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 342 publications
Gemini & Physical World: Large Language Models Can Estimate the Intensity of Earthquake Shaking from Multi-Modal Social Media Posts
Aman Raj
Marc Stogaitis
Youngmin Cho
Richard Allen
Patrick Robertson
Robert Bosch
Nivetha Thiruverahan
Alexei Barski
Tajinder Gadh
Geophysical Journal International (2025), ggae436
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This paper presents a novel approach for estimating the ground shaking intensity using real-time social media data and CCTV footage. Employing the Gemini 1.5 Pro’s (Reid et al. 2024) model, a multi-modal language model, we demonstrate the ability to extract relevant information from unstructured data utilizing generative AI and natural language processing. The model’s output, in the form of Modified Mercalli Intensity (MMI) values, align well with independent observational data. Furthermore, our results suggest that beyond its advanced visual and auditory understanding abilities, Gemini appears to utilize additional sources of knowledge, including a simplified understanding of the general relationship between earthquake magnitude, distance, and MMI intensity, which it presumably acquired during its training, in its reasoning and decision-making processes. These findings raise intriguing questions about the extent of Gemini's general understanding of the physical world and its phenomena. Gemini’s ability to generate results consistent with established scientific knowledge highlights the potential of LLMs like Gemini in augmenting our understanding of complex physical phenomena such as earthquakes. More specifically, the results of this study highlight the potential of LLMs like Gemini to revolutionize citizen seismology by enabling rapid, effective, and flexible analysis of crowdsourced data from eyewitness accounts for assessing earthquake impact and providing crisis situational awareness. This approach holds a great promise for improving early warning systems, disaster response, and overall resilience in earthquake-prone regions. This study provides a significant step toward harnessing the power of social media and AI for earthquake disaster mitigation.
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Quantum Computation of Stopping power for Inertial Fusion Target Design
Andrew Baczewski
Alec White
Dominic Berry
Alina Kononov
Joonho Lee
Proceedings of the National Academy of Sciences, 121 (2024), e2317772121
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Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it - one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. We also work out the constant factors associated with a novel implementation of a high order Trotter approach to simulating a grid representation of these systems. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with
roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoCo or P450.
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Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that AI-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time that is similar to or better than the reliability of nowcasts (0-day lead time) from a current state of the art global modeling system (the Copernicus Emergency Management Service Global Flood Awareness System). Additionally, we achieve accuracies over 5-year return period events that are similar to or better than current accuracies over 1-year return period events. This means that AI can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed in this paper was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.
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A scalable system to measure contrail formation on a per-flight basis
Thomas Dean
Sebastian Eastham
Marc Shapiro
Zebediah Engberg
Erica Brand
Dinesh Sanekommu
Ulrike Hager
Joe Ng
Carl Elkin
Environmental Research Communications (2024)
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In this work we describe a scalable, automated system to determine from satellite data whether a given flight has made a persistent contrail.
The system works by comparing flight segments to contrails detected by a computer vision algorithm running on images from the GOES-16 Advanced Baseline Imager. We develop a `flight matching' algorithm and use it to label each flight segment as a `match' or `non-match'. We perform this analysis on 1.6 million flight segments and compare these labels to existing contrail prediction methods based on weather forecast data. The result is an analysis of which flights make persistent contrails several orders of magnitude larger than any previous work. We find that current contrail prediction models fail to correctly predict whether we will match a contrail in many cases.
