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 11622 publications
When Android Apps Request Location with a Reason: A Developer Study and User Behavior Experiment
May Smith
Aman Jain
Proceedings on Privacy Enhancing Technologies (2027)
Preview abstract
Recent reports have highlighted how mobile apps share user location data with third parties, risking user privacy and platform trust. Although location data is highly sensitive, when users grant apps location access, they may not know the full extent to which it is used. We study how requiring Android apps to show a reason for location access could impact developers, users, and the platform.
We surveyed 323 Android app developers and found most supported such a requirement. The majority said it would have a positive impact on user privacy, trust for apps, and trust for Android, where impact on user trust for Android correlated most strongly with support. Many developers also said the intervention would increase the number of users granting location access. Yet their open-ended comments also revealed consistent concerns, such as apps providing dishonest reasons and platform verification.
To study the impact on user behavior, we conducted a randomized controlled experiment with 2579 US Android users. We tested how users' decisions to grant location access were impacted by app type, whether reasons were included in the requests, and the content of the reasons, including monetization. We did not find the reasons impacted users' decisions; decisions were instead driven by app type and demographics. Yet we did find the reasons could have a positive impact on user perception for the platform when the reasons did not include using data for ads. Our findings provide insights into developers' willingness to implement privacy-enhancing changes, and expose limits to improving user privacy by simply adding information to user interfaces.
View details
A 3D Scene Graphs Survey: Open Challenges and Future Directions
Dennis Rotondi
Francesco Argenziano
Sebastian Koch
Nathan Hughes
Martin Büchner
Johanna Wald
Lukas Schmid
Daniele Nardi
Abhinav Valada
Liam Paul
Luca Carlone
Kai Arras
Annual Review of Control, Robotics, and Autonomous Systems (ARCRAS), 10 (2027) (to appear)
Preview abstract
3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relational abstractions of the environment. Their expressiveness has made them relevant to a broad range of problems in robotics and computer vision, including mapping, task and motion planning, scene understanding, and many others. However, the field remains fragmented: different communities adopt distinct formulations, construction pipelines, and evaluation protocols, making it difficult to compare methods, identify common assumptions, and assess remaining challenges for robust real- world deployment. This survey provides a unified and critical review of 3DSGs, with particular emphasis on open challenges and future directions. We first formalize 3DSGs under a common definition and analyze the principal modeling choices that characterize existing formulations, including node and edge attributes, hierarchical structure, dynamic scene representations, and affordance-aware extensions. We then review how 3DSGs are constructed from raw sensory observations, covering both learning-oriented and construction-oriented systems. Finally, we examine downstream applications and evaluation strategies, from intrinsic graph quality to task-level performance. To support the community, we also provide a dedicated website that organizes and extends the surveyed works.
View details
"You Have Been Selected as the Winner": Characterizing User-Reported Scams on TikTok
Smirity Kaushik
Kyle Beadle
Gauri Nayak
Madelyn Rose Sanfilippo
Mainack Mondal
Yang Wang
JingJei Li
Yixin Zou
USENIX Symposium on Usable Privacy and Security (SOUPS) (2026)
Preview abstract
Short-form video platforms (SVPs) such as TikTok have grown rapidly in popularity. While online scams have been extensively studied, the extent to which they take new forms on SVPs and the discourses around them remain understudied. Using TikTok as a case study, we analyzed 150 videos in which content creators reported scam experiences and offered anti-scam advice. We focus on how TikTok users (creators, followers, and commenters) discuss scams, rather than analyzing scams. Our analysis surfaces six types of scams, including creator impersonation and account badge verification scams that target TikTok's influencer-follower ecosystem. Scammers also exploit platform-specific features (e.g., direct messaging or the "For You Page") to lure victims. In response, TikTok users share strategies to identify scammer profiles and communication cues, building community support through anti-scam advice. Based on our findings, we offer recommendations for systemizing platform support to combat scams and leveraging the influencer ecosystem to raise awareness.
