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 11092 publications
Preview abstract
Semantic data models express high-level business concepts and metrics, capturing the business logic needed to query a database correctly. Most data modeling solutions are built as layers above SQL query engines, with bespoke query languages or APIs. The layered approach means that semantic models can’t be used directly in SQL queries. This paper focuses on an open problem in this space – can we define semantic models in SQL, and make them naturally queryable in SQL?
In parallel, graph query is becoming increasingly popular, including in SQL. SQL/PGQ extends SQL with an embedded subset of the GQL graph query language, adding property graph views and making graph traversal queries easy.
We explore a surprising connection: semantic data models are graphs, and defining graphs is a data modeling problem. In both domains, users start by defining a graph model, and need query language support to easily traverse edges in the graph, which means doing joins in the underlying data.
We propose some useful SQL extensions that make it easier to use higher-level data model abstractions in queries. Users can define a “semantic data graph” view of their data, encapsulating the complex business logic required to query the underlying tables correctly. Then they can query that semantic graph model easily with SQL.
Our SQL extensions are useful independently, simplifying many queries – particularly, queries with joins. We make declared foreign key relationships usable for joins at query time – a feature that seems obvious but is notably missing in standard SQL.
In combination, these extensions provide a practical approach to extend SQL incrementally, bringing semantic modeling and graph query together with the relational model and SQL.
View details
ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding
Sunny Rajagopalan
Alireza Golestaneh
Shubhra Chandra
Min Zhou
Jonathan Vronsky
Songbai Yan
2026
Preview abstract
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.
View details
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
Preview abstract
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.
View details
Preview abstract
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.
View details
`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)
Preview abstract
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.
View details
Preview abstract
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.
View details
Who Controls the Curriculum for AI? The Limits of Participatory Design for Educational AI
Michael Madaio
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
How many T gates are needed to approximate an arbitrary n-qubit quantum state to within
a given precision ϵ? Improving prior work of Low, Kliuchnikov and Schaeffer, we show that the
optimal asymptotic scaling is Θ(sqrt{2^n log(1/ε)} + log(1/ε)) if we allow an unlimited number of ancilla qubits. We also show that this is the optimal T-count for implementing an arbitrary
diagonal n-qubit unitary to within error ϵ. We describe an application to batched synthesis of
single-qubit unitaries: we can approximate a tensor product of m = O(log log(1/ϵ)) arbitrary
single-qubit unitaries to within error ϵ with the same asymptotic T-count as is required to
approximate just one single-qubit unitary.
View details
CrossCheck: Input Validation for WAN Control Systems
Rishabh Iyer
Isaac Keslassy
Sylvia Ratnasamy
Networked Systems Design and Implementation (NSDI) (2026) (to appear)
Preview abstract
We present CrossCheck, a system that validates inputs to the Software-Defined Networking (SDN) controller in a Wide Area Network (WAN). By detecting incorrect inputs—often stemming from bugs in the SDN control infrastructure—CrossCheck alerts operators before they trigger network outages.
Our analysis at a large-scale WAN operator identifies invalid inputs as a leading cause of major outages, and we show how CrossCheck would have prevented those incidents. We deployed CrossCheck as a shadow validation system for four weeks in a production WAN, during which it accurately detected the single incident of invalid inputs that occurred while sustaining a 0% false positive rate under normal operation, hence imposing little additional burden on operators. In addition, we show through simulation that CrossCheck reliably detects a wide range of invalid inputs (e.g., detecting demand perturbations as small as 5% with 100% accuracy) and maintains a near-zero false positive rate for realistic levels of noisy, missing, or buggy telemetry data (e.g., sustaining zero false positives with up to 30% of corrupted telemetry data).
View details
Preview abstract
Text-to-image generative models have demonstrated great performance in generating realistic images. These generations are assumed to reflect a deep understanding of visual scenes. One interesting question is whether these models can possess a zero/few shot generalization capabilities that are known from humans. For example, a human can see an example of a new object and a word associated with this object, use their knowledge in a highly general way to recognize or imagine this novel object in a completely different setting or context. In this work, we are interested in testing whether text-image models can possess this same capability. In this work, we would like to test the hypothesis that text-to-image models may learn familiar objects better than novel objects. We use prompt tuning methods to learn those novel concepts while keeping the text-image models fixed. We prompt tune the model as well to learn familiar concepts, and evaluate the generalization ability for novel objects compared to familiar objects by running generation in different contexts/environments. In addition, instead of initializing the embedding vectors with some similar concepts, we use randomly initialized embedding vectors for both familiar and novel objects. Our human-survey evaluation results demonstrates that in some settings text-image models learn familiar objects better than novel objects.