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This is an invited OFC 2024 conference workshop talk regarding a new type of lower-power datacenter optics design choice: linear pluggable optics. In this talk I will discuss the fundamental performance constraints facing linear pluggable optics and their implications on DCN and ML use cases
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Dynamics of magnetization at infinite temperature in a Heisenberg spin chain
Ben Curtin
Guifre Vidal
Bob Buckley
Tom O'Brien
John Mark Kreikebaum
Rajeev Acharya
Joonho Lee
Ningfeng Zhu
Shirin Montazeri
Sergei Isakov
Jamie Yao
Clarke Smith
Rebecca Potter
Sean Harrington
Jeremy Hilton
Paula Heu
Alexei Kitaev
Alex Crook
Fedor Kostritsa
Kim Ming Lau
Dmitry Abanin
Trent Huang
Aaron Shorter
Steve Habegger
Steven Martin
Gina Bortoli
Seun Omonije
Richard Ross Allen
Charles Rocque
Vladimir Shvarts
Alfredo Torres
Anthony Megrant
Charles Neill
Michael Hamilton
Dar Gilboa
Lily Laws
Nicholas Bushnell
Kyle Anderson
Ramis Movassagh
David Rhodes
Mike Shearn
Wojtek Mruczkiewicz
Desmond Chik
Leonid Pryadko
Xiao Mi
Brooks Foxen
Frank Arute
Alejo Grajales Dau
Yaxing Zhang
Lara Faoro
Alexander Lill
Gordon Hill
JiunHow Ng
Justin Iveland
Marco Szalay
Orion Martin
Juan Campero
Juhwan Yoo
Michael Newman
William Giang
Gonzalo Garcia
Alex Opremcak
Amanda Mieszala
William Courtney
Andrey Klots
Wayne Liu
Pavel Laptev
Paul Conner
Rolando Somma
Vadim Smelyanskiy
Benjamin Chiaro
Grayson Young
Tim Burger
ILYA Drozdov
Agustin Di Paolo
Jimmy Chen
Marika Kieferova
Hung-Shen Chang
Michael Broughton
Negar Saei
Juan Atalaya
Markus Ansmann
Pavol Juhas
Murray Ich Nguyen
Yuri Lensky
Roberto Collins
Élie Genois
Jindra Skruzny
Yu Chen
Reza Fatemi
Leon Brill
Seneca Meeks
Ashley Huff
Doug Strain
Monica Hansen
Noah Shutty
Ebrahim Forati
Doug Thor
Dave Landhuis
Kenny Lee
Ping Yeh
Kunal Arya
Henry Schurkus
Cheng Xing
Cody Jones
Edward Farhi
Vlad Sivak
Raja Gosula
Andre Petukhov
Clint Earle
Alexander Korotkov
Ani Nersisyan
Christopher Schuster
George Sterling
Trond Andersen
Alexandre Bourassa
Salvatore Mandra
Kannan Sankaragomathi
Vinicius Ferreira
Tomaž Prosen
Vedika Khemani
Rhine Samajdar
Jesse Hoke
Sarang Gopalakrishnan
Andrew Dunsworth
Bill Huggins
Markus Hoffmann
Alexis Morvan
Josh Cogan
Science, 384 (2024), pp. 48-53
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Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain’s center, P(M). The first two moments of P(M) show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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Stable quantum-correlated many-body states through engineered dissipation
Sara Shabani
Dripto Debroy
Jerome Lloyd
Alexios Michailidis
Andrew Dunsworth
Bill Huggins
Markus Hoffmann
Alexis Morvan
Josh Cogan
Ben Curtin
Guifre Vidal
Bob Buckley
Tom O'Brien
John Mark Kreikebaum
Rajeev Acharya
Joonho Lee
Ningfeng Zhu
Shirin Montazeri
Sergei Isakov
Jamie Yao
Clarke Smith
Rebecca Potter
Sean Harrington
Jeremy Hilton
Paula Heu
Alexei Kitaev
Alex Crook
Fedor Kostritsa
Kim Ming Lau
Dmitry Abanin
Trent Huang
Aaron Shorter
Steve Habegger
Gina Bortoli
Charles Rocque
Vladimir Shvarts
Alfredo Torres
Anthony Megrant
Charles Neill
Michael Hamilton
Dar Gilboa
Lily Laws
Nicholas Bushnell
Ramis Movassagh
Mike Shearn
Wojtek Mruczkiewicz
Desmond Chik
Leonid Pryadko
Xiao Mi
Brooks Foxen
Frank Arute
Alejo Grajales Dau
Yaxing Zhang
Lara Faoro
Alexander Lill
JiunHow Ng
Justin Iveland
Marco Szalay
Orion Martin
Juhwan Yoo
Michael Newman
William Giang
Alex Opremcak
Amanda Mieszala
William Courtney
Andrey Klots
Wayne Liu
Pavel Laptev
Charina Chou
Paul Conner
Rolando Somma
Vadim Smelyanskiy
Benjamin Chiaro
Grayson Young
Tim Burger
ILYA Drozdov
Agustin Di Paolo
Jimmy Chen
Marika Kieferova
Michael Broughton
Negar Saei
Juan Atalaya
Markus Ansmann
Pavol Juhas
Murray Ich Nguyen
Yuri Lensky
Roberto Collins
Élie Genois
Jindra Skruzny
Igor Aleiner
Yu Chen
Reza Fatemi
Leon Brill
Ashley Huff
Doug Strain
Monica Hansen
Noah Shutty
Ebrahim Forati
Dave Landhuis
Kenny Lee
Ping Yeh
Kunal Arya
Henry Schurkus
Cheng Xing
Cody Jones
Edward Farhi
Raja Gosula
Andre Petukhov
Alexander Korotkov
Ani Nersisyan
Christopher Schuster
George Sterling
Kostyantyn Kechedzhi
Trond Andersen
Alexandre Bourassa
Kannan Sankaragomathi
Vinicius Ferreira
Science, 383 (2024), pp. 1332-1337
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Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models
