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 11067 publications
mmMUSE: An mmWave-based Motion-resilient Universal Speech Enhancement System
Chenming He
Yanyong Zhang
Kai Wang
Dequan Wang
Lingyu Wang
the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), ACM (2026) (to appear)
Preview abstract
Voice-based smart systems can greatly enhance user experiences by allowing higher-quality interactions through better voice perception. Speech enhancement can benefit such systems by isolating noise from speech. Recently, integrating millimeter-wave (mmWave) with audio for speech perception has gained increasing attention due to microphones' limitations in noisy environments. However, mmWave-based vocal extraction is severely affected by motion, which disperses vocal signals across ranges and introduces distortions. In this paper, we propose an mmWave-based motion-resilient universal speech enhancement system called mmMUSE, which fuses mmWave and audio signals. To mitigate motion interference, we develop a Doppler-based method for motion-robust vocal signal extraction. Moreover, by introducing the Vocal-Noise-Ratio metric to assess the prominence of vocal signals from mmWave, we achieve real-time voice activity detection that gains 3.81 dB of SISDR in noisy speeches. Additionally, we design a two-stage complex-valued network that includes an attention-based fusion network for cross-modal complementing and a time-frequency masking network for correcting amplitude and phase of speech to isolate noises.
Using mmWave and audio datasets from 46 participants, mmMUSE outperforms the state-of-the-art speech enhancement models, achieving an average SISDR improvement of 3.12 dB. Additionally, mmMUSE achieves SISDR improvements of 16.51 dB, 17.93 dB, 14.93 dB, and 18.95 dB in controlled environments involving intense noise, extensive motion, multiple speakers, and various obstructive materials, respectively. Finally, we evaluate mmMUSE in real-world scenarios including running, public spaces, and driving, maintaining a word error rate (WER) below 10%.
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
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
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
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
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
Mapping Farmed Landscapes from Remote Sensing
Alex Wilson
Michelangelo Conserva
Charlotte Stanton
CCAI Workshop at NeurIPS (2025)
Preview abstract
To overcome the critical lack of detailed ecological maps needed for managing agricultural landscapes, we developed Farmscapes: the first large-scale, high-resolution map that identifies ecologically vital rural features, including often overlooked elements like hedgerows and stone walls. We achieved high accuracy in mapping key habitats with a deep learning model trained on aerial imagery and expert annotations. As a result, this work enables data-driven planning for habitat restoration, supports the monitoring of key initiatives like the EU Biodiversity Strategy, and lays a foundation for advanced analysis of landscape connectivity.
View details
The Grand Challenge of Quantum Applications
Robbie King
Bill Huggins
Guang Hao Low
Tom O'Brien
arXiv:2511.09124 (2025)
Preview abstract
This perspective outlines promising pathways and critical obstacles on the road to developing useful quantum computing applications, drawing on insights from the Google Quantum AI team. We propose a five-stage framework for this process, spanning from theoretical explorations of quantum advantage to the practicalities of compilation and resource estimation. For each stage, we discuss key trends, milestones, and inherent scientific and sociological impediments. We argue that two central stages -- identifying concrete problem instances expected to exhibit quantum advantage, and connecting such problems to real-world use cases -- represent essential and currently under-resourced challenges. Throughout, we touch upon related topics, including the promise of generative artificial intelligence for aspects of this research, criteria for compelling demonstrations of quantum advantage, and the future of compilation as we enter the era of early fault-tolerant quantum computing.
