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 10376 publications
Linear Elastic Caching via Ski Rental
Todd Lipcon
The biennial Conference on Innovative Data Systems Research (2025)
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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.
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We introduce sum-of-squares spectral amplification (SOSSA), a framework for improving quantum simulation algorithms relevant to low-energy problems. SOSSA first represents the Hamiltonian as a sum-of-squares and then applies spectral amplification to amplify the low-energy spectrum. The sum-of-squares representation can be obtained using semidefinite programming. We show that SOSSA can improve the efficiency of traditional methods in several simulation tasks involving low-energy states. Specifically, we provide fast quantum algorithms for energy and phase estimation that improve over the state-of-the-art in both query and gate complexities, complementing recent results on fast time evolution of low-energy states. To further illustrate the power of SOSSA, we apply it to the Sachdev-Ye-Kitaev model, a representative strongly correlated system, where we demonstrate asymptotic speedups by a factor of the square root of the system size. Notably, SOSSA was recently used in [G.H. Low \textit{et al.}, arXiv:2502.15882 (2025)] to achieve state-of-art costs for phase estimation of real-world quantum chemistry systems.
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YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks
Saptarashmi Bandyopadhyay
Vikas Bahirwani
Lavisha Aggarwal
Bhanu Guda
Lin Li
Andrea Colaco
2025
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Multimodal AI Agents are AI models that have the capability of interactively and cooperatively assisting human users to solve day-to-day tasks. Augmented Reality (AR) head worn devices can uniquely improve the user experience of solving procedural day-to-day tasks by providing egocentric multimodal (audio and video) observational capabilities to AI Agents. Such AR capabilities can help the AI Agents see and listen to actions that users take which can relate to multimodal capabilities of human users. Existing AI Agents, either Large Language Models (LLMs) or Multimodal Vision-Language Models (VLMs) are reactive in nature, which means that models cannot take an action without reading or listening to the human user's prompts. Proactivity of AI Agents, on the other hand, can help the human user detect and correct any mistakes in agent observed tasks, encourage users when they do tasks correctly, or simply engage in conversation with the user - akin to a human teaching or assisting a user. Our proposed YET to Intervene (YETI) multimodal Agent focuses on the research question of identifying circumstances that may require the Agent to intervene proactively. This allows the Agent to understand when it can intervene in a conversation with human users that can help the user correct mistakes on tasks, like cooking, using Augmented Reality. Our YETI Agent learns scene understanding signals based on interpretable notions of Structural Similarity (SSIM) on consecutive video frames. We also define the alignment signal which the AI Agent can learn to identify if the video frames corresponding to the user's actions on the task are consistent with expected actions. These signals are used by our AI Agent to determine when it should proactively intervene. We compare our results on the instances of proactive intervention in the HoloAssist multimodal benchmark for an expert agent guiding an user agent to complete procedural tasks.
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Record Number of Members Visit U.S. Congress to Talk Tech Policy
IEEE Spectrum (2025)
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This IEEE Spectrum article reflects on advocacy for U.S. technological leadership during my Congressional visit through IEEE-USA. Leading an expert group of other distinguished IEEE members, we urged lawmakers to support critical initiatives. Key priorities included sustained funding for federal research institutions like NIST, NASA, and the NSF, reauthorizing the SBIR/STTR programs vital for small business innovation, and passing the CREATE AI Act to democratize AI resources by establishing the National AI Research Resource (NAIRR).
We also emphasized strengthening the STEM talent pipeline through the CHIPS and Science Act and expanding high-skilled immigrant visas. We highlighted rapid AI advancements, such as autonomous vehicles, the surge in FDA-approved AI based medical devices, as underscoring the need for these strategic investments and policy actions. The article conveys a sense of urgency, calling for concrete congressional action to ensure the U.S. maintains its technological edge while also sharing my personal experiences.
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Quartic Quantum Speedups for Planted Inference Problems
Alexander Schmidhuber
Ryan O'Donnell
Physical Review X, 15 (2025), pp. 021077
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We describe a quantum algorithm for the Planted Noisy kXOR problem (also known as sparse Learning Parity with Noise) that achieves a nearly quartic (4th power) speedup over the best known classical algorithm while also only using logarithmically many qubits. Our work generalizes and simplifies prior work of Hastings, by building on his quantum algorithm for the Tensor Principal Component Analysis (PCA) problem. We achieve our quantum speedup using a general framework based on the Kikuchi Method (recovering the quartic speedup for Tensor PCA), and we anticipate it will yield similar speedups for further planted inference problems. These speedups rely on the fact that planted inference problems naturally instantiate the Guided Sparse Hamiltonian problem. Since the Planted Noisy kXOR problem has been used as a component of certain cryptographic constructions, our work suggests that some of these are susceptible to super-quadratic quantum attacks.
