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 11135 publications
Type-Aware Ranking of Urban Similarity from Aerial Imagery
Idan Kligvasser
Yotam Intrator
Yuval Desheh
Aviad Barzilai
Niv Efron
Ehud Rivlin
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops (2026), pp. 821-829
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Estimating and ranking cross-city similarity from aerial imagery is a fundamental challenge in remote sensing and geospatial representation learning. Urban environments differ widely in road layout, marking conventions, and infrastructure design, yet standard visual representations often struggle to disentangle these meaningful structural variations from superficial appearances. In this work, we propose a type-aware contrastive learning framework that measures urban similarity by explicitly modeling distinct infrastructure elements. Leveraging open-vocabulary retrieval, we construct a globally diverse dataset of road-related features, such as intersections, crosswalks, and bus lanes, and train a type-conditioned Vision Transformer that fuses visual features with CLIP-derived semantic embeddings. Crucially, we introduce an adaptive per-type contrastive loss that dynamically emphasizes infrastructure categories with high discriminative power while down-weighting less informative types. To quantify city-level similarity, we aggregate per-type cosine similarities via a lightweight classifier to generate a global city-to-city similarity matrix. Experiments demonstrate that this type-aware approach significantly improves clustering quality and successfully generalizes to unseen cities, establishing a scalable, interpretable foundation for comparative urban analysis.
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`It’s still abuse’: Community attitudes and perceptions on AI-generated image-based sexual abuse
Nicola Henry
Gemma Beard
Lisa Given
Information, Communication, & Society (2026)
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There are growing concerns about AI-generated image-based sexual abuse (AI-IBSA), also known as nonconsensual sexualized ′deepfakes.′ Empirical research on AI-IBSA, however, remains very limited. This study surveyed 7231 respondents across Australia, the United Kingdom, and the United States to investigate community attitudes and perceptions on AI-IBSA. Through a vignette study, we explored the relationship between public familiarity with AI-IBSA, normative concerns about consent, and context-dependent judgments that vary based on the target's identity relational status, and how the content was used. Our findings reveal strong condemnation of AI-IBSA, yet respondents demonstrated low familiarity with the technology and their views varied depending on particular contexts. AI-IBSA targeting intimate partners was viewed as more unacceptable than targeting celebrities, and content created solely for personal use was seen as less unacceptable than content intended for distribution. The study highlights the need for approaches that go beyond technical fixes and punitive measures, advocating for a multifaceted response that integrates ethical data governance, digital sexual literacy, and restorative justice approaches.
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Usability Hasn’t Peaked: Exploring How Expressive Design Overcomes the Usability Plateau
Alyssa Sheehan
Bianca Gallardo
Ying Wang
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26), April 13–17, 2026, Barcelona, Spain (2026)
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Critics have argued that mobile usability has largely been optimized, and that only incremental gains are possible. We set out to explore if the newest generation of design systems, which promote greater flexibility and a return to design basics, could produce substantially more usable designs while maintaining or increasing aesthetic judgments. Through a study with 48 diverse participants completing tasks in 10 different applications, we found that in designs created following Material 3 Expressive guidelines, users fixated on the correct screen element for a task 33% faster, completed tasks 20% faster, and rated experiences more positively compared to versions designed using the previous Material design system. These improvements in performance and aesthetic ratings challenge the premise of a usability plateau and show that mobile usability has not peaked. We illustrate specific opportunities to make mobile experiences more usable by returning to design fundamentals while highlighting risks of added flexibility.
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The advent of 3D Gaussian Splatting has revolutionized graphics rendering by offering high visual quality and fast rendering speed. However, training large-scale scenes at high quality remains challenging due to the substantial memory demands required to store Gaussians and optimizer states. To address these limitations, we propose GS-Offload, fast and memory-efficient training system for 3D Gaussian Splatting. GS-Offload stores Gaussians and optimizer states in host memory and selectively transfer only the necessary data to GPU memory on demand, significantly reducing GPU memory usage. With carefully designed software pipelining and CPU-side optimizer acceleration, GS-Offload achieves training speed near that of GPU-only setups, while significantly lowering GPU memory demands.
