35 citations · 66 across the 33 of their papers we have counts for
35 papers
Neural Residual Modeling for Scientific Data Compression under Guaranteed Error Bounds
Surya Majumder, Liangji Zhu, Sanjay Ranka +1
Lossy compression of scientific simulation data increasingly relies on learned, latent-space architectures such as Residual Vector Quantization (RVQ), which iteratively quantize a…
Multivariate Scientific Data Compression with Learned Cross-Variable Latent Decorrelation and Autoregressive Entropy Modeling
Liangji Zhu, Anand Rangarajan, Sanjay Ranka
Scientific simulations generate collections of physical fields with heterogeneous statistics and dependencies, yet learned compressors often encode those fields independently or re…
QoI-Aware Provisional Rollout and Retrospective Reconciliation for Reduced-State Scientific Twins
Liangji Zhu, Scott Klasky, Jaemoon Lee +3
Scientific twins may need to continue operating when updates from an authoritative primary system are temporarily unavailable. Once synchronization resumes, the new boundary can al…
PhaseShift: Topology-Aware Data Harmonization and Model Consolidation Across Signalized Intersections
Yash Ranjan, Artur Kumik, Rahul Sengupta +2
Learned traffic-behavior models are commonly trained separately for each intersection, creating model portfolios that cannot share evidence across sites. We present PhaseShift, a t…
A Generative Model for Closed-Loop Microsimulation of Signalized Intersections
Yash Ranjan, Rahul Sengupta, Anand Rangarajan +1
Traffic microsimulators rely on hand-crafted behavior models that reproduce aggregate flow but miss the heterogeneous interactions between vehicles at signalized intersections. Lea…
Residual Modeling for High-Fidelity Learned Compression of Scientific Data
Liangji Zhu, Sanjay Ranka, Anand Rangarajan
Lossy compression is essential for massive spatiotemporal data from scientific simulations. Learned compressors can achieve high compression ratios at moderate accuracy targets, bu…
Enactor: From Traffic Simulators to Surrogate World Models
Yash Ranjan, Rahul Sengupta, Anand Rangarajan +1
Traffic microsimulators are widely used to evaluate road network performance under various ``what-if" conditions. However, the behavior models controlling the actions of the actors…
BigSUMO: A Scalable Framework for Big Data Traffic Analytics and Parallel Simulation
Rahul Sengupta, Nooshin Yousefzadeh, Manav Sanghvi +7
With growing urbanization worldwide, efficient management of traffic infrastructure is critical for transportation agencies and city planners. It is essential to have tools that he…
Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics
Yash Ranjan, Rahul Sengupta, Anand Rangarajan +1
Traffic Intersections are vital to urban road networks as they regulate the movement of people and goods. However, they are regions of conflicting trajectories and are prone to acc…
IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections
Yash Ranjan, Rahul Sengupta, Anand Rangarajan +1
Traffic simulators are widely used to study the operational efficiency of road infrastructure, but their rule-based approach limits their ability to mimic real-world driving behavi…
Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression
Jaemoon Lee, Xiao Li, Liangji Zhu +2
This paper proposes a new compression paradigm -- Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) -- for lossy scientific data compression. The framework is based o…
A General Framework for Error-controlled Unstructured Scientific Data Compression
Qian Gong, Zhe Wang, Viktor Reshniak +10
Data compression plays a key role in reducing storage and I/O costs. Traditional lossy methods primarily target data on rectilinear grids and cannot leverage the spatial coherence…
Foundation Model for Lossy Compression of Spatiotemporal Scientific Data
Xiao Li, Jaemoon Lee, Anand Rangarajan +1
We present a foundation model (FM) for lossy scientific data compression, combining a variational autoencoder (VAE) with a hyper-prior structure and a super-resolution (SR) module.…
Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds
Xiao Li, Jaemoon Lee, Anand Rangarajan +1
Scientific applications in fields such as high energy physics, computational fluid dynamics, and climate science generate vast amounts of data at high velocities. This exponential…
Video-based Pedestrian and Vehicle Traffic Analysis During Football Games
Jacques P. Fleischer, Ryan Pallack, Ahan Mishra +7
This paper utilizes video analytics to study pedestrian and vehicle traffic behavior, focusing on analyzing traffic patterns during football gamedays. The University of Florida (UF…
Graph Attention Network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation
Nooshin Yousefzadeh, Rahul Sengupta, Yashaswi Karnati +2
Traffic congestion has significant economic, environmental, and social ramifications. Intersection traffic flow dynamics are influenced by numerous factors. While microscopic traff…
MTDT: A Multi-Task Deep Learning Digital Twin
Nooshin Yousefzadeh, Rahul Sengupta, Yashaswi Karnati +2
Traffic congestion has significant impacts on both the economy and the environment. Measures of Effectiveness (MOEs) have long been the standard for evaluating traffic intersection…
Machine Learning Techniques for Data Reduction of Climate Applications
Xiao Li, Qian Gong, Jaemoon Lee +3
Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals…
Machine Learning Techniques for Data Reduction of CFD Applications
Jaemoon Lee, Ki Sung Jung, Qian Gong +5
We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics…
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
Qian Gong, Jieyang Chen, Ben Whitney +13
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capa…
A Spatiotemporal Correspondence Approach to Unsupervised LiDAR Segmentation with Traffic Applications
Xiao Li, Pan He, Aotian Wu +2
We address the problem of unsupervised semantic segmentation of outdoor LiDAR point clouds in diverse traffic scenarios. The key idea is to leverage the spatiotemporal nature of a…
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation
Aotian Wu, Pan He, Xiao Li +3
Most existing perception systems rely on sensory data acquired from cameras, which perform poorly in low light and adverse weather conditions. To resolve this limitation, we have w…
Scalable Hybrid Learning Techniques for Scientific Data Compression
Tania Banerjee, Jong Choi, Jaemoon Lee +5
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific…
Expressing linear equality constraints in feedforward neural networks
Anand Rangarajan, Pan He, Jaemoon Lee +2
We seek to impose linear, equality constraints in feedforward neural networks. As top layer predictors are usually nonlinear, this is a difficult task if we seek to deploy standard…