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Many AI applications of interest require specialized multi-modal models. Yet, relevant data for training these models is inherently scarce. Human annotation is prohibitively expensive, error-prone, and time-consuming. Meanwhile, existing synthetic data generation methods often rely on manual prompts, evolutionary algorithms, or extensive seed data from the target distribution - limiting scalability and control. In this paper, we introduce Simula, a novel, seedless framework that balances global and local reasoning to generate synthetic datasets. We utilize taxonomies to capture a global coverage space and use a series of agentic refinements to promote local diversity and complexity. Our approach allows users to define desired dataset characteristics through an explainable and controllable process, without relying on seed data. This unlocks new opportunities for developing and deploying AI in domains where data scarcity or privacy concerns are paramount.
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Oculomics: Current Concepts and Evidence
Zhuoting Zhu
Yueye Wang
Ziyi Qi
Wenyi Hu
Xiayin Zhang
Siegfried Wagner
Yujie Wang
An Ran Ran
Joshua Ong
Ethan Waisberg
Mouayad Masalkhi
Alex Suh
Yih Chung Tham
Carol Y. Cheung
Xiaohong Yang
Honghua Yu
Zongyuan Ge
Wei Wang
Bin Sheng
Andrew G. Lee
Alastair Denniston
Peter van Wijngaarden
Pearse Keane
Ching-Yu Cheng
Mingguang He
Tien Yin Wong
Progress in Retinal and Eye Research (2025)
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The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics- the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging (“hardware”); 2) the availability of large studies to interrogate associations (“big data”); 3) the development of novel analytical methods, including artificial intelligence (AI) (“software”). Oculomics offers an opportunity to enhance our understanding of the interplay between the eye and the body, while supporting development of innovative diagnostic, prognostic, and therapeutic tools. These advances have been further accelerated by developments in AI, coupled with large-scale linkage datasets linking ocular imaging data with systemic health data. Oculomics also enables the detection, screening, diagnosis, and monitoring of many systemic health conditions. Furthermore, oculomics with AI allows prediction of the risk of systemic diseases, enabling risk stratification, opening up new avenues for prevention or individualized risk prediction and prevention, facilitating personalized medicine. In this review, we summarise current concepts and evidence in the field of oculomics, highlighting the progress that has been made, remaining challenges, and the opportunities for future research.
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Mufu: Multilingual Fused Learning for Low- Resource Translation with LLM
Zheng Lim
Honglin Yu
Trevor Cohn
International Conference on Learning Representations (ICLR) 2025
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Multilingual large language models (LLMs) are great translators, but this is largely limited to high-resource languages. For many LLMs, translating in and out of low-resource languages remains a challenging task. To maximize data efficiency in this low-resource setting, we introduce Mufu, which includes a selection of automatically generated multilingual candidates and an instruction to correct inaccurate translations in the prompt. Mufu prompts turn a translation task into a postediting one, and seek to harness the LLM's reasoning capability with auxiliary translation candidates, from which the model is required to assess the input quality, align the semantics cross-lingually, copy from relevant inputs and override instances that are incorrect. Our experiments on En-XX translations over the Flores-200 dataset show LLMs finetuned against Mufu-style prompts are robust to poor quality auxiliary translation candidates, achieving performance superior to NLLB 1.3B distilled model in 64% of low- and very-low-resource language pairs. We then distill these models to reduce inference cost, while maintaining on average 3.1 chrF improvement over finetune-only baseline in low-resource translations.
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The global adoption of Large Language Models (LLMs) in healthcare shows promise for enhancing clinical workflows and improving patient outcomes. However, Automatic Speech
Recognition (ASR) errors in critical medical entities remain a significant challenge. These
errors can lead to severe consequences if undetected. This study investigates the prevalence and impact of ASR errors in medical transcription across Africa, Europe, and North America. By examining variations in accented English across three continents, we analyze the impact of regional speech patterns on ASR performance. Our research quantifies both the potential and limitations of LLMs in mitigating ASR inaccuracies within various medical settings, with particular attention to performance variations across regional accents and medical terminology. Our findings highlight significant disparities in ASR accuracy across regions and identify specific conditions under which LLM corrections prove most effective.
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Development and Evaluation of ML Models for Cardiotocography Interpretation
Nicole Chiou
Nichole Young-Lin
Abdoulaye Diack
Christopher Kelly
Sanmi Koyejo
NPJ Women's Health (2025)
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The inherent variability in the visual interpretation of cardiotocograms (CTGs) by obstetric clinical experts, both intra- and inter-observer, presents a substantial challenge in obstetric care. In response, we investigate automated CTG interpretation as a potential solution to enhance the early detection of fetal hypoxia during labor, thereby reducing unnecessary operative interventions and improving overall maternal and neonatal care. This study employs deep learning techniques to reduce the subjectivity associated with visual CTG interpretation. Our results demonstrate that employing objective cord blood pH measurements, rather than clinician-defined Apgar scores, yields more consistent and robust model performance. Additionally, through a series of ablation studies, we investigate the impact of temporal distribution shifts on the performance of these deep learning models. We examine tradeoffs between performance and fairness, specifically evaluating performance across demographic and clinical subgroups. Finally, we discuss the practical implications of our findings for the real-world deployment of such systems, emphasizing their potential utility in medical settings with limited resources.
