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 10501 publications
Deep Multi-modal Species Occupancy Modeling
Timm Haucke
Yunyi Shen
Levente Klein
David Rolnick
Lauren Gillespie
Sara Beery
bioRxiv (2025)
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Occupancy models are tools for modeling the relationship between habitat and species occurrence while accounting for the fact that species may still be present even if not detected. The types of environmental variables typically used for characterizing habitats in such ecological models, such as precipitation or tree cover, are frequently of low spatial resolution, with a single value for a spatial pixel size of, e.g., 1km2. This spatial scale fails to capture the nuances of micro-habitat conditions that can strongly influence species presence, and additionally, as many of these are derived from satellite data, there are aspects of the environment they cannot capture, such as the structure of vegetation below the forest canopy. We propose to combine high-resolution satellite and ground-level imagery to produce multi-modal environmental features that better capture micro-habitat conditions, and incorporate these multi-modal features into hierarchical Bayesian species occupancy models. We leverage pre-trained deep learning models to flexibly capture relevant information directly from raw imagery, in contrast to traditional approaches which rely on derived and/or hand-crafted sets of ecosystem covariates. We implement deep multi-modal species occupancy modeling using a new open-source Python package for ecological modeling, designed for bridging machine learning and statistical ecology. We test our method under a strict evaluation protocol on 16 mammal species across thousands of camera traps in Snapshot USA surveys, and find that multi-modal features substantially enhance predictive power compared to traditional environmental variables alone. Our results not only highlight the predictive value and complementarity of in-situ samples, but also make the case for more closely integrating deep learning models and traditional statistical ecological models.
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Perceptual Audio Coding: A 40-Year Historical Perspective
Juergen Herre
Schuyler Quackenbush
Minje Kim
2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2025)
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In the history of audio and acoustic signal processing perceptual audio coding has certainly excelled as a bright success story by its ubiquitous deployment in virtually all digital media devices, such as computers, tablets, mobile phones, set-top-boxes, and digital radios. From a technology perspective, perceptual audio coding has undergone tremendous development from the first very basic perceptually driven coders (including the popular mp3 format) to today’s full-blown integrated coding/rendering systems. This paper provides a historical overview of this research journey by pinpointing the pivotal development steps in the evolution of perceptual audio coding. Finally, it provides thoughts about future directions in this area.
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Users of routing services like Apple Maps, Google Maps, and Waze frequently wonder why a given route is proposed. This question particularly arises when dynamic conditions like traffic and road closures cause unusual routes to be proposed. While many such dynamic conditions may exist in a road network at any time, only a small fraction of those conditions are typically relevant to a given user's route. In this work, we give a simple algorithm that identifies a small set of traffic-laden road segments that answer the following question: Which traffic conditions cause a particular shortest traffic-aware route to differ from the shortest traffic-free route? We theoretically and experimentally show that our algorithm generates small and interpretable answers to this question.
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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)
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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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Measuring software development can help drive impactful change. However, it’s a complex task, and getting started can be daunting as it involves understanding what you should measure, and determining what you can measure. This article provides a guide to selecting a framework that aligns with organizational measurement strategy.
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Matryoshka Model Learning for Improved Elastic Student Models
Chetan Verma
Cho-Jui Hsieh
Ngot Bui
Yang Zhang
Wen Chen
Xin Liu
Inderjit Dhillon
2025
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Industry-grade ML models are carefully designed to meet rapidly evolving serving constraints, which requires significant resources for model development. In this paper, we propose MatTA, a framework for training multiple accurate Student models using a novel Teacher-TA-Student recipe. TA models are larger versions of the Student models with higher capacity, and thus allow Student models to better relate to the Teacher model and also bring in more domain-specific expertise. Furthermore, multiple accurate Student models can be extracted from the TA model. Therefore, despite only one training run, our methodology provides multiple servable options to trade off accuracy for lower serving cost. We demonstrate the proposed method, MatTA, on proprietary datasets and models. Its practical efficacy is underscored by live A/B tests within a production ML system, demonstrating 20% improvement on a key metric. We also demonstrate our method on GPT-2 Medium, a public model, and achieve relative improvements of over 24% on SAT Math and over 10% on the LAMBADA benchmark.
