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 10485 publications
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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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A Call to Action: Advancing the Conversation Around Neurodivergent Education-Employment Transitions
Dannie Lynn Fountain
Vicki Baker
Kevin Danley
Closing the Gap (2025)
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Neurodiversity is still largely stigmatized and excluded from DEIB frameworks and related organizational initiatives, despite the increased recognition regarding the benefits of neuroinclusion within the education and corporate spheres. We seek to address this knowledge-to-practice gap through the creation of the Neurodiversity Engagement Framework. By highlighting supports needed for neurodivergent individuals, and those that support them, the framework helps neurodivergent individuals navigate within and across higher education and industry contexts. Informed by an interdisciplinary review of literature from higher education, industry, and corporate leadership contexts, the Neurodiversity Engagement Framework brings to light prevailing challenges within practices and policies, serving as a guide for the creation of a more supportive foundation for neurodiverse individuals to thrive. In this manuscript, readers are encouraged to consider the myriad of impacts that neurodiversity has on higher education and industry experiences and the ways that organizations can be more proactive in their support of this growing population. To conclude, we offer a roadmap for future research and practice to further elucidate ways academic and corporation leaders and policymakers can effectively support neurodivergent individuals.
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StreetViewAI: 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 StreetViewAI, the first-ever accessible street view tool, which combines context-aware, multimodal AI, accessible navigation controls, and conversational speech. With StreetViewAI, 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 StreetViewAI 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.
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Scalable Private Partition Selection via Adaptive Weighting
Justin Y. Chen
Forty-second International Conference on Machine Learning (2025)
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In the differentially private partition selection problem (a.k.a. private set union, private key discovery), users hold subsets of items from an unbounded universe. The goal is to output as many items as possible from the union of the users' sets while maintaining user-level differential privacy. Solutions to this problem are a core building block for many privacy-preserving ML applications including vocabulary extraction in a private corpus, computing statistics over categorical data and learning embeddings over user-provided items.
We propose an algorithm for this problem, MaxAdaptiveDegree(MAD), which adaptively reroutes weight from items with weight far above the threshold needed for privacy to items with smaller weight, thereby increasing the probability that less frequent items are output. Our algorithm can be efficiently implemented in massively parallel computation systems allowing scalability to very large datasets. We prove that our algorithm stochastically dominates the standard parallel algorithm for this problem. We also develop a two-round version of our algorithm, MAD2R, where results of the computation in the first round are used to bias the weighting in the second round to maximize the number of items output. In experiments, our algorithms provide the best results across the board among parallel algorithms and scale to datasets with hundreds of billions of items, up to three orders of magnitude larger than those analyzed by prior sequential algorithms.
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We revisit the fundamental question of formally defining what constitutes a reconstruction attack. While often clear from the context, our exploration reveals that a precise definition is much more nuanced than it appears, to the extent that a single all-encompassing definition may not exist. Thus, we employ a different strategy and aim to "sandwich" the concept of reconstruction attacks by addressing two complementing questions: (i) What conditions guarantee that a given system is protected against such attacks? (ii) Under what circumstances does a given attack clearly indicate that a system is not protected? More specifically,
* We introduce a new definitional paradigm -- Narcissus Resiliency -- to formulate a security definition for protection against reconstruction attacks. This paradigm has a self-referential nature that enables it to circumvent shortcomings of previously studied notions of security. Furthermore, as a side-effect, we demonstrate that Narcissus resiliency captures as special cases multiple well-studied concepts including differential privacy and other security notions of one-way functions and encryption schemes.
* We formulate a link between reconstruction attacks and Kolmogorov complexity. This allows us to put forward a criterion for evaluating when such attacks are convincingly successful.