View details
StreetReaderAI: Making Street View Accessible Using Context-Aware Multimodal AI
Alex Fiannaca
Nimer Jaber
Victor Tsaran
Proceedings of the 2025 ACM Symposium on User Interface Software and Technology (UIST'25) (to appear)
Preview abstract
Interactive streetscape mapping tools such as Google Street View (GSV) and Meta Mapillary enable users to virtually navigate and experience real-world environments via immersive 360° imagery but remain fundamentally inaccessible to blind users. We introduce StreetReaderAI, the first-ever accessible street view tool, which combines context-aware, multimodal AI, accessible navigation controls, and conversational speech. With StreetReaderAI, blind users can virtually examine destinations, engage in open-world exploration, or virtually tour any of the over 220 billion images and 100+ countries where GSV is deployed. We iteratively designed StreetReaderAI with a mixed-visual ability team and performed an evaluation with eleven blind users. Our findings demonstrate the value of an accessible street view in supporting POI investigations and remote route planning. We close by enumerating key guidelines for future work.
View details
Preview abstract
Large Language Models (LLMs) have demonstrated impressive capabilities across a range of natural language processing tasks. In particular, improvements in reasoning abilities and the expansion of context windows have opened new avenues for leveraging these powerful models.
NL2SQL is challenging in that the natural language question is inherently ambiguous, while the SQL generation requires a precise understanding of complex data schema and semantics. One approach to this semantic ambiguous problem is to provide more and sufficient contextual information.
In this work, we explore the performance and the latency trade-offs of the extended context window (a.k.a., long context) offered by Google's state-of-the-art LLM (\textit{gemini-1.5-pro}).
We study the impact of various contextual information, including column example values, question and SQL query pairs, user-provided hints, SQL documentation, and schema. To the best of our knowledge, this is the first work to study how the extended context window and extra contextual information can help NL2SQL generation with respect to both accuracy and latency cost.
We show that long context LLMs are robust and do not get lost in the extended contextual information. Additionally, our long-context NL2SQL pipeline based on Google's \textit{gemini-pro-1.5} achieve a strong performance with 67.41\% on BIRD benchmark (dev) without finetuning and expensive self-consistency based techniques.
View details
Beyond Digital Literacy: Building Youth Digital Resilience Through Existing “Information Sensibility” Practices
Mia Hassoun
Ian Beacock
Todd Carmody
Beth Goldberg
Devika Kumar
Laura Murray
Rebekah Park
Behzad Sarmadi
Social Sciences Journal, 14(4) (2025)
Preview abstract
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.
View details
Linear Elastic Caching via Ski Rental
Todd Lipcon
Conference on Innovative Data Systems Research (2025)
Preview abstract
In this work we study the Linear Elastic Caching problem, where
the goal is to minimize the total cost of a cache inclusive of not
just its misses, but also its memory footprint integrated over time.
We demonstrate a theoretical connection to the classic ski rental
problem and propose a practical algorithm that combines online
caching algorithms with ski rental policies. We also introduce a
lightweight machine learning-based algorithm for ski rental that
is optimized for production workloads and is easy to integrate
within existing database systems. Evaluations on both production
workloads in Google Spanner and publicly available traces show
that the proposed elastic caching approach can significantly reduce
the total cache cost compared to traditional fixed-size cache policies.
View details
Pragmatic Fairness: Evaluating ML Fairness Within the Constraints of Industry
Jessie Smith
Michael Madaio
Robin Burke
Casey Fiesler
2025
Preview abstract
Machine learning (ML) fairness evaluation in real-world, industry settings presents unique challenges due to business-driven constraints that influence decision-making processes. While prior research has proposed fairness frameworks and evaluation methodologies, these approaches often focus on idealized conditions and may lack consideration for the practical realities faced by industry practitioners. To understand these practical realities, we conducted a semi-structured interview study with 21 experts from academia and industry specializing in ML fairness. Through this study, we explore three constraints of ML fairness evaluation in industry— balancing competing interests, lacking power/access, and getting buy-in—and how these constraints lead to satisficing, seeking satisfactory rather than ideal outcomes. We define the path from these constraints to satisficing as pragmatic fairness. Using recommender systems as a case study, we explore how practitioners navigate these constraints and highlight actionable strategies to improve fairness evaluations within these business-minded boundaries. This paper provides practical insights to guide fairness evaluations in industry while also showcasing how the FAccT community can better align research goals with the operational realities of practitioners.
View details