View details
LLM-Powered Analysis of IoT User Reviews: Tracking and Ranking Security and Privacy Concerns
Taufiq Islam Protick
Anupam Das
Proceedings of the International AAAI Conference on Web and Social Media (ICWSM) (2026)
Preview abstract
Being able to understand the security and privacy (S&P) concerns of IoT users brings benefits to both developers and users. To learn about users' views, we examine Amazon IoT reviews - one of the biggest IoT markets. This work presents a state-of-the-art methodology to identify and categorize reviews in which users express S&P concerns. We developed an automated pipeline by fine-tuning GPT-3.5-Turbo to build two models: the Classifier-Rationalizer-Categorizer and the Thematic Mapper. By leveraging dynamic few-shot prompting and the model's large context size, our pipeline achieved over 97% precision and recall, significantly outperforming keyword-based and classical ML methods. We applied our pipeline to 91K Amazon reviews about fitness trackers, smart speakers and cameras, over multiple years. We found that on average 5% contained S&P concerns, while security camera exhibited the highest prevalence at 10%. Our method detected significantly more S&P-relevant reviews than prior works: 15x more for fitness trackers, 29% more for smart speakers, and 70% more for cameras. Our longitudinal analysis reveals that concerns like surveillance and data control have persisted for years, suggesting limited industry progress. We demonstrate that across all device types, users consistently demand more precise control over what data is collected and shared. We uncover challenges in multi-user and multi-device interactions, identifying two previously unreported themes concerning inadequate controls for account separation and data access. These findings, ranging from broad persistent trends to specific instances of customer loss, offer actionable insights for developers to improve user satisfaction and trust.
View details
PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections
Pengfei He
Vishesh Sharma
Ash Fox
George Lee
Jiliang Tang
2026
Preview abstract
Large Language Models (LLMs) are rapidly evolving into agentic systems that interact with external tools and dynamic environments, but this also introduces severe security risks. In particular, indirect prompt injection attacks can compromise agents through malicious instructions hidden in external sources such as web pages, emails, and retrieved documents. Existing defenses are largely reactive, while current automated red-teaming methods mainly optimize attack success rather than systematically uncovering hidden vulnerabilities within the agent pipeline. In this work, we propose PI-Hunter, an automated agentic red-teaming framework that shifts the focus from attack optimization to vulnerability exposure. By combining static attack-surface analysis, source-aware seeding, trajectory evaluation, and feedback-guided exploration, PI-Hunter proactively discovers vulnerable ingestion paths and localizes how malicious instructions propagate through agent reasoning. Extensive experiments across multiple benchmarks, agent architectures, attacks, and defenses show that \method~substantially improves vulnerability exposure and attack-surface coverage compared with existing automated red-teaming baselines, while remaining effective even under strong prompt injection defenses.
View details
The Manager's Job Has Already Changed, Most Companies Haven't Noticed
AI Journal (2026)
Preview abstract
The traditional role of a manager, focused on overseeing people performing tasks with software tools, is undergoing a profound shift due to the rise of autonomous AI systems. Across functions like customer support, sales, and finance, software is no longer just a tool but is increasingly executing work end-to-end. This article examines the implications of this transition for the managerial role, arguing that the manager's job is evolving from an "operator-of-operators" to a "designer-of-boundaries." Key new responsibilities include defining the scope of autonomous systems, codifying implicit human judgment into explicit rules and policies, and designing effective human-in-the-loop escalation paths. This shift also impacts team structures, performance management conversations, and even professional identity, as experienced operators' roles are renegotiated. The article contends that companies must recognize and address these cultural and organizational changes to successfully leverage autonomous systems, and that board-level oversight needs to adapt to this new operational reality. The cultural work of redefining management and supporting the workforce through this transition is paramount and will ultimately determine the long-term value derived from these technological advancements.
View details
Preview abstract
The Private-Use Area (PUA) is an important part of the Unicode standard. It consists of several ranges of Unicode code points with no official character assignments. The PUA is primarily used as a temporary representation mechanism for characters outside the official standard to facilitate text entry and display of orthographies that cannot be adequately represented by other means. The primary downside of PUA is that characters lose their semantics if the pairing with the corresponding display font is broken. Consequently, they cannot be faithfully displayed in the general setting. Large-scale multilingual web corpora inevitably contain PUA code points of unclear provenance. We investigate the distribution of PUA characters within large-scale datasets, using filters for determining PUA tokens of linguistic interest. We analyze the resulting distributions both across scripts and writing systems, and show that PUA-bearing tokens can signal texts from under-represented languages. We explore whether an off-the-shelf large language model (LLM) can classify PUA characters as those that constitute relevant orthographic signals vs. punctuation or other noise. While the proportion of PUA-bearing paragraphs in the original corpora are small, we identify millions of paragraphs, and we argue that such data is still important for the long tail of data-scarce orthographies. Moreover, as a primary Unicode mechanism for poorly represented writing systems, the PUA is here to stay.
