and introduces distortions. In this paper, we propose an mmWave-based motion-resilient universal speech enhancement system called mmMUSE, which fuses mmWave and audio signals. To mitigate motion interference, we develop a Doppler-based method for motion-robust vocal signal extraction. Moreover, by introducing the Vocal-Noise-Ratio metric to assess the prominence of vocal signals from mmWave, we achieve real-time voice activity detection that gains 3.81 dB of SISDR in noisy speeches. Additionally, we design a two-stage complex-valued network that includes an attention-based fusion network for cross-modal complementing and a time-frequency masking network for correcting amplitude and phase of speech to isolate noises.
Using mmWave and audio datasets from 46 participants, mmMUSE outperforms the state-of-the-art speech enhancement models, achieving an average SISDR improvement of 3.12 dB. Additionally, mmMUSE achieves SISDR improvements of 16.51 dB, 17.93 dB, 14.93 dB, and 18.95 dB in controlled environments involving intense noise, extensive motion, multiple speakers, and various obstructive materials, respectively. Finally, we evaluate mmMUSE in real-world scenarios including running, public spaces, and driving, maintaining a word error rate (WER) below 10%.
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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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Beyond Digital Literacy: Building Youth Digital Resilience Through Existing “Information Sensibility” Practices
Mia Hassoun
Ian Beacock
Todd Carmody
Patrick Gage Kelley
Beth Goldberg
Devika Kumar
Laura Murray
Rebekah Park
Behzad Sarmadi
Social Sciences Journal, 14(4) (2025)
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Youth media consumption and disordered eating practices have historically been subjects of moral panics, often resulting in protective, deficit-based interventions like content removal. We argue for interventions which instead equip youth to evaluate and manage risks in their online environments, building upon their existing “information sensibility” practices. Drawing upon ethnographic research and intervention testing with 77 participants in the US and India, we analyze how youth (aged 13–26), including those with diverse political perspectives and those recovering from disordered eating (DE), engage with online news and health information. Participants generally algorithmically encountered (rather than searched for) information online, and their engagement was shaped more by social motivations—like belonging—than truth seeking. Participants interpreted online information collaboratively, relying on social cues and peer validation within their online communities. They demonstrated preference for personal testimonies and relatable sources, particularly those with similar social identities. We propose resilience-building interventions that build upon these youth online information practices by: (1) leveraging peer networks, promoting critical information engagement through collaborative learning and peer-to-peer support within online communities; (2) developing social media sensibility, equipping youth to critically evaluate information sources in situ; (3) providing pathways offline, connecting youth to desired in-person communities; and (4) encouraging probabilistic thinking.
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Quasiparticle-induced decoherence of a driven superconducting qubit
Mykola Kishmar
Pavel Kurilovich
Vlad Kurilovich
Thomas Connolly
Andrey Klots
Igor Aleiner
arXiv (2025)
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We develop a theory for two quasiparticle-induced decoherence mechanisms of a driven superconducting qubit. In the first mechanism, an existing quasiparticle (QP) tunnels across the qubit’s Josephson junction while simultaneously absorbing a qubit excitation and one (or several) photons from the drive. In the second mechanism, a qubit transition occurs during the non-linear absorption process converting multiple drive quanta into a pair of new QPs. Both mechanisms can remain significant in gap engineered qubits whose coherence is insensitive to QPs without the drive. Our theory establishes a fundamental limitation on fidelity of the microwave qubit operations—such as readout and gates—stemming from QPs.
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Security Signals: Making Web Security Posture Measurable At Scale
David Dworken
Artur Janc
Santiago (Sal) Díaz
Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb)
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The area of security measurability is gaining increased attention, with a wide range of organizations calling for the development of scalable approaches for assessing the security of software systems and infrastructure. In this paper, we present our experience developing Security Signals, a comprehensive system providing security measurability for web services, deployed in a complex application ecosystem of thousands of web services handling traffic from billions of users. The system collects security-relevant information from production HTTP traffic at the reverse proxy layer, utilizing novel concepts such as synthetic signals augmented with additional risk information to provide a holistic view of the security posture of individual services and the broader application ecosystem. This approach to measurability has enabled large-scale security improvements to our services, including prioritized rollouts of security enhancements and the implementation of automated regression monitoring. Furthermore, it has proven valuable for security research and prioritization of defensive work. Security Signals addresses shortcomings of prior web measurability proposals by tracking a comprehensive set of security properties relevant to web applications, and by extracting insights from collected data for use by both security experts and non-experts. We believe the lessons learned from the implementation and use of Security Signals offer valuable insights for practitioners responsible for web service security, potentially inspiring new approaches to web security measurability.
