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 10501 publications
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)
Preview abstract
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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XR Blocks: Accelerating Human-Centered AI + XR Innovation
Nels Numan
Evgenii Alekseev
Alex Cooper
Min Xia
Scott Chung
Jeremy Nelson
Xiuxiu Yuan
Jolica Dias
Tim Bettridge
Benjamin Hersh
Michelle Huynh
Konrad Piascik
Ricardo Cabello
Google, XR, XR Labs (2025)
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We are on the cusp where Artificial Intelligence (AI) and Extended Reality (XR) are converging to unlock new paradigms of interactive computing. However, a significant gap exists between the ecosystems of these two fields: while AI research and development is accelerated by mature frameworks like PyTorch and benchmarks like LMArena, prototyping novel AI-driven XR interactions remains a high-friction process, often requiring practitioners to manually integrate disparate, low-level systems for perception, rendering, and interaction. To bridge this gap, we present XR Blocks, a cross-platform framework designed to accelerate human-centered AI + XR innovation. XR Blocks provides a modular architecture with plug-and-play components for core abstraction in AI + XR: user, world, peers; interface, context, and agents. Crucially, it is designed with the mission of "minimum code from idea to reality", accelerating rapid prototyping of complex AI + XR apps. Built upon accessible technologies (WebXR, three.js, TensorFlow, Gemini), our toolkit lowers the barrier to entry for XR creators. We demonstrate its utility through a set of open-source templates, samples, and advanced demos, empowering the community to quickly move from concept to interactive prototype.
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Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces
Mauro Comi
Max Yang
Jonathan Tremblay
Valts Blukis
Yijiong Lin
Nathan Lepora
Laurence Aitchison
2025
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Touch and vision go hand in hand, mutually enhancing our ability to understand the world. From a research perspective, the problem of mixing touch and vision is underexplored and presents interesting challenges. To this end, we propose Tactile-Informed 3DGS, a novel approach that incorporates touch data (local depth maps) with multi-view vision data to achieve surface reconstruction and novel view synthesis. Our method optimises 3D Gaussian primitives to accurately model the object's geometry at points of contact. By creating a framework that decreases the transmittance at touch locations, we achieve a refined surface reconstruction, ensuring a uniformly smooth depth map. Touch is particularly useful when considering non-Lambertian objects (e.g. shiny or reflective surfaces) since contemporary methods tend to fail to reconstruct with fidelity specular highlights. By combining vision and tactile sensing, we achieve more accurate geometry reconstructions with fewer images than prior methods. We conduct evaluation on objects with glossy and reflective surfaces and demonstrate the effectiveness of our approach, offering significant improvements in reconstruction quality.
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Fast electronic structure quantum simulation by spectrum amplification
Guang Hao Low
Robbie King
Dominic Berry
Qiushi Han
Albert Eugene DePrince III
Alec White
Rolando Somma
arXiv:2502.15882 (2025)
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The most advanced techniques using fault-tolerant quantum computers to estimate the ground-state energy of a chemical Hamiltonian involve compression of the Coulomb operator through tensor factorizations, enabling efficient block-encodings of the Hamiltonian. A natural challenge of these methods is the degree to which block-encoding costs can be reduced. We address this challenge through the technique of spectrum amplification, which magnifies the spectrum of the low-energy states of Hamiltonians that can be expressed as sums of squares. Spectrum amplification enables estimating ground-state energies with significantly improved cost scaling in the block encoding normalization factor $\Lambda$ to just $\sqrt{2\Lambda E_{\text{gap}}}$, where $E_{\text{gap}} \ll \Lambda$ is the lowest energy of the sum-of-squares Hamiltonian. To achieve this, we show that sum-of-squares representations of the electronic structure Hamiltonian are efficiently computable by a family of classical simulation techniques that approximate the ground-state energy from below. In order to further optimize, we also develop a novel factorization that provides a trade-off between the two leading Coulomb integral factorization schemes-- namely, double factorization and tensor hypercontraction-- that when combined with spectrum amplification yields a factor of 4 to 195 speedup over the state of the art in ground-state energy estimation for models of Iron-Sulfur complexes and a CO$_{2}$-fixation catalyst.
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We study the existence of almost fair and near-optimal solutions to a routing problem as defined in the seminal work of Rosenthal. We focus on the setting where multiple alternative routes are available for each potential request (which corresponds to a potential user of the network). This model captures a collection of diverse applications such as packet routing in communication networks, routing in road networks with multiple alternative routes, and the economics of transportation of goods.
Our recommended routes have provable guarantees in terms of both the total cost and fairness concepts such as approximate envy-freeness. We employ and appropriately combine tools from algorithmic game theory and fair division. Our results apply on two distinct models: the splittable case where the request is split among the selected paths (e.g., routing a fleet of trucks) and the unsplittable case where the request is assigned to one of its designated paths (e.g., a single user request). Finally, we conduct an empirical analysis to test the performance of our approach against simpler baselines using the real world road network of New York City.