John Anderson
Science Advances, 10 (2024), eadk4489
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Probabilistic forecasting is crucial to decision-making under uncertainty about future weather. The dominant approach is to use an ensemble of forecasts to represent and quantify uncertainty in operational numerical weather prediction. However, generating ensembles is computationally costly. In this paper, we propose to generate ensemble forecasts at scale by leveraging recent advances in generative artificial intelligence. Our approach learns a data-driven probabilistic diffusion model from the 5-member ensemble GEFS reforecast dataset. The model can then be sampled efficiently to produce realistic weather forecasts, conditioned on a few members of the operational GEFS forecasting system. The generated ensembles have similar predictive skill as the full GEFS 31-member ensemble, evaluated against ERA5 reanalysis, and emulate well the statistics of large physics-based ensembles. We also apply the same methodology to developing a diffusion model for generative post-processing: the model directly learns to correct biases present in the emulated forecasting system by leveraging reanalysis data as labels during training. Ensembles from this generative post-processing model show greater reliability and accuracy, particularly in extreme event classification. In general, they are more reliable and forecast the probability of extreme weather more accurately than the GEFS operational ensemble. Our models achieve these results at less than 1/10th of the computational cost incurred by the operational GEFS system.
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Ubiquitous and Low-Cost Generation of Elevation Pseudo Ground Control Points
Moustafa Youssef
Etienne Le Grand
14th International Conference on Indoor Positioning and Indoor Navigation (IPIN). Hong Kong, China, 2024.
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In this paper, we design a system to generate Pseudo Ground Control Points (PGCPs) using standard low-cost widely available GNSS receivers in a crowd-sourcing manner. We propose a number of GNSS points filters that removes different causes of errors and biases, and design a linear regression height estimator leading to high-accuracy PGCP elevations. Evaluation of our system shows that the PGCPs can achieve a median accuracy of 22.5 cm in 25 metropolitan areas in the USA.
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BEYOND THE CODE: AI REGULATIONS AS THE SECRET COMPASS OF ENGINEERING MANAGERS
Proceedings of the American Society for Engineering Management 2024 International Annual Conference (2024)
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Technology is a product of society. As technology evolves, the norms governing it have to mature for enabling its proper use within the society. The interest in Artificial Intelligence (AI) has surged following the introduction of chatGPT. Firms, both large and small, are competing to develop new products and solutions involving AI. Amidst these developments, leading corporations such as Google and Microsoft have proactively committed to upholding responsible innovation in AI development. Governments worldwide are responding with the creation of guidelines and regulations in the field. Notably, in March 2024, the United Nations General Assembly (UNGA) adopted landmark regulation on AI.
At the heart of these developments in AI are engineering managers who leverage technical advances to build products and services that create value. To effectively harness AI for human benefit, engineering managers must be aware of these evolving regulations governing AI. Some regulations such as Digital Markets Act (DMA) and General Data Protection Regulations (GDPR) have far reaching consequences for organizations globally. Having a working knowledge of these statutory requirements will enable engineering managers to identify the opportunities and constraints in leveraging AI technology while building products and services. It will allow them to make informed decisions about data collection methods, model training processes, the deployment of AI systems and metrics for their evaluation. At scale, it can become a competitive advantage for the firms they work in, as explored through real-world examples in this paper.