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Augmenting LLMs with context leads to improved performance across many applications. Despite much research on Retrieval Augmented Generation (RAG) systems, an open question is whether errors arise because LLMs fail to utilize the context from retrieval or the context itself is insufficient to answer the query. To shed light on this, we develop a new notion of sufficient context, along with a way to classify instances that have enough information to answer the query. We then use sufficient context to analyze several models and datasets. By stratifying errors based on context sufficiency, we find that proprietary LLMs (Gemini, GPT, Claude) excel at answering queries when the context is sufficient, but often output incorrect answers instead of abstaining when the context is not. On the other hand, open-source LLMs (Llama, Mistral, Gemma) hallucinate or abstain often, even with sufficient context. We further categorize cases when the context is useful, and improves accuracy, even though it does not fully answer the query and the model errs without the context. Building on our findings, we explore ways to reduce hallucinations in RAG systems, including a new selective generation method that leverages sufficient context information for guided abstention. Our method improves the fraction of correct answers among times where the model responds by 2--10% for Gemini, GPT, and Gemma.
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Agents based on large language models (LLMs) for machine learning engineering (MLE) can automatically implement ML models via code generation. However, existing approaches to build such agents often rely heavily on inherent LLM knowledge and employ coarse exploration strategies that modify the entire code structure at once. This limits their ability to select effective task-specific models and perform deep exploration within specific components, such as experimenting extensively with feature engineering options. To overcome these, we propose MLE-STAR, a novel approach to build MLE agents. MLESTAR first leverages external knowledge by using a search engine to retrieve effective models from the web, forming an initial solution, then iteratively refines it by exploring various strategies targeting specific ML components. This exploration is guided by ablation studies analyzing the impact of individual code blocks. Furthermore, we introduce a novel ensembling method using an effective strategy suggested by MLE-STAR. Our experimental results show that MLE-STAR achieves medals in 64% of the Kaggle competitions on the MLE-bench Lite, significantly outperforming the best alternative.
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Perceptual Evaluation of a Mix Presentation for Immersive Audio with IAMF
Carlos Tejeda-Ocampo
Toni Hirvonen
Ema Souza-Blanes
Mahmoud Namazi
AES 158th Convention of the Audio Engineering Society (2025)
Preview abstract
Immersive audio mix presentations involve transmitting and rendering several audio elements simultaneously. This enables next-generation applications, such as personalized playback. Using immersive loudspeaker and headphone MUSHRA tests, we investigate bitrate vs. quality for a typical mix presentation use case of a foreground stereo element, plus a background Ambisonics scene. For coding, we use Immersive Audio Model and Formats, a recently
proposed system for Next-Generation Audio. Excellent quality is achieved at 384 kbit/s even with reasonable amount of personalization. We also propose a framework for content-aware analysis that can significantly reduce the bitrate when using underlying legacy audio coding instances.
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Opportunities and Applications of GenAI in Smart Cities: A User-Centric Survey
Shashank Kapoor
Aman Raj
IEEE COINS (2025)
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The proliferation of IoT in cities, combined with Digital Twins, creates a rich data foundation for Smart Cities aimed at improving urban life and operations. Generative AI (GenAI) significantly enhances this potential, moving beyond traditional AI analytics by processing multimodal content and generating novel outputs like text and simulations. Using specialized or foundational models, GenAI's natural language abilities such as Natural Language Understanding (NLU) and Generation (NLG) can power tailored applications and unified interfaces, dramatically lowering barriers for users interacting with complex smart city systems. In this paper, we focus on GenAI applications based on conversational interfaces within the context of three critical user archetypes in a Smart City - Citizens, Operators and Planners. We identify and review GenAI models and techniques that have been proposed or deployed for various urban subsystems in the contexts of these user archetypes. We also consider how GenAI can be built on the existing data foundation of official city records, IoT data streams and Urban Digital Twins. We believe this work represents the first comprehensive summarization of GenAI techniques for Smart Cities from the lens of the critical users in a Smart City.
