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 10795 publications
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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.
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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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Study of Arterials in the City of Rio de Janeiro for Traffic Coordination
Ori Rottenstreich
Eliav Buchnik
Danny Veikherman
Dan Karliner
Tom Kalvari
Shai Ferster
Ron Tsibulsky
Jack Haddad
2025
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Urban traffic congestion is a growing challenge, and optimizing signal timing strategies is crucial for improving traffic flow and reducing emissions. The coordination of signalized intersections improves both traffic operations and environmental aspects. Coordination is particularly important along arterials, sequences of signalized intersections that serve as the primary routes and carry a high volume of traffic. In this paper we analyze real data from the city of Rio de Janeiro to study properties of arterials. We refer to their length, the distance between intersections and to the properties of the traffic light plans such as cycle time. We then study their in practice level of coordination in terms of number of stops and their common locations along the arterials. We dive into particular arterials and provide insights that can be useful for efficient design of arterials in additional cities. Based on the analysis, we show how simple traffic properties can indicate the potential upon coordinating two adjacent intersections as part of an arterial in improving traffic performance.
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Position Paper: Graph Learning Loses Relevance Due To Poor Benchmarks
Maya Bechler-Speicher
Ben Finkelshtein
Fabrizio Frasca
Luis Müller
Jan Tönshoff
Antoine Siraudin
Viktor Zaverkin
Michael Bronstein
Mathias Niepert
Michael Galkin
Christopher Morris
2025
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While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and relevance. Current benchmarking practices often lack focus on transformative, real-world applications, favoring narrow domains like two-dimensional molecular graphs over broader, impactful areas such as combinatorial optimization, relational databases, or chip design. Additionally, many benchmark datasets poorly represent the underlying data, leading to inadequate abstractions and misaligned use cases. Fragmented evaluations and an excessive focus on accuracy further exacerbate these issues, incentivizing overfitting rather than fostering generalizable insights. These limitations have prevented the development of truly useful graph foundation models. This position paper calls for a paradigm shift toward more meaningful benchmarks, rigorous evaluation protocols, and stronger collaboration with domain experts to drive impactful and reliable advances in graph learning research, unlocking the potential of graph learning.
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Several resource allocation settings involve agents with unequal entitlements represented by weights. We analyze weighted fair division from an asymptotic perspective: if m items are divided among n agents whose utilities are independently sampled from a probability distribution, when is it likely that a fair allocation exist? We show that if the ratio between the weights is bounded, a weighted envy-free allocation exists with high probability provided that m = Ω(n log n/ log log n), generalizing a prior unweighted result. For weighted proportionality, we establish a sharp threshold of m = n/(1 − μ) for the transition from non-existence to existence, where μ ∈ (0, 1) denotes the mean of the distribution. In addition, we prove that for two agents, a weighted envy-free (and weighted proportional) allocation is likely to exist if m = ω(√r), where r denotes the ratio between the two weights.
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Shadow Hamiltonian Simulation
Rolando Somma
Robbie King
Tom O'Brien
Nature Communications, 16 (2025), pp. 2690
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Simulating quantum dynamics is one of the most important applications of quantum computers. Traditional approaches for quantum simulation involve preparing the full evolved state of the system and then measuring some physical quantity. Here, we present a different and novel approach to quantum simulation that uses a compressed quantum state that we call the "shadow state". The amplitudes of this shadow state are proportional to the time-dependent expectations of a specific set of operators of interest, and it evolves according to its own Schrödinger equation. This evolution can be simulated on a quantum computer efficiently under broad conditions. Applications of this approach to quantum simulation problems include simulating the dynamics of exponentially large systems of free fermions or free bosons, the latter example recovering a recent algorithm for simulating exponentially many classical harmonic oscillators. These simulations are hard for classical methods and also for traditional quantum approaches, as preparing the full states would require exponential resources. Shadow Hamiltonian simulation can also be extended to simulate expectations of more complex operators such as two-time correlators or Green's functions, and to study the evolution of operators themselves in the Heisenberg picture.
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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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Why all roads don't lead to Rome: Representation geometry varies across the human visual cortical hierarchy
Zahraa Chorghay
Arna Ghosh
Shahab Bakhtiari
Blake Richards
(2025) (to appear)
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Biological and artificial intelligence systems navigate the fundamental efficiency-robustness tradeoff for optimal encoding, i.e., they must efficiently encode numerous attributes of the input space while also being robust to noise. This challenge is particularly evident in hierarchical processing systems like the human brain. With a view towards understanding how systems navigate the efficiency-robustness tradeoff, we turned to a population geometry framework for analyzing representations in the human visual cortex alongside artificial neural networks (ANNs). In the ventral visual stream, we found general-purpose, scale-free representations characterized by a power law-decaying eigenspectrum in most but not areas. Of note, certain higher-order visual areas did not have scale-free representations, indicating that scale-free geometry is not a universal property of the brain. In parallel, ANNs trained with a self-supervised learning objective also exhibited scale-free geometry, but not after fine-tuning on a specific task. Based on these empirical results and our analytical insights, we posit that a system’s representation geometry is not a universal property and instead depends upon the computational objective.
