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.
Sort By
1 - 15 of 11376 publications
What’s on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale
Rameen Mahmood
Danny Yuxing Huang
Proceedings of ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT), Association for Computing Machinery (2026)
Mull-Tokens: Modality-Agnostic Latent Thinking
Arijit Ray
Chengzhi Mao
Bryan A. Plummer
Kate Saenko
Ranjay Krishna
Leonidas Guibas
Vincent Chu
IEEE/CVF Conference on Computer Vision and Pattern Recognition (Findings) (2026) (to appear)
Preview abstract
Reasoning goes beyond language; the real world requires reasoning about space, time, affordances, and much more that words alone cannot convey. Existing multimodal models exploring the potential of reasoning with images are brittle and do not scale. They rely on calling specialist tools, costly generation of images, or handcrafted reasoning data to switch between text and image thoughts. Instead, we offer a simpler alternative -- Mull-Tokens -- modality-agnostic latent tokens pre-trained to hold intermediate information in either image or text modalities to let the model think free-form towards the correct answer. We investigate best practices to train Mull-Tokens inspired by latent reasoning frameworks. We first train Mull-Tokens using supervision from interleaved text-image traces, and then fine-tune without any supervision by only using the final answers. Across four challenging spatial reasoning benchmarks involving tasks such as solving puzzles and taking different perspectives, we demonstrate that Mull-Tokens improve upon several baselines utilizing text-only reasoning or interleaved image-text reasoning, achieving a +3% average improvement and up to +16% on a puzzle solving reasoning-heavy split compared to our strongest baseline. Adding to conversations around challenges in grounding textual and visual reasoning, Mull-Tokens offers a simple solution to abstractly think in multiple modalities.
View details
Preview abstract
We study the problem of allocating access point bandwidth to users of a wireless network in the presence of adversarial jamming. Specifically, we consider a setting in which the network designer acts first and allocates access point bandwidth to the users of the network, before an adversary applies a jamming strategy to reduce the bandwidth of a subset (or all) of the access points. We consider a strong adversary who has complete information and can optimize the jamming strategy, subject to power budget constraints. In turn, the network designer must allocate the resources in anticipation of the adversary's actions.
We explain that our model gives rise to a special network interdiction model, which differs from the standard setting in two ways: The first is that the interdictor is given the benefit of responding, rather than leading the game. The second is that the interdiction is fractional and performed at the node level of the network. The interdiction then propagates to all edges incident to the access point.
In terms of technical results, we provide an allocation algorithm that is based on linear programming duality and show that the algorithm can solve the problem optimally, assuming knowledge of the adversary's budget constraints. We conduct experiments on synthetic data to show the extent to which the algorithm improves the total utilized bandwidth over the algorithm that optimizes bandwidth allocation while being oblivious to the adversary's existence.
View details
Preview abstract
This article delves into how Google Site Reliability Engineers (SREs) leverage Gemini 3 and the Gemini CLI to aggressively reduce Mean Time to Mitigation (MTTM) during real-world outages. By focusing on the SRE motto of "Eliminate Toil," the article walks through a simulated incident, demonstrating how an agentic CLI acts as a human-in-the-loop copilot across the entire incident lifecycle: from initial paging and investigation, through safe, tool-driven mitigation and root cause analysis, to automated postmortem generation and action item filing. This direct integration of Gemini's reasoning capabilities with operational data and internal tools creates a virtuous cycle where past incident learnings continuously inform and improve future solutions.