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Generative AI models, including large language models and multimodal models that include text and other media, are on the cusp of transforming many aspects of modern life, including entertainment, education, civic life, the arts, and a range of professions. There is potential for Generative AI to have a substantive impact on the methods and pace of discovery for a range of scientific disciplines. We interviewed twenty scientists from a range of fields (including the physical, life, and social sciences) to gain insight into whether or how Generative AI technologies might add value to the practice of their respective disciplines, including not only ways in which AI might accelerate scientific discovery (i.e., research), but also other aspects of their profession, including the education of future scholars and the communication of scientific findings. In addition to identifying opportunities for Generative AI to augment scientists’ current practices, we also asked participants to reflect on concerns about AI. These findings can help guide the responsible development of models and interfaces for scientific education, inquiry, and communication.
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With modern fluorescent probes and light microscopes, the possibilities of monitoring the dynamics of cells, organelles, molecular complexes and even single molecules with high spatiotemporal resolution are more than ever [1]. Characterizing the motion of cellular and molecular entities can reveal a great deal of information about their functions and interactions and overall activity landscape [2]. Motion characterization is generally the end product of an image analysis pipeline that starts with identifying and localizing the objects in each image (segmentation/detection), tracking them over time, and then analyzing the resulting trajectories to characterize the objects’ motion. Whether objects are large (e.g. cells or organelles) or small (e.g. molecules or molecular complexes), as long as they can be represented by coordinates (e.g. cell centroid position or molecular position), following them over time in a series of images is effectively a (multiple) particle tracking problem. In their recent publication in Nature Machine Intelligence, Pineda et al. [3] describe a powerful deep learning approach based on graph neural networks (GNNs) for particle tracking or, if desired, for the characterization of object motion without explicit tracking.
Particle tracking is often the most challenging step in the analysis pipeline from images to motion characterization. Whenever the analysis involves a relatively high density of heterogeneously moving objects, there is ambiguity in determining which object has gonewent where throughout the image series [4]. In addition, objects may merge with each other – due to crossing paths or interactions – and may undergo splitting, such as during cell division. Despite these challenges, a high density of tracked objects is often desired, because of the rich information that it yields about the system studied [5]. The novel GNN-based approach by Pineda et al. [3], named MAGIK, offers solutions to the tracking problem in two ways: First, MAGIK can be employed to construct the trajectories of the imaged objects from the graph of their connections in space and time. Second, MAGIK can be employed to characterize the motion of the imaged objects directly from their graph of spatiotemporal connections, without explicit tracking.
Graphs are ubiquitously used in science to represent complex systems of interacting objects, from molecules to social and transportation networks [6]. GNNs provide a framework for incorporating existing information about the objects, with an inductive bias based on a larger structure relating them, to make predictions about these objects or the system as a whole. In MAGIK [3], spatiotemporal connections between imaged objects, encoded in the structure of the graph, provide this inductive bias , with the premise that objects close in space-time are likely to be the same. MAGIK utilizes this graph representation in a powerful way by employing GNNs [7] to perform various tracking and motion characterization tasks. The GNN model proposed in MAGIK considers both spatial and temporal information in a static graph. This model is enhanced by an adaptive and interpretable attention mechanism. Attention estimates the strength of association among the objects and provides insights into the dynamics of the system for the task.
GNNs enable MAGIK to provide a versatile platform for performing multiple tasks from linking coordinates into trajectories to inferring local and global dynamic properties. MAGIK is tested for its flexibility and reliability in real and simulated scenarios corresponding to a variety of biological experiments. The results of the tests show that MAGIK is able to identify which spatiotemporal connections in a graph influence the dynamic properties of each object. They further show that MAGIK accurately constructs trajectories, obtaining outstanding results for cell tracking, including the identification of cell division events, using multiple microscopy techniques and cell types. As in most applications the final goal of tracking is to characterize the dynamics of the system, Pineda et al. [3] have tested MAGIK for quantifying motion parameters without explicit tracking, and they have shown that MAGIK can accurately and sensitively quantify local or global motion properties of the imaged objects. Technically, MAGIK performs these various tasks by tailoring its training to the task: Tracking as a graph edge classification task, local motion motion characterization as a graph node regression task, and global motion characterization as a graph-level regression or classification task.