View details
Preview abstract
Every team building Large Language Models (LLMs) faces a core challenge: offline benchmarks show performance gains and user engagement rises post deployment, but isolating cause from effect remains difficult. Simultaneous marketing, media coverage, and seasonal demand obscure whether model updates truly drive engagement gains. This paper presents a novel causal estimation approach that leverages non uniform quality improvements across capabilities within a single model version. Because capabilities improve unevenly (e.g., strong gains in coding versus modest gains in writing), users experience varied quality depending on their task distribution. This variation in experienced quality provides causal signal for estimation. We validate this approach on synthetic data with known ground truth. The raw estimator recovers 81% to 88% of the true effect, with the remainder lost to measurement error in usage estimates. Applying an errors in variables disattenuation adjustment (using a test retest reliability ratio mean correlation of 0.861) corrects the estimate to 1.017 (bootstrapped 95% CI: 0.915 to 1.109). By contrast, naive methods fail significantly, recovering only 63% without version controls and 62% using real time rather than frozen usage patterns. Permutation tests confirm the framework distinguishes true causal effects from noise. Sensitivity analysis indicates recovery improves monotonically from 78% to 87% as pre period length increases from 3 to 7 weeks, highlighting a clear tradeoff between sample duration and estimator precision. Seed sensitivity tests further confirm stability across random draws.
View details
Human-AI Interaction for Time-Critical Sensemaking in Missing Persons Investigations
Pola Labedzka
Miri Zilka
Dorian Peters
John Dudley
2026
Preview abstract
Every year an estimated 200,000 people go missing in the UK alone. Missing persons investigations involve challenging time-critical sensemaking tasks based on fragmented data sources. This paper describes a mixed-methods participatory study evaluating data science and AI-driven techniques (summarisation, fact extraction, and data visualisation) for supporting these investigations as part of a human-centered workflow. A series of human-AI interfaces were iteratively designed and tested with search officers and domain experts at Police Scotland. Based on findings, we describe: (1) user and information needs for missing persons investigations; (2) Insights on the benefits and challenges of applying LLM-based techniques in high-risk contexts; and (3) lessons for integrating AI for information and sensemaking tasks in policing more broadly. We highlight that in high-stakes contexts, where accuracy and context-sensitivity are paramount, AI techniques must be balanced with other approaches and designed in close partnership with end-users.
View details
Correct-by-Construction Framework for Robustness Against Divergent Voltage Drops and High Clock Divergence
Prateek Pendyala
Mathew Kaipanatu
2026
Preview abstract
Limitations in Sign-off Methodology:
Traditional STA corner selection 10% lower STA corner from PMIC voltage is selected- design is constantly optimised for 10% lower voltage, thereby failing to build margin against differential drop.
IR aware STA does not account timing path’s geometric imbalances (logic depths), net dominated interconnect skews (net delays & metal layer variation) - all dominant in advanced process nodes. Furthermore, this is workload dependent: fixing IR STA violations does not build margins on unseen vectors.
Frequent Silicon issues due to these gaps:
Low voltage mode scan shift Vmin jumps need to meet slack on paths which become exponentially sensitive to voltage gradients. Even small differential IR drops (capture & launch traversing through contrasting IR hotspot & cool regions) cause catastrophic slack loss
High divergence paths with structural imbalances-where clock paths are net-dominated & are operated at high speeds often fail to meet hold timing, despite good pre-silicon margins due to high interlayer metal-sheet & via resistances in lower process nodes.
Additionally, there is considerable PPA impact -higher dynamic & leakage power in clock & data paths respectively in divergent paths. Our proposed solution aims to address above gaps.