View details
Preview abstract
In large-scale distributed enterprises, traditional Knowledge Management (KM) systems face a critical failure mode: static documentation cannot keep pace with evolving operational realities and regional nuances. This "knowledge latency" forces employees out of self-service workflows and into costly support ticketing queues. This paper introduces SENTINEL, a geo-contextual AI framework designed to shift enterprise support from reactive retrieval to proactive interception. The architecture employs a novel dual-engine system integrated into an omni-present interface. The first engine utilizes Large Language Models (LLMs) to conduct pre-emptive, historical case-grounded audits of documentation, generating a "Contextual Density" score that identifies friction zones. The second engine is an autonomous Retrieval-Augmented Generation (RAG) agent that surfaces in-situ via a location-intelligent assistant window, resolving queries in real-time. By functioning as a strategic "defensive barrier" at the point of origin, SENTINEL demonstrates how a proactive AI assistant can drive high-fidelity, in-situ case deflection.
View details
Who Controls the Curriculum for AI? The Limits of Participatory Design for Educational AI
Learning Under Algorithmic Conditions, University of Minnesota Press (2026)
Preview abstract
Participatory design is a long-standing effort to shift control over technology design from technologists to users and communities impacted by technologies. For educational AI, this means involving students, families, teachers, and other stakeholders in shaping the design of AI systems. While promising, in this article, I situate the recent calls for participatory design of educational AI systems within a different historical tradition—that of contests over local control of educational curricula. I argue that approaches that attempt to steer the design and development of educational AI through participatory methods may inadvertently reproduce the history of political contestation of educational curricula, in ways that may privilege the most powerful communities, rather than those inequitably impacted. What might it look like to treat participatory AI design as a site for political contestation? How might these approaches avoid reproducing the same majoritarian tendencies that led to educational inequities in the first place?
View details
Preview abstract
This talk addresses the challenges of operating Google's monitoring systems at scale, handling terabytes of telemetry data and preventing overload from diverse workloads. We'll explore how Google's internal client library and Monarch, its planet-scale time-series database, work together for cost-effective data collection. Key principles include a distributed push model, dynamic client-side data reduction, centralized retention, and periodic metric analysis. The session will then bridge these concepts to the open-source world, discussing our work with OpenTelemetry's OpAMP protocol to achieve similar scalable and efficient telemetry collection. Attendees will gain insights into adapting these principles for cost savings and learn about our collaboration with the OpAMP SIG to benefit the broader community.
View details
Preview abstract
Validating conversational artificial intelligence (AI) for regulated medical software applications may present challenges, as static test datasets and manual review may be limited in identifying emergent, conversational anomalies. A multi-agent AI system may be configured in a closed-loop for automated validation. The system can, for example, utilize an end user persona simulator agent to generate prompts for a target model and a domain /regulatory expert adjudicator agent to evaluate the target model’s responses against a configurable rubric. A meta-analysis agent can analyze anomalies to identify underlying vulnerabilities, which may then be used to programmatically synthesize new adversarial personas. This adaptive process can generate evidence to support regulatory compliance and continuous performance monitoring for medical software algorithms systems.
View details
Preview abstract
This disclosure describes systems and methods for a multi-agent framework that can automate and scale cognitive work. The framework can, for example, use a cognitive assembly line of specialized computational agents to perform tasks such as research and drafting. A beneficial component could be an adversarial review panel (ARP), which is a multi-agent review system where distinct agent personas critique a generated draft from varied perspectives. The structured feedback from the ARP can be used to automatically iterate on and refine the work product. This approach can improve the intellectual rigor of generated content and reduce the time required for production, which may allow human operators to focus on activities such as strategic oversight and final validation.
View details
SNPeek: Side-Channel Analysis for Privacy Applications on Confidential VMs
Ruiyi Zhang
Albert Cheu
Adria Gascon
Michael Schwarz
Octavian Suciu
Network and Distributed System Security (NDSS) (2026)
Preview abstract
Confidential virtual machines (CVMs) based on trusted execution environments (TEEs) enable new privacy-preserving solutions. But CVMs are not a privacy panacea, as they are vulnerable to side-channel attacks that may compromise confidentially of workloads.