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Automated loss of pulse detection on a commercial smartwatch
Kamal Shah
Yiwen Chen
Anthony Stange
Lawrence Cai
Matt Wimmer
Pramod Rudrapatna
Shelten Yuen
Anupam Pathak
Shwetak Patel
Mark Malhotra
Marc Stogaitis
Jeanie Phan
Ali Connell
Jim Taylor
Jacqueline Shreibati
Daniel McDuff
Tajinder Gadh
Jake Sunshine
Nature, 642 (2025), pp. 174-181
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Out-of-hospital cardiac arrest is a time-sensitive emergency that requires prompt identification and intervention: sudden, unwitnessed cardiac arrest is nearly unsurvivable. A cardinal sign of cardiac arrest is sudden loss of pulse. Automated biosensor detection of unwitnessed cardiac arrest, and dispatch of medical assistance, may improve survivability given the substantial prognostic role of time, but only if the false-positive burden on public emergency medical systems is minimized. Here we show that a multimodal, machine learning-based algorithm on a smartwatch can reach performance thresholds making it deployable at a societal scale. First, using photoplethysmography, we show that wearable photoplethysmography measurements of peripheral pulselessness (induced through an arterial occlusion model) manifest similarly to pulselessness caused by a common cardiac arrest arrhythmia, ventricular fibrillation. On the basis of the similarity of the photoplethysmography signal (from ventricular fibrillation or arterial occlusion), we developed and validated a loss of pulse detection algorithm using data from peripheral pulselessness and free-living conditions. Following its development, we evaluated the end-to-end algorithm prospectively: there was 1 unintentional emergency call per 21.67 user-years across two prospective studies; the sensitivity was 67.23% (95% confidence interval of 64.32% to 70.05%) in a prospective arterial occlusion cardiac arrest simulation model. These results indicate an opportunity, deployable at scale, for wearable-based detection of sudden loss of pulse while minimizing societal costs of excess false detections.
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Browser fingerprinting enables persistent cross-site user tracking via subtle techniques that often evade conventional defenses or cause website breakage when script-level blocking countermeasures are applied. Addressing these challenges requires detection methods offering both function-level precision to minimize breakage and inherent robustness against code obfuscation and URL manipulation.
We introduce ByteDefender, the first system leveraging V8 engine bytecode to detect fingerprinting operations specifically at the JavaScript function level. A Transformer-based classifier, trained offline on bytecode sequences, accurately identifies functions exhibiting fingerprinting behavior. We develop and evaluate lightweight signatures derived from this model to enable low-overhead, on-device matching against function bytecode during compilation but prior to execution, which only adds a 4% (average) latency to the page load time. This mechanism facilitates targeted, real-time prevention of fingerprinting function execution, thereby preserving legitimate script functionality. Operating directly on bytecode ensures inherent resilience against common code obfuscation and URL-based evasion. Our evaluation on the top 100k websites demonstrates high detection accuracy at both function- and script-level, with substantial improvements over state-of-the-art AST-based methods, particularly in robustness against obfuscation. ByteDefender offers a practical framework for effective, precise, and robust fingerprinting mitigation.
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Binamix -- A Python Library for Generating Binaural Audio Datasets
Dan Barry
Davoud Shariat Panah
Alessandro Ragano
Andrew Hines
AES 158th Audio Engineering Society Convention (2025)
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The increasing demand for spatial audio in applications such as virtual reality, immersive media, and spatial audio research necessitates robust solutions to generate binaural audio data sets for use in testing and validation. Binamix is an open-source Python library designed to facilitate programmatic binaural mixing using the extensive SADIE II Database, which provides Head Related Impulse Response (HRIR) and Binaural Room Impulse Response (BRIR) data for 20 subjects. The Binamix library provides a flexible and repeatable framework for creating large-scale spatial audio datasets, making it an invaluable resource for codec evaluation, audio quality metric development, and machine learning model training. A range of pre-built example scripts, utility functions, and visualization plots further streamline the process of custom pipeline creation. This paper presents an overview of the library’s capabilities, including binaural rendering, impulse response interpolation, and multi-track mixing for various speaker layouts. The tools utilize a modified Delaunay triangulation technique to achieve accurate HRIR/BRIR interpolation where desired angles are not present in the data. By supporting a wide range of parameters such as azimuth, elevation, subject Impulse Responses (IRs), speaker layouts, mixing controls, and more, the library enables researchers to create large binaural datasets for any downstream purpose. Binamix empowers researchers and developers to advance spatial audio applications with reproducible methodologies by offering an open-source solution for
binaural rendering and dataset generation. We release the library under the Apache 2.0 License at https://github.com/QxLabIreland/Binamix/
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Deep Multi-modal Species Occupancy Modeling
Timm Haucke
Yunyi Shen
Levente Klein
David Rolnick
Lauren Gillespie
Sara Beery
bioRxiv (2025)
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Occupancy models are tools for modeling the relationship between habitat and species occurrence while accounting for the fact that species may still be present even if not detected. The types of environmental variables typically used for characterizing habitats in such ecological models, such as precipitation or tree cover, are frequently of low spatial resolution, with a single value for a spatial pixel size of, e.g., 1km2. This spatial scale fails to capture the nuances of micro-habitat conditions that can strongly influence species presence, and additionally, as many of these are derived from satellite data, there are aspects of the environment they cannot capture, such as the structure of vegetation below the forest canopy. We propose to combine high-resolution satellite and ground-level imagery to produce multi-modal environmental features that better capture micro-habitat conditions, and incorporate these multi-modal features into hierarchical Bayesian species occupancy models. We leverage pre-trained deep learning models to flexibly capture relevant information directly from raw imagery, in contrast to traditional approaches which rely on derived and/or hand-crafted sets of ecosystem covariates. We implement deep multi-modal species occupancy modeling using a new open-source Python package for ecological modeling, designed for bridging machine learning and statistical ecology. We test our method under a strict evaluation protocol on 16 mammal species across thousands of camera traps in Snapshot USA surveys, and find that multi-modal features substantially enhance predictive power compared to traditional environmental variables alone. Our results not only highlight the predictive value and complementarity of in-situ samples, but also make the case for more closely integrating deep learning models and traditional statistical ecological models.