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This tutorial examines the progress and scaling limitations of IM-DD based optical technologies and explores how datacenter use cases optimized coherent technology, including a newly proposed polarization-folding, time-diversity approach and a novel single-sideband coherent detection technology—can address some of these challenges
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Judging an action’s safety requires knowledge of the context in which the action takes place. To human agents who act in various contexts, this may seem obvious: performing an action such as email deletion may or may not be appropriate depending on the email’s content, the goal (e.g., to erase sensitive emails or to clean up trash), and the type of email address (e.g., work or personal). Unlike people, computational systems have often had only limited agency in limited contexts. Thus, manually crafted policies and user confirmation (e.g., smartphone app permissions or network access control lists), while imperfect, have sufficed to restrict harmful actions. However, with the upcoming deployment of generalist agents that support a multitude of tasks (e.g., an automated personal assistant), we argue that we must rethink security designs to adapt to the scale of contexts and capabilities of these systems. As a first step, this paper explores contextual security in the domain of agents and proposes contextual agent security (Conseca), a framework to generate just-in-time, contextual, and human-verifiable security policies.
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Deletion Robust Non-Monotone Submodular Maximization over Matroids
Paul Duetting
Federico Fusco
Ashkan Norouzi Fard
Journal of Machine Learning Research, 26 (2025), pp. 1-28
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Maximizing a submodular function is a fundamental task in machine learning and in this paper we study the deletion robust version of the problem under the classic matroids constraint. Here the goal is to extract a small size summary of the dataset that contains a high value independent set even after an adversary deleted some elements. We present constant-factor approximation algorithms, whose space complexity depends on the rank $k$ of the matroid and the number $d$ of deleted elements. In the centralized setting we present a $(4.597+O(\eps))$-approximation algorithm with summary size $O( \frac{k+d}{\eps^2}\log \frac{k}{\eps})$ that is improved to a $(3.582+O(\eps))$-approximation with $O(k + \frac{d}{\eps^2}\log \frac{k}{\eps})$ summary size when the objective is monotone. In the streaming setting we provide a $(9.435 + O(\eps))$-approximation algorithm with summary size and memory $O(k + \frac{d}{\eps^2}\log \frac{k}{\eps})$; the approximation factor is then improved to $(5.582+O(\eps))$ in the monotone case.
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DroidCCT: Cryptographic Compliance Test via Trillion-Scale Measurement
Preview
Rémi Audebert
Pedro Barbosa
Borbala Benko
Alex (Mac) Mihai
László Siroki
Catherine Vlasov
Annual Computer Security Applications Conference (ACSAC) (2025) (to appear)
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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Reasoning-SQL: Reinforcement Learning with Partial Rewards for Reasoning-Enhanced Text-to-SQL
Mohammadreza Pourreza
Shayan Talaei
Hailong Li
Azalia Mirhoseini
Amin Saberi
Conference on Language Modeling (COLM) (2025) (to appear)
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Text-to-SQL is a challenging task involving multiple reasoning-intensive subtasks, including natural language understanding, database schema comprehension, and precise SQL query formulation. Existing approaches often rely on handcrafted reasoning paths with inductive biases that can limit their overall effectiveness. Motivated by the recent success of reasoning-enhanced models such as DeepSeek R1 and OpenAI o1, which effectively leverage reward-driven self-exploration to enhance reasoning capabilities and generalization, we propose a novel set of partial rewards tailored specifically for the Text-to-SQL task. Our reward set includes schema-linking, AI feedback, n-gram similarity, and syntax check, explicitly designed to address the reward sparsity issue prevalent in reinforcement learning (RL). Leveraging group relative policy optimization (GRPO), our approach explicitly encourages large language models (LLMs) to develop intrinsic reasoning skills necessary for accurate SQL query generation. With models of different sizes, we demonstrate that RL-only training with our proposed rewards consistently achieves higher accuracy and superior generalization compared to supervised fine-tuning (SFT). Remarkably, our RL-trained 14B-parameter model significantly outperforms larger proprietary models, e.g. o3-mini by 4% and Gemini-1.5-Pro-002 by 3% on the BIRD benchmark. These highlight the efficacy of our proposed RL-training framework with partial rewards for enhancing both accuracy and reasoning capabilities in Text-to-SQL 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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Visualizing Dynamics of Charges and Strings in (2+1)D Lattice Gauge Theories