View details
A comparison of machine learning and human graders for glaucoma diagnosis from fundus images for population screening
Thomas RP Taylor
Robert Luben
Laura Meliante
Kelsey V. Stuart
Dun Jack Fu
Michelle P Y Chan
David C Broadway
Andrew Carroll
Ruiqi Hu
Wessel Verburg
Mahantesh I. Biradar
Pearse Keane
Paul J Foster
Anthony Khawaja
Ophthalmology (2026)
Preview abstract
Purpose: To compare the accuracy of vertical cup-disc ratios (VCDR), ascertained by machine
learning (ML) versus human graders, from fundus images for glaucoma detection. This study
utilizes population-based data, with a disease prevalence and case-mix that is closer to a
real-world setting than conventional case-control studies, with the aim of developing
improved glaucoma screening tests.
Design: Cross-sectional analysis of a population-based study.
Participants: 6,304 participants of the EPIC-Norfolk Eye Study with color fundus images
gradable by humans and ML in both eyes.
Methods: VCDR was independently estimated from two-dimensional fundus images of
EPIC-Norfolk Eye Study participants by trained human graders (H-VCDR) and an externally
trained, open access, ML model (ML-VCDR). A neural network trained on 81,830
ophthalmologist-labeled images was used to generate pseudo-labels for over 100,000 UK
Biobank images, on which ML-VCDR was subsequently trained. Glaucoma status was
ascertained by tertiary center specialist examination. Predictive performance of VCDR for
glaucoma status was examined using logistic regression. ML-VCDR estimates were
additionally compared to a popular open-source ML model (AutoMorph) and scanning laser
ophthalmoscopy (Heidelberg Retinal Tomography (HRT)).
Main Outcome Measures: Area Under the Receiver Operated Characteristic Curve (AUROC),
explained variance (McFadden’s pseudo-R2).
Results: Of 6,304 participants (mean age 68 years; 57% women), 696 had glaucoma or
suspect status in at least one eye. For left eyes, H-VCDR and ML-VCDR explained 17% (95% CI
14.7 - 20.4) and 31% (95% CI 27.9 - 33.9) of glaucoma status variance and had an area under
the ROC curve (AUROC) of 79% (95% CI 76.8 - 81.2) and 88% (95% CI 86.3 - 89.1),
respectively. Right eye H-VCDR and ML-VCDR explained 20% (95% CI 16.9 - 22.6) and 35%
(95% CI 32.4 - 37.9) of the variance, and had an AUROC of 81% (95% CI 78.6 - 82.5) and 90%
(95% CI 88.7 - 91.0), respectively. ML-VCDR also performed better than AutoMorph and HRT
at predicting glaucoma status from VCDR estimations.
Conclusions: In this population-based setting, ML far outperformed trained human graders
at predicting specialist-ascertained glaucoma status from fundus images. This provides
promise for ML-supported strategies for glaucoma population screening.
View details
Proactive Heterogeneous Interconnect Arbitration via ML-Aware Compiler Hinting
Prakash Viswalingam
2026
Preview abstract
Modern heterogeneous Systems-on-Chip (SoCs) integrate highly diverse intellectual property (IP) blocks, including latency-sensitive CPUs, Throughput-intensive GPUs, real-time streaming display/camera engines, and massive parallel Neural Processing Units (NPUs/TPUs). Arbitrating access to a shared Network-on-Chip (NoC) and memory subsystem under such diverse traffic profiles expose a critical structural bottleneck. Traditional reactive Quality of Service (QoS) and Dynamic Voltage and Frequency Scaling (DVFS) frameworks incur substantial performance and energy penalties due to phase-locked loop (PLL) lag and transient queue saturation. This paper introduces a Compiler-Assisted Interconnect Hinting (CAIH) framework that eliminates reactive latency penalties by exploiting the deterministic nature of deep learning execution graphs. We propose a lightweight Instruction Set Architecture (ISA) extension (ICH) that allows the deep learning compiler to proactively signal upcoming memory phases, operational intensity shifts, and lookahead cycles to a centralized System QoS (SQoS) arbiter via a dedicated physical sideband channel. Experimental results extracted from cycle-accurate gem5/Garnet simulations demonstrate that our proactive framework reduces average CPU memory latency by 22%, completely eliminates display buffer underflows under heavy concurrent workloads, and achieves up to a 15% reduction in LPDDR6 memory subsystem power.