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Despite the advent of legislation such as the General Data Protection Regulation (GDPR) with its associated "Right to be Forgotten" (RTBF), few, if any, studies have measured user reactions to realistic edge cases with public-interest content. Surveying both users covered by and excluded from RTBF, this vignette-based survey experiment sought to better understand how users think of delisting content from search engine results and what factors influence user perceptions. While leaving information accessible in search engine results generally leads to warmer feelings towards those search engines than delisting it, we find that users do prefer different outcomes
depending on contextual elements specific to given cases. We also find that whether a country has active RTBF legislation does seem to be associated with both knowledge and attitudes about RTBF, but is unlikely to explain all of it. These results indicate a complex context around removing public-interest content from search engines’ results; it is essential that experts sensitive to local context perform the review in order to ensure that removal requests are handled in a way that meets users’ expectations.
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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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Steering Self-Evaluation: Interpreting LLM’s Reasoning Across Domains and Languages
Praveen Hegde
2025
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Understanding and controlling the reasoning processes of large language models (LLMs) is crucial for their reliable deployment. In this work, we investigate the latent representation of self-evaluation behavior - the ability of a model to assess its own reasoning steps - a vital behavior for robust reasoning. Through targeted steering vector computation, we identify a direction within LLM activations that represents this self-evaluation behavior. Crucially, we demonstrate that this steering vector for self-evaluation exhibits remarkable cross-contextual efficacy, working well across different domains (e.g., math and medicine) and languages (e.g., English and Spanish). This suggests that the identified latent direction captures a fundamental, abstract representation of self-evaluation within the LLM's internal state, offering a promising avenue for interpretable and controllable reasoning across diverse applications.
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Mind the GAP: Geometry Aware Passthrough Mitigates Cybersickness
Trishia Chemaly
Mohit Goyal
Sakar Khattar
Bjorn Vlaskamp
Aveek Purohit
Konstantine Tsotsos
2025
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Virtual Reality headsets isolate users from the real-world by restricting their perception to the virtual-world. Video See-Through (VST) headsets address this by utilizing world-facing cameras to create Augmented Reality experiences. However, directly displaying camera feeds can cause visual discomfort and cybersickness due to the inaccurate perception of scale and exaggerated motion parallax. This paper presents initial findings on the potential of geometry aware passthrough systems to mitigate cybersickness through enhanced depth perception. We introduce a promising protocol for quantitatively measuring cybersickness experienced by users in VST headsets. Using this protocol, we conduct a user study to compare direct passthrough and geometry aware passthrough systems. To the best of our knowledge, our study is the first one to reveal reduced nausea, disorientation, and total scores of cybersickness with geometry aware passthrough. It also uncovers several potential avenues to further mitigate visually-induced discomfort.
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As large language models (LLMs) improve in their capacity to serve as personal AI assistants, their ability to output uniquely tailored, personalized responses that align with the soft preferences of their users is imperative for maximizing user satisfaction and retention. However, lay users are notoriously bad at prompt specification and often struggle with conveying their latent preferences to AI assistants. To resolve this, we demonstrate that activation steering, an inference-time method, can effectively control the response of the LLMs towards expressing different preferences. In contrast to memory-based personalization methods that require long user history, steering is extremely lightweight and easily-controllable via an interpretable linear strength factor. We further conduct a within-subjects user study (n=14) to investigate how end users personalize their conversations through three different steerable chatbot interfaces. The results demonstrate the effectiveness of preference-based steering for aligning real-world conversations with user preferences, and we discuss qualitative findings on how diverse values around control, transparency, and usability of personalization lead users to prefer different interfaces.
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Introducing the DORA AI Capabilities Model: 7 keys to succeeding in AI-assisted software development
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Artificial intelligence is rapidly transforming software development. But simply adopting AI tools isn’t a guarantee of success. Across the industry, tech leaders and developers are asking the same critical questions: How do we move from just using AI to truly succeeding with it? How do we ensure our investment in AI delivers better, faster, and more reliable software?
The DORA research team has developed the inaugural DORA AI Capabilities Model to provide data-backed guidance for organizations grappling with these questions. This is not just another report on AI adoption trends; it is a guide to the specific technical and cultural practices that amplify the benefits of AI.
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