Data Mining and Modeling
The proliferation of machine learning means that learned classifiers lie at the core of many products across Google. However, questions in practice are rarely so clean as to just to use an out-of-the-box algorithm. A big challenge is in developing metrics, designing experimental methodologies, and modeling the space to create parsimonious representations that capture the fundamentals of the problem. These problems cut across Google’s products and services, from designing experiments for testing new auction algorithms to developing automated metrics to measure the quality of a road map.
Data mining lies at the heart of many of these questions, and the research done at Google is at the forefront of the field. Whether it is finding more efficient algorithms for working with massive data sets, developing privacy-preserving methods for classification, or designing new machine learning approaches, our group continues to push the boundary of what is possible.
Recent Publications
Fine-Grained Table Retrieval for Open-Domain Tabular Question Answering
Xingyu Ji
Wojciech Kosiuk
Madelon Hulsebos
Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026), Association for Computational Linguistics
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This work introduces a fine-grained table retrieval framework for grounding large
language models in heterogeneous, open-domain relational data. Instead of encoding a query as a single vector, the approach decomposes natural
language queries into semantic components and embeds each independently, enabling
more precise matching of compositional query intent. These representations are used in a staged retrieval pipeline with component-level
search, connectivity-aware grouping, and reranking. Experiments on three TARGET benchmark corpora show consistent improvements in capped recall@k and stronger alignment between query intent and tabular structure over dense retrieval baselines, particularly for longer and more complex queries and when using lightweight embedding models.
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Human-Computer Interaction research and design pedagogy rely on idealized process models, such as the Double Diamond, to describe how user experiences are designed. These models assume an orderly, linear design process that, while easy to understand, fails to capture the iterative and collaborative reality of professional practice. A few qualitative studies have successfully captured this complexity -- still, they often suffer from retrospective narrative smoothing and lack systemic scale. To understand how design unfolds in real products, we analyzed historical snapshots of 102 Figma files from a multi-national technology company and investigated the true trajectories of the design process at scale. Our analysis reveals that while the established process models might be applicable, the operational details are highly non-linear. Rather than a straight line from ideation toward completion, design advances are repeatedly reset to the ideation stage as feedback is received. We argue that by treating design files as operational telemetry, the industry can move beyond abstract frameworks to build practices and collaborative tools that support the non-linear realities of modern product development.
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This paper outlines a grammar of data analysis, as distinct from grammars of data manipulation. The primitives of this grammar are metrics and dimensions. We describe a Python implementation of this grammar called Meterstick, which is agnostic to the underlying data source, which may be a DataFrame or a SQL database.
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Autonomous Causal Inference Using Artificial Intelligence Agents
Teginder Singh
Justin Chen
Kiran Dattani
Sean Hamill
Jake Van Bochove
Technical Disclosure Commons (2025)
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Post-market drug-safety procedures, known as pharmacovigilance, require a team of skilled physicians or epidemiologists to manually investigate each adverse event. This disclosure describes artificial intelligence (AI) agent-based, cloud computing techniques that automate the end-to-end cognitive workflow of investigating potential adverse drug events (ADEs). Moving beyond current techniques, which only perform statistical signal detection, the described techniques enable the determination of biological causality. Upon receiving a statistical signal of an adverse event, plausible biological hypotheses that can explain the signal are autonomously generated. A set of specialized software agents are dispatched to forage for evidence across disparate data sources (real-world evidence platforms, scientific literature, genomic databases, etc.). The results generated by the agents are synthesized into findings that score the likelihood of a causal link. The final output is a detailed, auditable causality dossier that enables human safety experts to make faster, better-informed decisions.
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First Passage Percolation with Queried Hints
Kritkorn Karntikoon
Aaron Schild
Yiheng Shen
Ali Sinop
AISTATS (2024)
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Optimization problems are ubiquitous throughout the modern world. In many of these applications, the input is inherently noisy and it is expensive to probe all of the noise in the input before solving the relevant optimization problem. In this work, we study how much of that noise needs to be queried in order to obtain an approximately optimal solution to the relevant problem. We focus on the shortest path problem in graphs, where one may think of the noise as coming from real-time traffic. We consider the following model: start with a weighted base graph $G$ and multiply each edge weight by an independently chosen, uniformly random number in $[1,2]$ to obtain a random graph $G'$. This model is called \emph{first passage percolation}. Mathematicians have studied this model extensively when $G$ is a $d$-dimensional grid graph, but the behavior of shortest paths in this model is still poorly understood in general graphs. We make progress in this direction for a class of graphs that resembles real-world road networks. Specifically, we prove that if the geometric realization of $G$ has constant doubling dimension, then for a given $s-t$ pair, we only need to probe the weights on $((\log n) / \epsilon)^{O(1)}$ edges in $G'$ in order to obtain a $(1 + \epsilon)$-approximation to the $s-t$ distance in $G'$. We also demonstrate experimentally that this result is pessimistic -- one can even obtain a short path in $G'$ with a small number of probes to $G'$.
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Probe-Based Study of Traffic Variability for the Design of Traffic Light Plans
Ori Rottenstreich
Eliav Buchnik
Shai Ferster
Tom Kalvari
Dan Karliner
Omer Litov
Nitzan Tur
Danny Veikherman
Jack Haddad
COMSNETS 2024, https://www.comsnets.org/ (2024)
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Computing efficient traffic signal plans is often based on the amount of traffic in an intersection, its distribution over the various intersection movements and hours as well as on performance metrics such as traffic delay. In their simple and typical form, plans are fixed in the same hour over weekdays. This allows low operation costs without the necessity for traffic detection and monitoring tools. A critical factor in the potential efficiency of such plans is the similarity of traffic patterns over the days along each of the intersection movements. In this paper, we study traffic variability and propose simple metrics to measure it based on traffic volume and traffic delay. We propose an automatic probe data-based method, for city-wide estimation of traffic variability. We discuss how such measures can be used for signal planning such as an indication of which intersections can benefit from dynamic but expensive traffic detection tools or in selecting plan resolution. Likewise, we discuss various methods to mitigate the impact of such variability. We demonstrate the framework based on real traffic statistics to study the traffic variability in the city of Haifa along its 162 intersections.
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