View details
Simulation-Based Inference: A Practical Guide
Michael Deistler
Jan Boelts
Peter Steinbach
Guy Moss
Thomas Moreau
Manuel Gloeckler
Pedro L. C. Rodriguez
Julia Linhart
Janne K. Lappalainen
Benjamin Kurt Miller
Pedro J. Goncalves
Cornelius Schröder
Jakob H. Macke
arXiv (2025)
Preview abstract
A central challenge in many areas of science and engineering is to identify model parameters that are consistent with empirical data and prior knowledge. Bayesian inference offers a principled framework for this task, but can be computationally prohibitive when models are defined by stochastic simulators. Simulation-Based Inference (SBI) provides a suite of methods to overcome this limitation and has enabled scientific discoveries in fields such as particle physics, astrophysics and neuroscience. The core idea of SBI is to train neural networks on data generated by a simulator, without requiring access to likelihood evaluations. Once trained, the neural network can rapidly perform inference on empirical observations without requiring additional optimization or simulations. In this tutorial, we provide a practical guide for practitioners aiming to apply SBI methods. We outline a structured SBI workflow and offer practical guidelines and diagnostic tools for every stage of the process--from setting up the simulator and prior, choosing the SBI method and neural network architecture, training the inference model, to validating results and interpreting the inferred parameters. We illustrate these steps through examples from astrophysics, psychophysics, and neuroscience. This tutorial empowers researchers to apply state-of-the-art SBI methods, facilitating efficient parameter inference for scientific discovery.
View details
Improved FPT Approximation Scheme and Approximate Kernel for Biclique-Free Max k-Weight SAT: Greedy Strikes Back
Theoretical Computer Science, 1028 (2025)
Preview abstract
In the Max k-Weight SAT (aka Max SAT with Cardinality Constraint) problem, we are given a CNF formula with n variables and m clauses together with a positive integer k. The goal is to find an assignment where at most k variables are set to one that satisfies as many constraints as possible. Recently, Jain et al. (SODA 2023) gave an FPT approximation scheme (FPT-AS) with running time 2^O((dk/ε)^d) * (n + m)^O(1) for Max k-Weight SAT when the incidence graph is K_{d,d}-free. They asked whether a polynomial-size approximate kernel exists. In this work, we answer this question positively by giving an (1 − ε)-approximate kernel with (dk/ε)^O(d) variables. This also implies an improved FPT-AS with running time (dk/ε)^O(dk) * (n+m)^O(1)-time algorithm for the problem. Our approximate kernel is based mainly on a couple of greedy strategies together with a sunflower lemma-style reduction rule.
View details
Using Magnesium Hydroxide for Ocean Alkalinity Enhancement: Elucidating the Role of Formation Conditions on Material Properties and Dissolution Kinetics
Frontiers in Climate (2025)
Preview abstract
Mg(OH)2 holds potential as an alkalinity source for Ocean Alkalinity Enhancement (OAE). It is a
current byproduct of desalination treatment through the alkalinity exchange of
electrochemically derived NaOH to the Mg-rich reverse osmosis reject brine. Characterization
found no chemical composition difference among seawater-precipitated and industrial sourced
Mg(OH)2 with both having high (>98%) purity. Differences were found with the crystallinity with
industrial sources containing a higher degree of crystallinity of 0.83-0.85 compared to 0.16-0.33
for seawater-precipitated paste. Mg(OH)2 with a higher degree of crystallinity (>80%) had
significantly slower dissolution rates than Mg(OH)2 with a lower degree of crystallinity (<20%).
Results revealed that there is a strong inverse relation between degree of crystallinity and
dissolution rate of both seawater-precipitated and industrial sourced Mg(OH)2. Seawater39 precipitated Mg(OH)2, with its similar purity to industrial sources yet faster and more complete
dissolution and alkalinity release, could hold an advantage over other alkalinity sources for OAE
applications with its seemingly tunable dissolution kinetics.