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ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding
Sunny Rajagopalan
Alireza Golestaneh
Shubhra Chandra
Min Zhou
Jonathan Vronsky
Songbai Yan
2026
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We present ALF (Advertiser Large Foundation model), a multi-modal transformer architecture for understanding advertiser behavior and intent across text, image, video and structured data modalities. Through contrastive learning and multi-task optimization, ALF creates unified advertiser representations that capture both content and behavioral patterns. Our model achieves state-of-the-art performance on critical tasks including fraud detection, policy violation identification, and advertiser similarity matching. In production deployment, ALF reduces false positives by 90\% while maintaining 99.8\% precision on abuse detection tasks. The architecture's effectiveness stems from its novel combination of multi-modal transformations, intersample attention mechanism, spectrally normalized projections, and calibrated probabilistic outputs.
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The Ontic-Epistemic Distinction: Implications for Robust Machine Intelligence
Shreya Ishita
Master's Thesis (2026) (to appear)
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The current pursuit of robust Machine Intelligence is largely predicated on a substrate independent, functionalist view of cognition, where sufficiently large syntactic processing is expected to eventually yield semantic understanding. This paper explores the ontological distinctions between these computational frameworks and biological cognition, specifically regarding the emergence of robustness. By analyzing phenomena such as the "reversal curse" and performance on novel reasoning benchmarks (e.g., ARC-AGI), I examine whether current limitations are transient artifacts of scale or indicative of a distinct architectural category.
Synthesizing Stevan Harnad’s "Symbol Grounding Problem" with Evan Thompson’s framework of Intrinsic Normativity in autopoietic systems, I argue that true generality requires "Sense-Making", a process distinct from "Information Processing", whereby an agent’s internal states are causally coupled with its environment via survival or system wide stakes. Without this intrinsic normativity, machines may remain epistemic instruments rather than ontic agents. By defining this "Ontic Gap," this paper offers a theoretical lens for evaluating AI safety and governance, moving beyond behavioral simulation to address the structural conditions of understanding.
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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.
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Agentic AI Infrastructure in Practice: Learn These Key Hurdles to Deploy Production AI Agents Efficiently
https://swisscognitive.ch/ (2026)
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The emergence of Agentic AI—autonomous systems capable of reasoning, decision-making, and multi-step execution—represents a paradigm shift in enterprise technology. Moving beyond simple generative tasks, these agents offer the potential to solve long-standing industry pain points, with over 90% of enterprises planning integration within the next three years. However, the transition from successful proof-of-concept (PoC) to a resilient, production-grade system presents significant hurdles.
This article categorizes these challenges into three primary domains:
Technical and Engineering Hurdles: Issues such as "entangled workflows" that complicate debugging, the struggle to maintain output quality and mitigate hallucinations, and the unpredictability caused by shifting underlying models or data sources.
People, Process, and Ecosystem Hurdles: The high operational costs and unclear ROI of large models, the necessity of a new "Agent Ops" skillset, the complexity of integrating agents with disparate enterprise systems, and a rapidly evolving regulatory landscape.
The Pace of Change and Security risks: The technical debt incurred by shifting software frameworks and the expanded attack surface created by autonomous agents.
The article concludes that successful deployment requires a shift from informal "vibe-testing" to rigorous engineering discipline. By adopting code-first frameworks, establishing robust evaluation metrics (KPIs), and prioritizing functional deployment over theoretical optimization, organizations can effectively manage the lifecycle of Agentic AI and realize its transformative business value.