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AI and Generative AI Transforming Disaster Management: A Survey of Damage Assessment and Response Techniques
Aman Raj
Shashank Kapoor
IEEE Compsac 2025 (2025)
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Natural disasters, including earthquakes, wildfires and cyclones, bear a huge risk on human lives as well as infrastructure assets. An effective response to disaster depends on the ability to rapidly and efficiently assess the intensity of damage. Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) presents a breakthrough solution, capable of combining knowledge from multiple types and sources of data, simulating realistic scenarios of disaster, and identifying emerging trends at a speed previously unimaginable. In this paper, we present a comprehensive review on the prospects of AI and GenAI in damage assessment for various natural disasters, highlighting both its strengths and limitations. We talk about its application to multimodal data such as text, image, video, and audio, and also cover major issues of data privacy, security, and ethical use of the technology during crises. The paper also recognizes the threat of Generative AI misuse, in the form of dissemination of misinformation and for adversarial attacks. Finally, we outline avenues of future research, emphasizing the need for secure, reliable, and ethical Generative AI systems for disaster management in general. We believe that this work represents the first comprehensive survey of Gen-AI techniques being used in the field of Disaster Assessment and Response.
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Enhancing Remote Sensing Representations through Mixed-Modality Masked Autoencoding
Ori Linial
Yochai Blau
Nadav Sherman
Yotam Gigi
Wojciech Sirko
Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops (2025), pp. 507-516
Preview abstract
This paper presents an innovative approach to pre-training models for remote sensing by integrating optical and radar data from Sentinel-2 and Sentinel-1 satellites. Using a novel variation on the masked autoencoder (MAE) framework, our model incorporates a dual-task setup: reconstructing masked Sentinel-2 images and predicting corresponding Sentinel-1 images. This multi-task design enables the encoder to capture both spectral and structural features across diverse environmental conditions. Additionally, we introduce a "mixing" strategy in the pretraining phase, combining patches from both image sources, which mitigates spatial misalignment errors and enhances model robustness. Evaluation on segmentation and classification tasks, including Sen1Floods11 and BigEarthNet, demonstrates significant improvements in adaptability and generalizability across varied downstream remote sensing applications. Our findings highlight the advantages of leveraging complementary modalities for more resilient and versatile land cover analysis.
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Databases in the Era of Memory-Centric Computing
Yannis Chronis
Anastasia Ailamaki
Lawrence Benson
Jana Gičeva
Eric Seldar
Lisa Wu Wills
2025
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The increasing disparity between processor core counts and memory bandwidth, coupled with the rising cost and underutilization of memory, introduces a performance and cost Memory Wall and presents a significant challenge to the scalability of database systems. We argue that current processor-centric designs are unsustainable, and we advocate for a shift towards memory-centric computing, where disaggregated memory pools enable cost-effective scaling and robust performance. Database systems are uniquely positioned to leverage memory-centric systems because of their intrinsic data-centric nature. We demonstrate how memory-centric database operations can be realized with current hardware, paving the way for more efficient and scalable data management in the cloud.
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Triaging mammography with artificial intelligence: an implementation study
Sarah M. Friedewald
Sunny Jansen
Fereshteh Mahvar
Timo Kohlberger
David V. Schacht
Sonya Bhole
Dipti Gupta
Scott Mayer McKinney
Stacey Caron
David Melnick
Mozziyar Etemadi
Samantha Winter
Alejandra Maciel
Luca Speroni
Martha Sevenich
Arnav Agharwal
Rubin Zhang
Gavin Duggan
Shiro Kadowaki
Atilla Kiraly
Jie Yang
Basil Mustafa
Krish Eswaran
Shravya Shetty
Breast Cancer Research and Treatment (2025)
Preview abstract
Purpose
Many breast centers are unable to provide immediate results at the time of screening mammography which results in delayed patient care. Implementing artificial intelligence (AI) could identify patients who may have breast cancer and accelerate the time to diagnostic imaging and biopsy diagnosis.
Methods
In this prospective randomized, unblinded, controlled implementation study we enrolled 1000 screening participants between March 2021 and May 2022. The experimental group used an AI system to prioritize a subset of cases for same-visit radiologist evaluation, and same-visit diagnostic workup if necessary. The control group followed the standard of care. The primary operational endpoints were time to additional imaging (TA) and time to biopsy diagnosis (TB).
