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 10422 publications
Preview abstract
Mainstream artificial neural network models, such as Deep Neural Networks (DNNs) are computation-heavy and energy-hungry. Weightless Neural Networks (WNNs) are natively built with RAM-based neurons and represent an entirely distinct type of neural network computing compared to DNNs. WNNs are extremely low-latency, low-energy, and suitable for efficient, accurate, edge inference. The WNN approach derives an implicit inspiration from the decoding process observed in the dendritic trees of biological neurons, making neurons based on Random Access Memories (RAMs) and/or Lookup Tables (LUTs) ready-to-deploy neuromorphic digital circuits. Since FPGAs are abundant in LUTs, LUT based WNNs are a natural fit for implementing edge inference in FPGAs.
WNNs has been demonstrated to be an energetically efficient AI model, both in software, as well as in hardware. For instance, the most recent DWN – Differential Weightless Neural Network – model demonstrates up to 135× reduction in energy costs in FPGA implementations compared to other multiplication-free approaches, such as binary neural networks (BNNs) and DiffLogicNet, up to 9% higher accuracy in deployments on constrained devices, and culminate in up to 42.8× reduction in circuit area for ultra-low-cost chip implementations. This tutorial will help participants understand how WNNs work, why WNNs were underdogs for such a long time, and be introduced to the most recent members of the WNN family, such as BTHOWeN , LogicWiSARD, COIN, ULEEN and DWN, and contrast to BNNs and LogicNets.
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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
Preview abstract
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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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)
Preview abstract
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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Study of Arterials in the City of Rio de Janeiro for Traffic Coordination
Eliav Buchnik
Danny Veikherman
Dan Karliner
Tom Kalvari
Shai Ferster
Ron Tsibulsky
Jack Haddad
2025
Preview abstract
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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Preview abstract
Delay monitoring is a commonly arising problem in applications such as queue management systems, scheduling, and traffic monitoring. Motivated by such applications, we formulate a queue monitoring problem, where there is a FIFO queue with arbitrary arrivals and departures, and a server needs to monitor the length of a queue by using (decentralized) pings from packets in the queue. Packets can send pings informing the server about the number of packets ahead of them in the queue. Via novel online algorithms and lower bounds, we tightly characterize the trade-off between the number of pings sent and the accuracy of the server's real time estimates. Further, our approximate estimates can be made to be accurate to an arbitrary precision.
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Databases in the Era of Memory-Centric Computing
Yannis Chronis
Anastasia Ailamaki
Lawrence Benson
Helena Caminal
Jana Gičeva
Eric Seldar
Lisa Wu Wills
2025
Preview abstract
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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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)
Preview abstract
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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SSDTrain: Faster Large Language Model Training Using SSD-Based Activation Offloading
Kun Wu
Jeongmin Brian Park
Mert Hidayetoğlu
Vikram Sharma Mailthody
Sitao Huang
Steven Lumetta
Wen-mei Hwu
Design Automation Conference (DAC) (2025)
Preview abstract
The scaling up of Large Language Models (LLMs) demands more memory than current GPUs can provide, hindering the training process. To address this challenge, we propose SSDTrain to efficiently offload activations, the intermediate tensors produced during LLM training, to SSDs. This approach reduces GPU memory usage without impacting performance by adaptively overlapping data transfers with computation. SSDTrain is compatible with popular deep learning frameworks like PyTorch, Megatron, and DeepSpeed, and it employs techniques such as tensor deduplication, forwarding, and adaptive offloading to further enhance efficiency. We conduct extensive experiments on Llama, BERT, and T5. Results demonstrate that SSDTrain effectively reduces 45% of the activation peak memory usage. It can perfectly overlap the IO with the computation without introducing performance penalty. SSDTrain can achieve a performance boost of up to 31% compared to the conventional training strategy using the same GPU systems.
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Scaling Wearable Foundation Models
Girish Narayanswamy
Kumar Ayush
Yuzhe Yang
Orson Xu
Shun Liao
Shyam Tailor
Jake Sunshine
Tim Althoff
Shrikanth (Shri) Narayanan
Jiening Zhan
Mark Malhotra
Shwetak Patel
Samy Abdel-Ghaffar
Daniel McDuff
2025
Preview abstract
Wearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large volumes of data. However, making sense of these observations for scientific and actionable insights is non-trivial. Inspired by the empirical success of generative modeling, where large neural networks learn powerful representations from vast amounts of text, image, video, or audio data, we investigate the scaling properties of wearable sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, accelerometer, electrodermal activity, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM, a multimodal foundation model built on the largest wearable-signals dataset with the most extensive range of sensor modalities to date. Our results establish the scaling laws of LSM for tasks such as imputation, interpolation and extrapolation across both time and sensor modalities. Moreover, we highlight how LSM enables sample-efficient downstream learning for tasks including exercise and activity recognition.