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A Computer Vision Problem in Flatland
Erin Connelly
Annalisa Crannell
Timothy Duff
Rekha R. Thomas
SIAM Journal on Applied Algebra and Geometry, 10 (2026), pp. 14-45
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When is it possible to project two sets of labeled points of equal cardinality lying in a pair of projective planes to the same image on a projective line? We give a complete answer to this question, obtaining the following results. We first show that such a pair of projections exist if and only if the two point sets are themselves images of a common point set in projective space. Moreover, we find that for generic pairs of point sets, a common projection exists if and only if their cardinality is at most seven. In these cases, we give an explicit description of the loci of projection centers that enable a common image.
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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)
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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.
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Phoenix: Rowhammer Attacks on DDR5 with Self-Correcting Synchronization
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Michele Marazzi
Kaveh Razavi
Salman Qazi
Diego Meyer
Patrick Jattke
IEEE Security & Privacy (S&P) (2026)
On-the-Fly OVD Adaptation with FLAME: Few-shot Localization via Active Marginal-Samples Exploration
Yehonathan Refael
Amit Aides
Aviad Barzilai
Vered Silverman
Bolous Jaber
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops (2026), pp. 886-894
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Open-vocabulary object detection (OVD) models offer remarkable flexibility applications by enabling object detection from arbitrary text queries. Still, the zero-shot performance of the pre-trained models is hampered by the inherent semantic ambiguity of natural language, result to low precision, leading to insufficient crucial downstream applications. For instance, in the remote sensing (RS) domain, a query for "ship" can yield varied and contextually irrelevant results. To address this, for real time applications, we propose a novel cascaded architecture that synergizes the broad capabilities of a large, pre-trained OVD model with a lightweight, few-shot classifier. Our approach utilizes the frozen weights of the zero-shot model to generate initial, high-recall object-embedding proposals, which are then refined by a compact classifier trained in real-time on a handful of user-annotated examples. The core of our contribution is an efficient one step active learning strategy for selecting the most informative samples for user annotation. Our method identifies (extremely) small amount of an uncertain candidates near the theoretical decision boundary using density estimation and then applies clustering to ensure a diverse training set. This targeted sampling enables our cascaded system to elevate performance on standard remote sensing benchmarks. Our work thus presents a practical and resource-efficient framework for adapting foundational models to specific user needs, drastically reducing annotation overhead while achieving high accuracy without costly full-model fine-tuning.
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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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Audio Description ( AD) provides essential access to visual media for blind and low vision ( BLV) audiences. Yet current AD production tools remain largely inaccessible to BLV video creators, who possess valuable expertise but face barriers due to visually- driven interfaces. We present ADCanvas, a multimodal authoring system that supports non- visual control
over audio description ( AD) creation. ADCanvas combines conversational interaction with keyboard- based playback control and a plain- text, screen reader–
accessible editor to support end- to- end AD authoring and visual question answering ( VQA). Combining screen- reader- friendly controls with a multimodal
LLM agent, ADCanvas supports live VQA, script generation, and AD modification. Through a user study with 12 BLV video creators, we find that users adopt
the conversational agent as an informational aide and drafting assistant, while maintaining agency through verification and editing. For example, participants
saw themselves as curators who received information from the model and filtered it down for their audience. Our findings offer design implications for
accessible media tools, including precise editing controls, accessibility support for creative ideation, and configurable rules for human- AI collaboration.
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Modern user interfaces are complex composites, with elements originating from various sources, such as the operating system, apps, a web browser, or websites. Many security and privacy models implicitly depend on users correctly identifying an element's source, a concept we term ''surface attribution.'' Through two large-scale vignette-based surveys (N=4,400 and N=3,057), we present the first empirical measurement of this ability.
We find that users struggle, correctly attributing UI source only 55% of the time on desktop and 53% on mobile. Familiarity and strong brand cues significantly improve accuracy, whereas UI positioning, a long-held security design concept especially for browsers, has minimal impact. Furthermore, simply adding a ''Security & Privacy'' brand cue to Android permission prompts failed to improve attribution. These findings demonstrate a fundamental gap in users' mental models, indicating that relying on them to distinguish trusted UI is a fragile security paradigm.
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