View details
Regression Test Selection at Scale
Randall Parker
Laiba Mehnaz
Charlie Song
Michelle Wang
Daniel Lee
Brandon Stewart
Ilya Kavalerov
2026
Preview abstract
Core libraries in massive codebases are dependency
hot-spots with high code churn and an immense number of
reverse dependencies. A small code change in these libraries
can impact a vast number of clients, creating a massive blast
radius of costly breakages if a bug or regression is submitted.
To prevent expensive post-submit breakages, it is not feasible to
test global dependencies during the pre-submit stage for each
code change because of resource limitations and the desire for
minimal developer friction.
In this paper, we propose a novel ranking-based approach that
utilizes a hybrid call graph to test global dependencies during the
pre-submit stage. Our method leverages the call graph to select a
per-commit subset of these global dependencies and to generate
inexpensive graph structural features that allow our machine
learning model to learn the complex relationships between library
code changes and client tests to predict failures. Evaluating our
approach on 10 high-impact core libraries with an average test
suite size of 220,000, we demonstrate that a fixed budget of 2,000
tests per commit, representing just 0.9% of the total execution
cost, effectively catches regressions for 40% of failing commits.
View details
Unveiling the Global Landscape of Android Security Updates
Haiyun Deng
Abbas Acar
Esteban Luques
Harun Oz
Ahmet Aris
Selcuk Uluagac
IEEE Transactions on Dependable and Secure Computing (2026)
Preview abstract
Android is the world’s leading mobile operating
system, with over three billion active devices. Detecting vulnerabilities and ensuring timely patch deployment are critical to
maintaining security. The Android Open Source Project (AOSP)
has enhanced the transparency of security updates through Security Patch Levels. However, challenges related to update speed
and availability persist. In 2022, Google reported that half of the
zero-day vulnerabilities discovered in the wild were variations of
vulnerabilities that had already been patched. Recent research
mainly highlights delays in update distribution, often attributing
them to fragmentation and focusing primarily on flagship devices
or limited time-frames. Our approach takes a device-centric
perspective to investigate Android update patterns, analyzing
567K security update records from 2014 to 2024, covering 904
distinct devices from six key Original Equipment Manufacturers
(OEMs) across 98 countries. Our extensive analysis revealed
notable differences in update release timing across OEMs, device types, and regions. Our study also examines documented
vulnerabilities and weaknesses, while assessing OEM compliance
with Android security guidelines. Our study shows that ∼89.7%
of vulnerabilities on unpatched Android devices are exploitable
without user interaction and with low attack complexity. We
also identified delays linked to fragmentation and OEM-specific
challenges, and provide actionable insights for improvement.
View details
Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT
Michelangelo Conserva
Alex Wilson
Anna Michalak
Phil Brodrick
Andrew Thorpe
Proceedings of the National Academy of Sciences (2026)
Preview abstract
Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system inefficiencies. Global tracking with imaging spectroscopy is just becoming feasible, but still largely relies on manual tracking. Here we present the Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) model, an end-to-end vision transformer framework that advances the state of the practice by directly utilizing the complete radiance spectrum from the Earth Surface Mineral Dust Source Investigation (EMIT) instrument to jointly retrieve methane enhancements across all pixels within a scene. This approach joins spectral information content - where the methane signature resides - with essential spatial context to significantly lower detection limits. MAPL-EMIT simultaneously supports quantification, plume delineation, and source localization, even for multiple overlapping plumes. The model was trained on 3.6 million physics-based synthetic plumes injected into global EMIT radiance data. Evaluation against synthetic observations confirms the model’s ability to identify plumes with high recall and precision and to capture weaker plumes relative to existing matched-filter approaches. On real-world benchmarks, MAPL-EMIT captures 79% of known hand-annotated NASA L2B plume complexes across a test set of 1084 EMIT granules, while identifying twice as many plausible plumes than identified by human analysts. Further validation against coincident airborne data, top-emitting landfills, and controlled release experiments confirms the models efficacy at identifying previously uncaptured sources. By incorporating model-generated metrics such as spectral fit scores and estimated noise levels, the framework can further limit false-positive rates. Overall, MAPL-EMIT enables high-throughput implementation on the full EMIT data catalog, shifting methane monitoring from labor-intensive workflows to a rapid, scalable paradigm for facility-level accountability.
View details