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Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning
Preview
Mingfei Lau
Allen Chen
Yeming Fang
Tingting Xu
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), Vienna, Austria (2025), 7466–7492
Global earthquake detection and warning using Android phones
Marc Stogaitis
Youngmin Cho
Richard Allen
Boone Spooner
Patrick Robertson
Micah Berman
Greg Wimpey
Robert Bosch
Nivetha Thiruverahan
Steve Malkos
Alexei Barski
Science, 389 (2025), pp. 254-259
Preview abstract
Earthquake early-warning systems are increasingly being deployed as a strategy to reduce losses in earthquakes, but the regional seismic networks they require do not exist in many earthquake-prone countries. We use the global Android smartphone network to develop an earthquake detection capability, an alert delivery system, and a user feedback framework. Over 3 years of operation, the system detected an average of 312 earthquakes per month with magnitudes from M 1.9 to M 7.8 in Türkiye. Alerts were delivered in 98 countries for earthquakes with M ≥4.5, corresponding to ~60 events and 18 million alerts per month. User feedback shows that 85% of people receiving an alert felt shaking, and 36, 28, and 23% received the alert before, during, and after shaking, respectively. We show how smartphone-based earthquake detection algorithms can be implemented at scale and improved through postevent analysis.
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The Case for Leveraging Transport Signals to Improve Internet Speed Test Efficiency
Cristina Leon
Computer Communication Review (2025) (to appear)
Preview abstract
Internet speed tests are an important tool to enable consumers and regulators to monitor the quality of Internet access. However, increased Internet speeds to the home and an increased demand for speed testing pose scaling challenges to providers of speed tests, who must maintain costly infrastructure to keep up with this demand. In recent years, this has led the popular NDT speed test to limit data transfer to a total of 250MB, which comes at the cost of accuracy for high bandwidth speed test clients.
In this paper, we observe that the NDT speed test server’s congestion control algorithm (BBRv1) is also trying to estimate the capacity of the connection. We leverage this observation and signals from BBR to improve the accuracy and efficiency of speed tests. We first show how leveraging signals from BBR can more than double the accuracy of a 10MB test–from 17% to 43%–for clients with speeds over 400Mbps.
We then show how using BBR signals to adaptively end the speed test reduces data transfer by 36% and increased accuracy by 13% for high bandwidth clients, relative to a 100MB fixed length test. Even accounting for clients that never observe enough samples to utilize the BBR signal, this adaptive approach still uses 25% less data than a fixed 100MB test with 37-44% higher accuracy.
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Beyond Digital Literacy: Building Youth Digital Resilience Through Existing “Information Sensibility” Practices
Mia Hassoun
Ian Beacock
Todd Carmody
Patrick Gage Kelley
Beth Goldberg
Devika Kumar
Laura Murray
Rebekah Park
Behzad Sarmadi
Social Sciences Journal, 14(4) (2025)
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Youth media consumption and disordered eating practices have historically been subjects of moral panics, often resulting in protective, deficit-based interventions like content removal. We argue for interventions which instead equip youth to evaluate and manage risks in their online environments, building upon their existing “information sensibility” practices. Drawing upon ethnographic research and intervention testing with 77 participants in the US and India, we analyze how youth (aged 13–26), including those with diverse political perspectives and those recovering from disordered eating (DE), engage with online news and health information. Participants generally algorithmically encountered (rather than searched for) information online, and their engagement was shaped more by social motivations—like belonging—than truth seeking. Participants interpreted online information collaboratively, relying on social cues and peer validation within their online communities. They demonstrated preference for personal testimonies and relatable sources, particularly those with similar social identities. We propose resilience-building interventions that build upon these youth online information practices by: (1) leveraging peer networks, promoting critical information engagement through collaborative learning and peer-to-peer support within online communities; (2) developing social media sensibility, equipping youth to critically evaluate information sources in situ; (3) providing pathways offline, connecting youth to desired in-person communities; and (4) encouraging probabilistic thinking.
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Generative AI for medical education: Insights from a case study with medical students and an AI tutor for clinical reasoning
Amy Wang
Roma Ruparel
Paul Jhun
Julie Anne Seguin
Patricia Strachan
Renee Wong
2025
Preview abstract
Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), have demonstrated significant potential in clinical reasoning skills such as history-taking and differential diagnosis generation—critical aspects of medical education. This work explores how LLMs can augment medical curricula through interactive learning. We conducted a participatory design process with medical students, residents and medical education experts to co-create an AI-powered tutor prototype for clinical reasoning. As part of the co-design process, we conducted a qualitative user study, investigating learning needs and practices via interviews, and conducting concept evaluations through interactions with the prototype. Findings highlight the challenges learners face in transitioning from theoretical knowledge to practical application, and how an AI tutor can provide personalized practice and feedback. We conclude with design considerations, emphasizing the importance of context-specific knowledge and emulating positive preceptor traits, to guide the development of AI tools for medical education.
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Mix&Slice
Marco Rosa
Encyclopedia of Cryptography, Security and Privacy, Springer Nature Switzerland (2025), pp. 1550-1555
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Mix&Slice is an encryption technique that enables efficient and robust access revocation on resources stored at external cloud providers. The technique makes use of a transformation that provides strong inter-dependency in the encrypted representation of a resource. To perform access revocation, it is then sufficient to re-encrypt a small portion of the resource to have guarantees that the resource (and any of its parts) will become unintelligible to those from whom access has been revoked.
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