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We propose moving from Software Defined Networks (SDN) to Software Managed Networks (SMN) where all information for managing the life cycle of a network (from deployment to operations to upgrades), across all layers (from Layer 1 through 7) is stored in a central repository. Crucially, a SMN also has a generalized control plane that, unlike SDN, controls all aspects of the cloud including traffic management (e.g., capacity planning) and reliability (e.g., incident routing) at both short (minutes) and large (years) time scales. Just as SDN allows better routing, a SMN improves visibility and enables cross-layer optimizations for faster response to failures and better network planning and operations. Implemented naively, SMN for planetary scale networks requires orders of magnitude larger and more heterogeneous data (e.g., alerts, logs) than SDN. We address this using coarsening - mapping complex data to a more compact abstract representation that has approximately the same effect, and is more scalable, maintainable, and learnable. We show examples including Coarse Bandwidth Logs for capacity planning and Coarse Dependency Graphs for incident routing. Coarse Dependency Graphs improve an incident routing metric from 45% to 78% while for a distributed approach like Scouts the same metric was 22%. We end by discussing how to realize SMN, and suggest cross-layer optimizations and coarsenings for other operational and planning problems in networks.
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Unprecedented Insights into Maternal Sleep: A Large-scale Longitudinal Analysis of Real-world Wearable Device Data Before, During, and After Pregnancy
Nichole Young-Lin
Conor Heneghan
Logan Schneider
Logan Niehaus
Ariel Haney
Karla Gleichauf
Jacqueline Shreibati
Belen Lafon
Lancet eBioMedicine (2025)
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Introduction: Current understanding of pregnancy and postpartum sleep is driven by limited lab or self-reported data. Consumer wearable devices may help reveal longitudinal, real-world sleep patterns.
Methods: We analyzed de-identified wearable device data from 2,540 users in the United States and Canada who met strict wear-time requirements (≥80% daily usage for ≥80% of the time periods of interest [12 weeks prepregnancy, throughout pregnancy, and 20 weeks immediately postpartum]). We tracked sleep time and staging using Fitbit devices.
Results: Compared to prepregnancy, total sleep time (TST) increased from an average of 425.3±43.5 min to a peak of 447.6±47.6 min at gestational week 10 with ongoing declines throughout pregnancy. Time in bed (TIB) followed a similar pattern. Increased light sleep drove the initial TST rise. Deep and REM sleep decreased significantly throughout pregnancy, with maximum reductions of 19.2±13.8 min (p<0.01) and 9.0±19.2 min (p<0.01) respectively by pregnancy end. Sleep efficiency also declined slightly during pregnancy (median drop from 88.3% to 86.8%). After delivery, TIB remained below the prepregnancy baseline by 14.7±45.7 min at one year postpartum and 15.2±47.7 min at 1.5 years postpartum.
Conclusion: This unprecedented look at large-scale, real-world sleep and pregnancy patterns revealed a previously unquantified initial increase in sleep followed by decreases in both quantity and quality as pregnancy progresses. Sleep deficits persist for at least 1.5 years postpartum. These quantified trends can assist clinicians and patients in understanding what to expect.
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Leveraging Per-Example Privacy for Machine Unlearning
Nazanin Mohammadi Sepahvand
Anvith Thudi
Ashmita Bhattacharyya
Nicolas Papernot
Eleni Triantafillou
Daniel M. Roy
Karolina Dziugaite
International Conference on Machine Learning (ICML) (2025)
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This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning. Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our theoretical analysis applies to Langevin dynamics, we empirically demonstrate that the derived guarantees—and their trends—continue to hold for fine-tuning, even in the absence of explicit noise. Our results show that per-instance privacy levels computed from training dynamics reliably predict unlearning difficulty, offering a principled and practical way to assess unlearning performance. Furthermore, our method identifies harder-to-unlearn data more effectively than existing heuristics, providing a more precise tool for guiding unlearning strategies. These findings pave the way for adaptive and efficient unlearning methods tailored to the properties of specific data points.