In this work, we develop the FARFETCH’D framework to help developers evaluate side-channel assisted privacy attacks that are broadly applicable to CVMs. The privacy reduction due to these attacks heavily depend on the execution environment and the workload, which varies vastly:What are avail-able attack primitives? How does the particular privacy work-load behave?This makes manual investigation and efficiently mitigating software-based side channels a cumbersome and impossible task. FARFETCH’D solves this challenge by providing a set of configurable attack primitives that can execute on real CVM hardware and automated ML-based analysis pipelines. We evaluate the effectiveness of FARFETCH’D on privacy-preserving workloads. Our results show that our approach is effective at pinpointing the vulnerability of privacy apps against side channels and help evaluating mitigation based on oblivious memory and differential privacy.
View details
Preview abstract
PURPOSE: To introduce Cardio Load (CL), a metric quantifying cardiovascular work from all activities across the day, and to investigate its distribution by age, gender, and workout profiles. CL adapts the Training Impulse (TRIMP) model by leveraging continuous heart rate and movement data from wearables, enabling minute-level intensity estimation. We also discuss the derivation of weekly target loads, intended to guide fitness maintenance.
METHODS: A retrospective analysis was conducted on 31.2 million hours of wrist-worn wearable data collected over a six-week period. The dataset comprised a 40,000-subject subset (37.9% female) of consenting Google Pixel Watch® users in the United States, aged 18 to 80 years (18-39: 41.8%, 40-59: 43.5%, 60+: 14.6%). Measured data included minute-interval heart rate averages, resting and maximum heart rates, minute-interval averaged accelerometer log energy, and manually-logged or auto-detected activity types. Cardio Load scores and target loads were calculated daily for each subject and compared across age and gender. We also compared the proportions of CL gained during workouts and incidental daily activities for these groups.
RESULTS: Overall, the study population's mean ± SD weekly CL scores were 221 ± 156 (female) and 259 ± 169 (male). Median weekly Cardio Load (CL) values exhibited consistency for individuals between 30 and 75 years of age. When analyzed in five-year age groups, the coefficient of variation (CV%) of median weekly CL values within this age range was less than 4.5%, with younger and older subjects demonstrating higher and lower median CL, respectively. The median proportion of CL accumulated during structured workouts versus incidental daily activity was 41.0% (female) and 49.0% (male) for all subjects, though this varied considerably with average weekly workout duration. CV% of weekly target load and daily target load over 6 weeks was 23.6% and 35.2% respectively.
CONCLUSION: Cardio Load provides a continuous quantification of activity load from wearables, acknowledging both structured workouts and everydayincidental activity. CL is equitably rewarded for age ranges spanning 30-75 years. Weekly target loads were found to have little measurement variability and be more consistent and, consequently, more practical for planning training and physical activity than daily targets.
View details
Preview abstract
Generative AI (GenAI) is evolving from standalone tools to interconnected ecosystems that integrate chatbots, cloud platforms, and third-party services. While this ecosystem model enables personalization and extended services, it also introduces complex information flows and amplifies privacy risks. Existing solutions focus on system-level protections, offering little support for users to make meaningful privacy choices. To address this gap, we conducted two vignette-based survey studies with 486 participants and a followup interview study with 16 participants. We also explored users’ needs and preferences for privacy choice design across both GenAI personalization and data-sharing. Our results reveal paradoxical patterns: participants sometimes trusted third-party ecosystems more for personalization but perceived greater control in first-party ecosystems when data was shared externally. We discuss design implications for privacy choice interfaces that enhance transparency, control, and trust in GenAI ecosystems.