As demonstrated by MAGIK, GNNs offer powerful tools for the analysis of the spatiotemporal connections between objects in biological images. New developments in the fields of graphs and GNNs will further advance this goal. One possibility is to replace the fixed graph and the fully connected graph in MAGIK with a learnable sparse graph [8]. Another possibility is to use hypergraphs, which go beyond binary connections (a fundamental limitation of graphs). This would be a promising approach to characterize the spatiotemporal connections of systems with complex interactions [9]. Furthermore, as the problem studied here is temporal in nature, it may benefit from temporal GNNs [10], which directly incorporate time into the GNN formulation. All in all, the powerful combination of cutting-edge microscopes, fluorescent probes and geometric deep learning analytical tools will aid with the study of the organization, dynamics and interactions of diverse systems, from molecules in a cell, to cells in a tissue, and beyond.
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Estimates of broadband upwelling irradiance from GOES-16 ABI
Vincent Rudolf Meijer
Joe Ng
Carl Elkin
Sixing Chen
Geoff Davis
Remote Sensing of Environment, 285 (2023)
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Satellite-derived estimates of the Earth’s radiation budget are crucial for understanding and predicting the weather and climate. However, existing satellite products measuring broadband outgoing longwave radiation (OLR) and reflected shortwave radiation (RSR) have spatio-temporal resolutions that are too coarse to evaluate important radiative forcers like aircraft condensation trails. We present a neural network which estimates OLR and RSR based on narrowband radiances, using collocated Cloud and Earth’s Radiant Energy System (CERES) and GOES-16 Advanced Baseline Imager (ABI) data. The resulting estimates feature strong agreement with the CERES data products (R^2 = 0.977 for OLR and 0.974 for RSR on CERES Level 2 footprints), and we provide open access to the collocated satellite data and model outputs on all available GOES-16 ABI data for the 4 years from 2018–2021.
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Chimane-Mosetén
Jeanette Sakel
Amazonian Languages: An International Handbook, De Gruyter Mouton (2023)
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Chimane-Mosetén (also known as Mosetenan; ISO 639–3: cas; Glottocode: mose1249) is a dialect continuum spoken by 13,500–16,000 people in the Amazonian region of northern Bolivia. It has not been convincingly shown to be related to any other language. Its status as an isolate makes it unique in many respects, not least in its combination of features typical of both Amazonian and Andean languages. Like its closer geographical neighbors in Amazonian Bolivia, including Movima, Tacana, Reyesano, and Cavineña, it exhibits contrastive nasality in the vowel system and is head marking and predominantly agglutinative. Bound pronominal forms marking arguments in the clause have the same form as bound pronominals marking possessors. Subordinate clauses typically involve nominalized verbs. Unlike most of its Amazonian neighbors, on the other hand, it does not have a semantically-based classifier or gender system but instead features arbitrarily assigned masculine or feminine gender. It also does not feature any incorporation of nouns, adverbs, or adpositions. It has an extensive oblique case-marking system, though core case-marking does not occur. More similar to Quechua and other Andean languages, it features a complex predicate-argument agreement system in which one or more agreement suffixes cross-reference the subject and object arguments of a transitive verb. It also has a large class of lexical numbers following a decimal numeral system.
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Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Dimitar Georgiev
Artur Petrov Toshev
Filip Ekström Kelvinius
Johannes Gasteiger
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS) (2023)
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Recent advances in graph neural networks (GNNs) have allowed molecular simulations with accuracy on par with conventional gold-standard methods at a fraction of the computational cost. Nonetheless, as the field has been progressing to bigger and more complex architectures, state-of-the-art GNNs have become largely prohibitive for many large-scale applications. In this paper, we, for the first time, explore the utility of knowledge distillation (KD) for accelerating molecular GNNs. To this end, we devise KD strategies that facilitate the distillation of hidden representations in directional and equivariant GNNs and evaluate their performance on the regression task of energy and force prediction. We validate our protocols across different teacher-student configurations and demonstrate that they can boost the predictive accuracy of student models without altering their architecture. We also conduct comprehensive optimization of various components of our framework, and investigate the potential of data augmentation to further enhance performance. All in all, we manage to close as much as 59% of the gap in predictive accuracy between models like GemNet-OC and PaiNN with zero additional cost at inference.
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