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Toward Sensor-In-the-Loop LLM Agent: Benchmarks and Implications
Zhiwei Ren
Junbo Li
Minjia Zhang
Di Wang
Longfei Shangguan
SenSys 2025 - The 23rd ACM Conference on Embedded Networked Sensor Systems (2025)
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This paper advocates for sensor-informed personal agents that can take advantage of sensor hints on wearables to enhance the personal agent's response. We demonstrate that such a sensor-in-the-loop design paradigm can be easily integrated into existing LLM agents by building a prototype named WellMax based on existing well-developed techniques such as structured prompt tuning and few-shot prompting. The head-to-head comparison with a non-sensor-informed agent across five use scenarios demonstrates that this sensor-in-the-loop design can effectively improve users' needs and their overall experience. The deep-dive into agents' replies and participants' feedback further reveals that sensor-in-the-loop agents not only provide more contextually relevant responses but also exhibit a greater understanding of user priorities and situational nuances. We further conduct two case studies to examine the potential pitfalls and distill key insights from this sensor-in-the-loop agent. We believe this work sets the stage for more intelligent, empathetic, and effective interactions in future AI-driven personal assistants.
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Fast electronic structure quantum simulation by spectrum amplification
Guang Hao Low
Robbie King
Dominic Berry
Qiushi Han
Albert Eugene DePrince III
Alec White
Rolando Somma
arXiv:2502.15882 (2025)
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The most advanced techniques using fault-tolerant quantum computers to estimate the ground-state energy of a chemical Hamiltonian involve compression of the Coulomb operator through tensor factorizations, enabling efficient block-encodings of the Hamiltonian. A natural challenge of these methods is the degree to which block-encoding costs can be reduced. We address this challenge through the technique of spectrum amplification, which magnifies the spectrum of the low-energy states of Hamiltonians that can be expressed as sums of squares. Spectrum amplification enables estimating ground-state energies with significantly improved cost scaling in the block encoding normalization factor $\Lambda$ to just $\sqrt{2\Lambda E_{\text{gap}}}$, where $E_{\text{gap}} \ll \Lambda$ is the lowest energy of the sum-of-squares Hamiltonian. To achieve this, we show that sum-of-squares representations of the electronic structure Hamiltonian are efficiently computable by a family of classical simulation techniques that approximate the ground-state energy from below. In order to further optimize, we also develop a novel factorization that provides a trade-off between the two leading Coulomb integral factorization schemes-- namely, double factorization and tensor hypercontraction-- that when combined with spectrum amplification yields a factor of 4 to 195 speedup over the state of the art in ground-state energy estimation for models of Iron-Sulfur complexes and a CO$_{2}$-fixation catalyst.
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Passive Heart Rate Monitoring During Smartphone Use in Everyday Life
Shun Liao
Paolo Di Achille
Jiang Wu
Silviu Borac
Jonathan Wang
Eric Teasley
Lawrence Cai
Daniel McDuff
Hao-Wei Su
Brent Winslow
Anupam Pathak
Shwetak Patel
Jim Taylor
Jamie Rogers
(2025)
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Resting heart rate (RHR) is an important biomarker of cardiovascular health and mortality, but tracking it longitudinally generally requires a wearable device, limiting its availability. We present PHRM, a deep learning system for passive heart rate (HR) and RHR measurements during ordinary smartphone use, using facial video-based photoplethysmography. Our system was developed using 225,773 videos from 495 participants and validated on 185,970 videos from 205 participants in laboratory and free-living conditions – the largest validation study of its kind. Compared to reference electrocardiogram, PHRM achieved a mean absolute percentage error (MAPE) <10% for HR measurements across three skin tone groups of light, medium and dark pigmentation; MAPE for each skin tone group was non-inferior versus the others. Daily RHR measured by PHRM had a mean absolute error <5 bpm compared to a wearable HR tracker, and was associated with known risk factors. These results highlight the potential of smartphones to enable passive and equitable heart health monitoring.