View details
Peeking Ahead of the Field Study: Exploring VLM Personas as Support Tools for Embodied Studies in HCI
Xinyue Gui
Ding Xia
Mark Colley
Yuan Li
Vishal Chauhan
Anubhav Anubhav
Ehsan Javanmardi
Stela Hanbyeol Seo
Chia-Ming Chang
Manabu Tsukada
Takeo Igarashi
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI 26)
Preview abstract
Field studies are irreplaceable but costly, time-consuming, and error-prone, which need careful preparation. Inspired by rapid-prototyping in manufacturing, we propose a fast, low-cost evaluation method using Vision-Language Model (VLM) personas to simulate outcomes comparable to field results. While LLMs show human-like reasoning and language capabilities, autonomous vehicle (AV)-pedestrian interaction requires spatial awareness, emotional empathy, and behavioral generation. This raises our research question: To what extent can VLM personas mimic human responses in field studies? We conducted parallel studies: 1) one real-world study with 20 participants, and 2) one video-study using 20 VLM personas, both on a street-crossing task. We compared their responses and interviewed five HCI researchers on potential applications. Results show that VLM personas mimic human response patterns (e.g., average crossing times of 5.25 s vs. 5.07 s) lack the behavioral variability and depth. They show promise for formative studies, field study preparation, and human data augmentation.
View details
Preview abstract
Enterprise service centers, particularly in domains like People Operations, are critical hubs of organizational knowledge work. They face a persistent difficulty in disseminating the tacit, case-specific expertise of senior agents, which can lead to inconsistent service and slower onboarding for new hires. While existing Knowledge Management (KM) and Case-Based Reasoning (CBR) systems have improved the retrieval of historically similar cases, they inadvertently shift the cognitive burden of synthesizing this information to the time-constrained agent. This paper introduces the Dynamic Case Precedent (DCP) architecture, a novel socio-technical framework designed to address this gap. The DCP architecture moves beyond simple precedent recommendation to automated precedent synthesis. It achieves this by integrating a semantic retrieval model with the large-context reasoning capabilities of a generative Large Language Model (LLM). We propose a three-pillar framework—(1) Contextual Similarity Indexing, (2) Generative Insight Synthesis, and (3) Human-in-the-Loop Refinement. By analyzing multiple relevant historical cases to generate a concise summary of resolution patterns, the DCP architecture aims to reduce agent cognitive load, accelerate proficiency, and improve service consistency. This conceptual framework offers a new model for human-AI collaboration, framing the AI not as a mere information tool, but as an active partner in sensemaking.
View details
Sexual dimorphism in the complete connectome of the Drosophila male central nervous system
Stuart Berg
Isabella R Beckett
Marta Costa
Philipp Schlegel
Elizabeth C Marin
Aljoscha Nern
Stephan Preibisch
Wei Qiu
Shin-ya Takemura
Andrew Champion
Reed A. George
Gary Huang
William Katz
Christopher Ordish
Ken Hayworth
Eric Trautman
Vivek Jayaraman
Wyatt Korff
Geoffrey W Meissner
Sandro Romani
Jan Funke
Christopher Knecht
Stephan Saalfeld
Louis Scheffer
Scott Waddell
Gwyneth Card
Carlos Ribeiro
Michael B. Reiser
Harald Hess
Gerry Rubin
Gregory S.X.E. Jefferis
bioRxiv (2026)
Preview abstract
Sex differences in behaviour exist across all animals, typically under strong genetic regulation. In Drosophila, fruitless/doublesex transcription factors can identify dimorphic neurons but their organisation into functional circuits remains unclear.
We present the connectome of the entire Drosophila male central nervous system. This contains 166,691 neurons spanning the brain and nerve cord, fully proofread and annotated including fruitless/doublesex expression and 11,691 types. We provide the first comprehensive comparison between male and female brain connectomes to synaptic resolution, finding 7,205 isomorphic, 114 dimorphic, 262 male-specific and 69 female-specific types.
This resource enables analysis of full sensory-to-motor circuits underlying complex behaviours and the impact of dimorphic elements. Sex-specific/dimorphic neurons are concentrated in higher brain centres while the sensory and motor periphery are largely isomorphic. Within higher centres, male-specific connections are organised into hotspots defined by male-specific neurons or arbours. Numerous circuit switches reroute sensory information to form antagonistic circuits controlling opposing behaviours.
(Full author list included with the paper.)
View details
