48 citations · 67 across the 15 of their papers we have counts for
18 papers
FlashMap: Flow Map Speculative Decoding
Jeff Shen, Zhejian Zhou, Ryan Lee +2
Speculative decoding accelerates large language model (LLM) inference by using a lightweight drafter to propose a block of tokens that the target model verifies in one parallel pas…
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux +20
Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We test this question on the Ray…
Homogeneous Stellar Parameters from Heterogeneous Spectra with Deep Learning
Jeff Shen, Joshua S. Speagle, Shirley Ho
Large-scale spectroscopic surveys have collectively observed millions of stars across the Milky Way, but each derives stellar labels using independent pipelines with distinct model…
MIMIC: A Generative Multimodal Foundation Model for Biomolecules
Siavash Golkar, Jake Kovalic, Irina Espejo Morales +28
Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation models in biology are trained with…
Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme
Rudy Morel, Francesco Pio Ramunno, Jeff Shen +18
Conditional diffusion models provide a natural framework for probabilistic prediction of dynamical systems and have been successfully applied to fluid dynamics and weather predicti…
Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning
Jeff Shen, Francois Lanusse, Liam Holden Parker +24
Sequential scientific data span many resolutions and domains, and unifying them into a common representation is a key step toward developing foundation models for the sciences. Ast…
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Michael McCabe, Payel Mukhopadhyay, Tanya Marwah +22
Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unst…
AION-1: Omnimodal Foundation Model for Astronomical Sciences
Liam Parker, Francois Lanusse, Jeff Shen +24
While foundation models have shown promise across a variety of fields, astronomy still lacks a unified framework for joint modeling across its highly diverse data modalities. In th…
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
The Multimodal Universe Collaboration, Jeroen Audenaert, Micah Bowles +26
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MU…
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Ruben Ohana, Michael McCabe, Lucas Meyer +24
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small…
Iron Snails: non-equilibrium dynamics and spiral abundance patterns
Neige Frankel, David W. Hogg, Scott Tremaine +2
Galaxies are not in a dynamical steady state. They continually undergo perturbations, e.g., from infalling dwarf galaxies and dark-matter substructure. After a dynamical perturbati…
Multiscale Feature Attribution for Outliers
Jeff Shen, Peter Melchior
Machine learning techniques can automatically identify outliers in massive datasets, much faster and more reproducible than human inspection ever could. But finding such outliers i…
Outlier Detection in the DESI Bright Galaxy Survey
Yan Liang, Peter Melchior, ChangHoon Hahn +3
We present an unsupervised search for outliers in the Bright Galaxy Survey (BGS) dataset from the DESI Early Data Release. This analysis utilizes an autoencoder to compress galaxy…
Disentangling Stellar Age Estimates from Galactic Chemodynamical Evolution
Jeff Shen, Joshua S. Speagle, J. Ted Mackereth +2
Stellar ages are key for determining the formation history of the Milky Way, but are difficult to measure precisely. Furthermore, methods that use chemical abundances to infer ages…
The Dragonfly Spectral Line Mapper: Design and First Light
Seery Chen, Deborah M. Lokhorst, Jeff Shen +4
The Dragonfly Spectral Line Mapper (DSLM) is the latest evolution of the Dragonfly Telephoto Array, which turns it into the world's most powerful wide-field spectral line imager. T…
The pathfinder Dragonfly Spectral Line Mapper: Pushing the limits for ultra-low surface brightness spectroscopy
Deborah M. Lokhorst, Seery Chen, Imad Pasha +4
The pathfinder Dragonfly Spectral Line Mapper is a distributed aperture telescope based off of the Dragonfly Telephoto Array with additional instrumentation (the Dragonfly "Filter-…
The Mass of the Milky Way from the H3 Survey
Jeff Shen, Gwendolyn M. Eadie, Norman Murray +8
The mass of the Milky Way is a critical quantity which, despite decades of research, remains uncertain within a factor of two. Until recently, most studies have used dynamical trac…
Molecular gas in a gravitationally lensed galaxy group at
Jeff Shen, Allison W. S. Man, Johannes Zabl +5
Most molecular gas studies of galaxies are of intrinsically bright objects, despite the galaxy population being primarily "normal" galaxies with less extreme star formati…