Results
The final cohort included 463 experimental and 392 control participants. The one-sided Mann-Whitney U test was employed for analysis of TA and TB. In the control group, the TA was 25.6 days [95% CI 22.0–29.9] and TB was 55.9 days [95% CI 45.5–69.6]. In comparison, the experimental group's mean TA was reduced by 25% (6.4 fewer days [one-sided 95% CI > 0.3], p<0.001) and mean TB was reduced by 30% (16.8 fewer days; 95% CI > 5.1], p=0.003). The time reduction was more pronounced for AI-prioritized participants in the experimental group. All participants eventually diagnosed with breast cancer were prioritized by the AI.
Conclusions
Implementing AI prioritization can accelerate care timelines for patients requiring additional workup, while maintaining the efficiency of delayed interpretation for most participants. Reducing diagnostic delays could contribute to improved patient adherence, decreased anxiety and addressing disparities in access to timely care.
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Capturing Real-World Habitual Sleep Patterns with a Novel User-centric Algorithm to Pre-Process Fitbit Data in the All of Us Research Program: Retrospective observational longitudinal study
Hiral Master
Jeffrey Annis
Karla Gleichauf
Lide Han
Peyton Coleman
Kelsie Full
Neil Zheng
Doug Ruderfer
Logan Schneider
Evan Brittain
Journal of Medical Internet Research (2025)
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Background:
Commercial wearables such as Fitbit quantify sleep metrics using fixed calendar times as default measurement periods, which may not adequately account for individual variations in sleep patterns. To address this limitation, experts in sleep medicine and wearable technology developed a user-centric algorithm designed to more accurately reflect actual sleep behaviors and improve the validity of wearable-derived sleep metrics.
Objective:
This study aims to describe the development of a new user-centric algorithm, compare its performance with the default calendar-relative algorithm, and provide a practical guide for analyzing All of Us Fitbit sleep data on a cloud-based platform.
Methods:
The default and user-centric algorithms were implemented to preprocess and compute sleep metrics related to schedule, duration, and disturbances using high-resolution Fitbit sleep data from 8563 participants (median age 58.1 years, 6002/8341, 71.96%, female) in the All of Us Research Program (version 7 Controlled Tier). Variations in typical sleep patterns were calculated by examining the differences in the mean number of primary sleep logs classified by each algorithm. Linear mixed-effects models were used to compare differences in sleep metrics across quartiles of variation in typical sleep patterns.
Results:
Out of 8,452,630 total sleep logs collected over a median of 4.2 years of Fitbit monitoring, 401,777 (4.75%) nonprimary sleep logs identified by the default algorithm were reclassified as primary sleep by the user-centric algorithm. Variation in typical sleep patterns ranged from –0.08 to 1. Among participants with the greatest variation in typical sleep patterns, the user-centric algorithm identified significantly more total sleep time (by 17.6 minutes; P<.001), more wake after sleep onset (by 13.9 minutes; P<.001), and lower sleep efficiency (by 2.0%; P<.001), on average. Differences in sleep stage metrics between the 2 algorithms were modest.
Conclusions:
The user-centric algorithm captures the natural variability in sleep schedules, providing an alternative approach to preprocess and evaluate sleep metrics related to schedule, duration, and disturbances. A publicly available R package facilitates the implementation of this algorithm for clinical and translational research.
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Differentiable Approximations for Distance Queries
David M. Mount
Proceedings of the 2025 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)
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The widespread use of gradient-based optimization has motivated the adaptation of various classical algorithms into differentiable solvers compatible with learning pipelines. In this paper, we investigate the enhancement of traditional geometric query problems such that the result consists of both the geometric function as well as its gradient. Specifically, we study the fundamental problem of distance queries against a set of points P in R^d, which also underlies various similarity measures for learning algorithms.
The main result of this paper is a multiplicative (1+epsilon)-approximation of the Euclidean distance to P which is differentiable at all points in R^d \ P with asymptotically optimal bounds on the norms of its gradient and Hessian, from a data structure with storage and query time matching state-of-the-art results for approximate nearest-neighbor searching. The approximation is realized as a regularized distance through a partition-of-unity framework, which efficiently blends multiple local approximations, over a suitably defined covering of space, into a smooth global approximation. In order to obtain the local distance approximations in a manner that facilitates blending, we develop a new approximate Voronoi diagram based on a simple point-location data structure, simplifying away both the lifting transformation and ray shooting.
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