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Improved FPT Approximation Scheme and Approximate Kernel for Biclique-Free Max k-Weight SAT: Greedy Strikes Back
Theoretical Computer Science, 1028 (2025)
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In the Max k-Weight SAT (aka Max SAT with Cardinality Constraint) problem, we are given a CNF formula with n variables and m clauses together with a positive integer k. The goal is to find an assignment where at most k variables are set to one that satisfies as many constraints as possible. Recently, Jain et al. (SODA 2023) gave an FPT approximation scheme (FPT-AS) with running time 2^O((dk/ε)^d) * (n + m)^O(1) for Max k-Weight SAT when the incidence graph is K_{d,d}-free. They asked whether a polynomial-size approximate kernel exists. In this work, we answer this question positively by giving an (1 − ε)-approximate kernel with (dk/ε)^O(d) variables. This also implies an improved FPT-AS with running time (dk/ε)^O(dk) * (n+m)^O(1)-time algorithm for the problem. Our approximate kernel is based mainly on a couple of greedy strategies together with a sunflower lemma-style reduction rule.
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In this paper I describe the performance enchantments I implemented in a quantum-error-correction decoder developed at Google. The decoder is an open-source project and I am documenting the speedups I achieved in this paper.
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Adversarial Attacks in Multimodal Systems: A Practitioner's Survey
Aman Raj
IEEE Compsac 2025 (2025)
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Multimodal models represent a significant advancement in Artificial Intelligence. A single model is trained to understand unstructured modalities: text, image, video, and audio. Open-source variants of multimodal models have made these breakthroughs further accessible. ML practitioners adopt, finetune, and deploy open-source models in real-world applications. However, considering the vast landscape of adversarial attacks across these modalities, these models also inherit vulnerabilities of all the modalities, and eventually, the adversarial threat amplifies. While broad research is available on possible attacks within or across these modalities, a practitioner-focused view of outlining attack types remains absent in the multimodal world. This paper addresses the gap by surveying adversarial attacks targeting all four modalities: text, image, video, and audio. This survey provides a view of the adversarial attack landscape and presents how multimodal adversarial threats have evolved. To the best of our knowledge, this survey is the first comprehensive summarization of the threat landscape in the multimodal world.
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We present new efficient algorithms for high-dimensional calibration via reduction to the TreeSwap algorithm of Dagan et al.
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Fast ACS: Low-Latency File-Based Ordered Message Delivery at Scale
Anil Raghunath Iyer
Neel Bagora
Chang Yu
Olivier Pomerleau
Vivek Kumar
Prunthaban Kanthakumar
Usenix Annual Technical Conference (2025)
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Low-latency message delivery is crucial for real-time systems. Data originating from a producer must be delivered to consumers, potentially distributed in clusters across metropolitan and continental boundaries. With the growing scale of computing, there can be several thousand consumers of the data. Such systems require a robust messaging system capable of transmitting messages containing data across clusters and efficiently delivering them to consumers. The system must offer guarantees like ordering and at-least-once delivery while avoiding overload on consumers, allowing them to consume messages at their own pace.
This paper presents the design of Fast ACS (an abbreviation for Ads Copy Service), a file-based ordered message delivery system that leverages a combination of two-sided (inter-cluster) and one-sided (intra-cluster) communication primitives—namely, Remote Procedure Call and Remote Direct Memory Access, respectively—to deliver messages. The system has been successfully deployed to dozens of production clusters and scales to accommodate several thousand consumers within each cluster, which amounts to Tbps-scale intra-cluster consumer traffic at peak. Notably, Fast ACS delivers messages to consumers across the globe within a few seconds or even sub-seconds (p99) based on the message volume and consumer scale, at a low resource cost.
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Online Bidding under RoS Constraints without Knowing the Value
Sushant Vijayan
Swati Padmanabhan
The Web Conference (2025)
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We consider the problem of auto-bidding in online advertising from the perspective of a single advertiser. The goal of the advertiser is to maximize their value under the Return-on-Spend (RoS) constraint, with performance measured in terms of \emph{regret} against the optimal offline solution that knows all queries a priori. Importantly, the value of the item is \textit{unknown} to the bidder ahead of time. The goal of the bidder is to quickly identify the optimal bid, while simultaneously satisfying budget and RoS constraints. Using a simple UCB-style algorithm, we provide the first result which achieves optimal regret and constraint violation for this problem.
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