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During remote communication, participants often share both digital and physical content, such as product designs, digital assets, and environments, to enhance mutual understanding. Recent advances in augmented communication have facilitated users to swiftly create and share digital 2D copies of physical objects from video feeds into a shared space. However, conventional 2D representations of digital objects limits spatial referencing in immersive environments. To address this, we propose Thing2Reality, an Extended Reality (XR) meeting platform that facilitates spontaneous discussions of both digital and physical items during remote sessions. With Thing2Reality, users can quickly materialize ideas or objects in immersive environments and share them as conditioned multiview renderings or 3D Gaussians. Thing2Reality enables users to interact with remote objects or discuss concepts in a collaborative manner. Our user studies revealed that the ability to interact with and manipulate 3D representations of objects significantly enhances the efficiency of discussions, with the potential to augment discussion of 2D artifacts.
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Computer use agents (CUAs) need to plan long-horizon task workflows grounded in diverse, ever-changing applications and environments, but learning is hindered by the scarcity of large-scale, high-quality training data. Existing datasets are small, domain-specific, and costly to annotate, while current synthetic data generation methods often yield brittle, simplistic, or misaligned task demonstrations.
We introduce Watch & Learn (W&L), a framework that transforms human demonstration videos available in the Internet into executable UI trajectories at scale. Inspired by robotics, we train an inverse dynamics model that accurately predicts user actions from consecutive screens, bypassing the need for complex heuristics. To scale to the web, we curate a large state-transition corpus and design a retrieval framework that identifies relevant video tutorials, enabling automatic conversion of raw videos into structured UI trajectories without requiring manual annotations. Beyond training data, we show that the generated UI trajectories can also serve as in-context exemplars, providing CUAs with long-horizon priors and domain-specific knowledge at inference time.
On the challenging OSWorld and Mind2Web benchmarks, UI trajectories extracted with W&L consistently improve both general-purpose and state-of-the-art frameworks when used in-context, and delivers stronger gains for open-source models when used in training. These results highlight web-scale human demonstration videos as a practical and scalable foundation for advancing CUAs towards real-world deployment.
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On the Design of the Binaural Rendering Library for Eclipsa Audio Immersive Audio Container
Tomasz Rudzki
Gavin Kearney
AES 158th Convention of the Audio Engineering Society (2025)
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Immersive Audio Media and Formats (IAMF), also known as Eclipsa Audio, is an open-source audio container developed to accommodate multichannel and scene-based audio formats. Headphone-based delivery of IAMF audio requires efficient binaural rendering. This paper introduces the Open Binaural Renderer (OBR), which is designed to render IAMF audio. It discusses the core rendering algorithm, the binaural filter design process as well as real-time implementation of the renderer in a form of an open-source C++ rendering library. Designed for
multi-platform compatibility, the renderer incorporates a novel approach to binaural audio processing, leveraging a combination of spherical harmonic (SH) based virtual listening room model and anechoic binaural filters. Through its design, the IAMF binaural renderer provides a robust solution for delivering high-quality immersive audio across diverse platforms and applications.
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Circadian rhythm of heart rate and activity: a cross-sectional study
Maryam Khalid
Logan Schneider
Aravind Natarajan
Conor Heneghan
Karla Gleichauf
Chronobiology International (2025)
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ABSTRACT
Background: Circadian rhythms are commonly observed in a number of physiological processes. Consumer wearable devices have made it possible to obtain continuous time series data from a large number of individuals. We study circadian rhythms from measurements of heart rate, movement, and sleep, from a cohort of nearly 20,000 participants over the course of 30 days.
Methods: Participation was restricted to Fitbit users of age 21 years or older residing in the United States or Canada. Participants were enrolled through a recruitment banner shown on the Fitbit App. The advertisement was shown to 531,359 Fitbit users, and 23,239 enrolled in the program. Of these, we obtained heart rate data from 19,350 participants. We obtain the underlying circadian rhythm from time series heart rate by modeling the circadian rhythm as a sum over the first two Fourier harmonics. The first Fourier harmonic accounts for the 24-hour rhythmicity, while the second harmonic accounts for non-sinusoidal perturbations.
Findings: We observe a circadian rhythm in both heart rate and acceleration. From the diurnal modulation, we obtain the following circadian parameters: (i) amplitude of modulation, (ii) bathyphase, (iii) acrophase, (iv) non-sinusoidal fraction, and (v) fraction of day when the heart rate is greater than the mean. The amplitude, bathyphase, and acrophase depend on sex, and decrease with age. The waketime on average, follows the bathyphase by 2.4 hours. In most individuals, the circadian rhythm of heart rate lags the circadian rhythm of activity.
Interpretation: Circadian metrics for heart rate and activity can be reliably obtained from commercially available wearable devices. Distributions of circadian metrics can be valuable tools for individual-level interpretation.
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