View details
Reconfigurable Stream Network Architecture
Chengyue Wang
Jason Cong
James Hoe
International Symposium on Computer Architecture (ISCA) (2025)
Preview abstract
As AI systems grow increasingly specialized and complex, managing hardware heterogeneity becomes a pressing challenge. How can we efficiently coordinate and synchronize heterogeneous hardware resources to achieve high utilization? How can we minimize the friction of transitioning between diverse computation phases, reducing costly stalls from initialization, pipeline setup, or drain? Our insight is that a network abstraction at the ISA level naturally unifies heterogeneous resource orchestration and phase transitions. This paper presents a Reconfigurable Stream Network Architecture (RSN), a novel ISA abstraction designed for the DNN domain. RSN models the datapath as a circuit-switched network with stateful functional units as nodes and data streaming on the edges. Programming a computation corresponds to triggering a path. Software is explicitly exposed to the compute and communication latency of each functional unit, enabling precise control over data movement for optimizations such as compute-communication overlap and layer fusion. As nodes in a network naturally differ, the RSN abstraction can efficiently virtualize heterogeneous hardware resources by separating control from the data plane, enabling low instruction-level intervention. We build a proof-of-concept design RSN-XNN on VCK190, a heterogeneous platform with FPGA fabric and AI engines. Compared to the SOTA solution on this platform, it reduces latency by 6.1x and improves throughput by 2.4x-3.2x. Compared to the T4 GPU with the same FP32 performance, it matches latency with only 18% of the memory bandwidth. Compared to the A100 GPU at the same 7nm process node, it achieves 2.1x higher energy efficiency in FP32.
View details
Preview abstract
In recent years, deep learning has made remarkable progress in a wide range of domains, with a
particularly notable impact on natural language
processing tasks. One of the challenges associated
with training deep neural networks is the need
for large amounts of computational resources and
time. In this paper, we present Deep Fusion, an efficient approach to network training that leverages
pre-trained initializations of smaller networks.
We show that Deep Fusion accelerates the training process, reduces computational requirements,
and leads to improved generalization performance
on a variety of NLP tasks and T5 model sizes.
Our experiments demonstrate that Deep Fusion
is a practical and effective approach to reduce
the training time and resource consumption while
maintaining, or even surpassing, the performance
of traditional training methods.
View details
Better autoregressive regression with LLMs via regression-aware fine-tuning
Zhao Meng
Aditya Menon
The Thirteenth International Conference on Learning Representations (2025)
Preview abstract
Decoder-based large language models (LLMs) have proven highly versatile, with remarkable successes even on problems ostensibly removed from traditional language generation. One such example is solving regression problems, where the targets are real numbers rather than textual tokens. A common approach to use LLMs on such problems is to perform fine-tuning based on the cross-entropy loss, and use autoregressive sampling at inference time. Another approach relies on fine-tuning a separate predictive head with a suitable loss such as squared error. While each approach has had success, there has been limited study on principled ways of using decoder LLMs for regression. In this work, we compare different prior works under a unified view, and introduce regression-aware fine-tuning(RAFT), a novel approach based on the Bayes-optimal decision rule. We demonstrate how RAFT improves over established baselines on several benchmarks and model families.
View details
ESAM++: Efficient Online 3D Perception on the Edge
Qin Liu
Lavisha Aggarwal
Vikas Bahirwani
Lin Li
Aleksander Holynski
Saptarashmi Bandyopadhyay
Zhengyang Shen
Marc Niethammer
Ehsan Adeli
Andrea Colaco
2025
Preview abstract
Online 3D scene perception in real time is critical for robotics, AR/VR, and autonomous systems, particularly in edge computing scenarios where computational resources are limited. Recent state-of-the-art methods like EmbodiedSAM (ESAM) demonstrate the promise of online 3D perception by leveraging the 2D visual foundation model (VFM) with efficient 3D query lifting and merging. However, ESAM depends on a computationally expensive sparse 3D U-Net for point cloud feature extraction, which we identify as the primary efficiency bottleneck. In this paper, we propose a lightweight and scalable alternative for online 3D scene perception tailored to edge devices. Our method introduces a 3D Sparse FeaturePyramid Network (SFPN) that efficiently captures multi-scale geometric features from streaming 3D point clouds while significantly reducing computational over-head and model size. We evaluate our approach on four challenging segmentation benchmarks—ScanNet, ScanNet200, SceneNN, and 3RScan—demonstrating that our model achieves competitive accuracy with up to 3×faster inference and 3×small model size compared to ESAM, enabling practical deployment in real-world edge scenarios. Code and models will be released.
View details