Tyler Cochran
Bernhard Jobst
Yuri Lensky
Gaurav Gyawali
Norhan Eassa
Melissa Will
Aaron Szasz
Dmitry Abanin
Rajeev Acharya
Laleh Beni
Trond Andersen
Markus Ansmann
Frank Arute
Kunal Arya
Abe Asfaw
Juan Atalaya
Brian Ballard
Alexandre Bourassa
Michael Broughton
David Browne
Brett Buchea
Bob Buckley
Tim Burger
Nicholas Bushnell
Anthony Cabrera
Juan Campero
Hung-Shen Chang
Jimmy Chen
Benjamin Chiaro
Jahan Claes
Agnetta Cleland
Josh Cogan
Roberto Collins
Paul Conner
William Courtney
Alex Crook
Ben Curtin
Sayan Das
Laura De Lorenzo
Agustin Di Paolo
Paul Donohoe
ILYA Drozdov
Andrew Dunsworth
Alec Eickbusch
Aviv Elbag
Mahmoud Elzouka
Vinicius Ferreira
Ebrahim Forati
Austin Fowler
Brooks Foxen
Suhas Ganjam
Robert Gasca
Élie Genois
William Giang
Dar Gilboa
Raja Gosula
Alejo Grajales Dau
Dietrich Graumann
Alex Greene
Steve Habegger
Monica Hansen
Sean Harrington
Paula Heu
Oscar Higgott
Jeremy Hilton
Robert Huang
Ashley Huff
Bill Huggins
Cody Jones
Chaitali Joshi
Pavol Juhas
Hui Kang
Amir Karamlou
Kostyantyn Kechedzhi
Trupti Khaire
Bryce Kobrin
Alexander Korotkov
Fedor Kostritsa
John Mark Kreikebaum
Vlad Kurilovich
Dave Landhuis
Tiano Lange-Dei
Brandon Langley
Kim Ming Lau
Justin Ledford
Kenny Lee
Loick Le Guevel
Wing Li
Alexander Lill
Will Livingston
Daniel Lundahl
Aaron Lunt
Sid Madhuk
Ashley Maloney
Salvatore Mandra
Leigh Martin
Orion Martin
Cameron Maxfield
Seneca Meeks
Anthony Megrant
Reza Molavi
Sebastian Molina
Shirin Montazeri
Ramis Movassagh
Charles Neill
Michael Newman
Murray Ich Nguyen
Chia Ni
Kris Ottosson
Alex Pizzuto
Rebecca Potter
Orion Pritchard
Ganesh Ramachandran
Matt Reagor
David Rhodes
Gabrielle Roberts
Kannan Sankaragomathi
Henry Schurkus
Mike Shearn
Aaron Shorter
Noah Shutty
Vladimir Shvarts
Vlad Sivak
Spencer Small
Clarke Smith
Sofia Springer
George Sterling
Jordan Suchard
Alex Sztein
Doug Thor
Mert Torunbalci
Abeer Vaishnav
Justin Vargas
Sergey Vdovichev
Guifre Vidal
Steven Waltman
Shannon Wang
Brayden Ware
Kristi Wong
Cheng Xing
Jamie Yao
Ping Yeh
Bicheng Ying
Juhwan Yoo
Grayson Young
Yaxing Zhang
Ningfeng Zhu
Yu Chen
Vadim Smelyanskiy
Adam Gammon-Smith
Frank Pollmann
Michael Knap
Nature, 642 (2025), 315–320
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Lattice gauge theories (LGTs) can be used to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging, as it requires solving many-body problems that are generally beyond perturbative limits. Here we investigate the dynamics of local excitations in a LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit that prepares low-energy states that have a large overlap with the ground state; then we create charge excitations with local gates and simulate their quantum dynamics by means of a discretized time evolution. As the electric field coupling constant is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the electric field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT, from which we uncover two distinct regimes inside the confining phase: for weak confinement, the string fluctuates strongly in the transverse direction, whereas for strong confinement, transverse fluctuations are effectively frozen. We also demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a new set of techniques for investigating emergent excitations and string dynamics.
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Collaborative Diffusion Model for Recommender System
Gyuseok Lee
Yaochen Zhu
Hwanjo Yu
Yao Zhou
Jundong Li
2025
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Diffusion-based recommender systems (DR) have gained increasing attention for their advanced generative and denoising capabilities. However, existing DR face two central limitations: (i) a trade-off between enhancing generative capacity via noise injection and retaining the loss of personalized information. (ii) the underutilization of rich item-side information. To address these challenges, we present a Collaborative Diffusion model for Recommender System (CDiff4Rec). Specifically, CDiff4Rec generates pseudo-users from
item features and leverages collaborative signals from both real and pseudo personalized neighbors identified through behavioral similarity, thereby effectively reconstructing nuanced user preferences. Experimental results on three public datasets show that CDiff4Rec outperforms competitors by effectively mitigating the loss of personalized information through the integration of item content and collaborative signals.
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