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Leveraging Per-Example Privacy for Machine Unlearning
Nazanin Mohammadi Sepahvand
Anvith Thudi
Ashmita Bhattacharyya
Nicolas Papernot
Eleni Triantafillou
Daniel M. Roy
Karolina Dziugaite
International Conference on Machine Learning (ICML) (2025)
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This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning. Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our theoretical analysis applies to Langevin dynamics, we empirically demonstrate that the derived guarantees—and their trends—continue to hold for fine-tuning, even in the absence of explicit noise. Our results show that per-instance privacy levels computed from training dynamics reliably predict unlearning difficulty, offering a principled and practical way to assess unlearning performance. Furthermore, our method identifies harder-to-unlearn data more effectively than existing heuristics, providing a more precise tool for guiding unlearning strategies. These findings pave the way for adaptive and efficient unlearning methods tailored to the properties of specific data points.
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Improving simulation-based origin-destination demand calibration using sample segment counts data
Arwa Alanqary
Yechen Li
The 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII), Okinawa, Japan (2025) (to appear)
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This paper introduces a novel approach to demand estimation that utilizes partial observations of segment-level track counts. Building on established simulation-based demand estimation methods, we present a modified formulation that integrates sample track counts as a regularization term. This approach effectively addresses the underdetermination challenge in demand estimation, moving beyond the conventional reliance on a prior OD matrix. The proposed formulation aims to preserve the distribution of the observed track counts while optimizing the demand to align with observed path-level travel times. We tested this approach on Seattle's highway network with various congestion levels. Our findings reveal significant enhancements in the solution quality, particularly in accurately recovering ground truth demand patterns at both the OD and segment levels.
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We present a scalable and agile approach for ads image content moderation at Google, addressing the challenges of moderating massive volumes of ads with diverse content and evolving policies. The proposed method utilizes human-curated textual descriptions and cross-modal text-image co-embeddings to enable zero-shot classification of policy violating ads images, bypassing the need for extensive supervised training data and human labeling. By leveraging large language models (LLMs) and user expertise, the system generates and refines a comprehensive set of textual descriptions representing policy guidelines. During inference, co-embedding similarity between incoming images and the textual descriptions serves as a reliable signal for policy violation detection, enabling efficient and adaptable ads content moderation. Evaluation results demonstrate the efficacy of this framework in significantly boosting the detection of policy violating content.
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As large language models (LLMs) improve in their capacity to serve as personal AI assistants, their ability to output uniquely tailored, personalized responses that align with the soft preferences of their users is imperative for maximizing user satisfaction and retention. However, lay users are notoriously bad at prompt specification and often struggle with conveying their latent preferences to AI assistants. To resolve this, we demonstrate that activation steering, an inference-time method, can effectively control the response of the LLMs towards expressing different preferences. In contrast to memory-based personalization methods that require long user history, steering is extremely lightweight and easily-controllable via an interpretable linear strength factor. We further conduct a within-subjects user study (n=14) to investigate how end users personalize their conversations through three different steerable chatbot interfaces. The results demonstrate the effectiveness of preference-based steering for aligning real-world conversations with user preferences, and we discuss qualitative findings on how diverse values around control, transparency, and usability of personalization lead users to prefer different interfaces.
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A Strategic Framework for AI Product Development and Evaluation in Enterprise Software
International Journal of Computer Engineering and Technology (IJCET), Volume 16, Issue 1 (2025)
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This article presents a comprehensive framework for developing and evaluating AI products in enterprise software systems, addressing the critical challenges organizations face during AI transformation initiatives. The article introduces a structured approach to decision-making for AI integration, encompassing ROI evaluation, user value assessment, and business impact analysis. It establishes distinct methodologies for both assistive and autonomous AI systems, providing detailed metrics for measuring success and performance across different implementation scenarios. Across various industries, the framework has shown potential in reducing implementation time, increasing user adoption rates, and enhancing overall project success rates, highlighting its practical applicability. The article methodology combines theoretical analysis with practical case studies, resulting in a flexible yet robust framework that can adapt to various organizational contexts. The framework's primary contribution lies in its practical approach to bridging the gap between theoretical AI capabilities and real-world implementation challenges, offering product leaders a systematic methodology for AI product development and evaluation. By addressing both current implementation challenges and future scalability requirements, this framework provides organizations with a foundational tool for navigating their AI transformation journey while maintaining a focus on measurable business outcomes and user value creation.
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