1 citations · 3 across the 19 of their papers we have counts for
22 papers
Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data
Guang Zhao, Xihaier Luo, Sean McCue +5
Implicit neural representations (INRs) offer a powerful framework for modeling high-dimensional physical fields, but their training remains inefficient because evaluating dense coo…
Efficient Neural Field Learning via Adaptive Coverage and Focused Sampling
Guang Zhao, Xihaier Luo, Huan-Hsin Tseng +4
Implicit neural representations (INRs) provide a flexible framework for modeling high-dimensional continuous fields, but their training is often inefficient due to uniform subsampl…
DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Brandon R. Feng, Brian J. Reich, Daniel Beaglehole +7
The recent developments of complex deep learning models have led to unprecedented ability to accurately predict across multiple data representation types. Conformal prediction for…
Uncertainty-Calibrated Spatiotemporal Field Diffusion with Sparse Supervision
Kevin Valencia, Xihaier Luo, Shinjae Yoo +1
Physical fields are typically observed only at sparse, time-varying sensor locations, making forecasting and reconstruction ill-posed and uncertainty-critical. We present SOLID, a…
OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learning
Kevin Valencia, Thilina Balasooriya, Xihaier Luo +2
Multimodal spatiotemporal learning on real-world experimental data is constrained by two challenges: within-modality measurements are sparse, irregular, and noisy (QA/QC artifacts)…
Data Management System Analysis for Distributed Computing Workloads
Kuan-Chieh Hsu, Sairam Sri Vatsavai, Ozgur O. Kilic +20
Large-scale international collaborations such as ATLAS rely on globally distributed workflows and data management to process, move, and store vast volumes of data. ATLAS's Producti…
CGSim: A Simulation Framework for Large Scale Distributed Computing Environment
Sairam Sri Vatsavai, Raees Khan, Kuan-Chieh Hsu +20
Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new alg…
TPCpp-10M: Simulated proton-proton collisions in a Time Projection Chamber for AI Foundation Models
Shuhang Li, Yi Huang, David Park +10
Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field i…
Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, Tadashi Maeno, Fatih Furkan Akman +23
The collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. R…
FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics
David Park, Shuhang Li, Yi Huang +9
Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the developme…
Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures
Ozgur O. Kilic, David K. Park, Yihui Ren +18
Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. Thes…
Maximal Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators
Shanda Li, Shinjae Yoo, Yiming Yang
Fourier Neural Operators (FNOs) offer a principled approach for solving complex partial differential equations (PDEs). However, scaling them to handle more complex PDEs requires in…
STACI: Spatio-Temporal Aleatoric Conformal Inference
Brandon R. Feng, David Keetae Park, Xihaier Luo +3
Fitting Gaussian Processes (GPs) provides interpretable aleatoric uncertainty quantification for estimation of spatio-temporal fields. Spatio-temporal deep learning models, while s…
SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields
David Keetae Park, Xihaier Luo, Guang Zhao +3
Spatiotemporal learning is challenging due to the intricate interplay between spatial and temporal dependencies, the high dimensionality of the data, and scalability constraints. T…
Generalizable Implicit Neural Representations via Parameterized Latent Dynamics for Baroclinic Ocean Forecasting
Guang Zhao, Xihaier Luo, Seungjun Lee +7
Mesoscale ocean dynamics play a critical role in climate systems, governing heat transport, hurricane genesis, and drought patterns. However, simulating these processes at high res…
GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
Miruna Oprescu, David K. Park, Xihaier Luo +2
Estimating causal effects from spatiotemporal observational data is essential in public health, environmental science, and policy evaluation, where randomized experiments are often…
Alternative Mixed Integer Linear Programming Optimization for Joint Job Scheduling and Data Allocation in Grid Computing
Shengyu Feng, Jaehyung Kim, Yiming Yang +18
This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as…
Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events
Ayush Khot, Xihaier Luo, Ai Kagawa +1
Uncertainty quantification (UQ) methods play an important role in reducing errors in weather forecasting. Conventional approaches in UQ for weather forecasting rely on generating a…
Variable Rate Neural Compression for Sparse Detector Data
Yi Huang, Yeonju Go, Jin Huang +9
High-energy large-scale particle colliders generate data at extraordinary rates. Developing real-time high-throughput data compression algorithms to reduce data volume and meet the…
AI Surrogate Model for Distributed Computing Workloads
David K. Park, Yihui Ren, Ozgur O. Kilic +18
Large-scale international scientific collaborations, such as ATLAS, Belle II, CMS, and DUNE, generate vast volumes of data. These experiments necessitate substantial computational…
Automated and Holistic Co-design of Neural Networks and ASICs for Enabling In-Pixel Intelligence
Shubha R. Kharel, Prashansa Mukim, Piotr Maj +4
Extreme edge-AI systems, such as those in readout ASICs for radiation detection, must operate under stringent hardware constraints such as micron-level dimensions, sub-milliwatt po…
Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction
Wei Xu, Derek Freeman DeSantis, Xihaier Luo +5
Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines. In a recent work, we present…