About

Anand Rangarajan, Ph.D., is a faculty member in the Department of Computer & Information Science & Engineering. His research interests include Machine Learning, Computer Vision, and Medical Image Analysis. He is affiliated with the fields of AI/Machine Learning, Computer Vision, and Medical Image Computing. Dr. Rangarajan earned his Ph.D. from the University of Southern California in 1991. He is involved in research related to computer vision and medical image computing, contributing to advancements in these areas through his academic work.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Computer vision
• Data Mining
• Mathematics
• Geography
• Computer network
• Real-time computing
• Automotive engineering
• Engineering

Selected publications

• BigSUMO: A Scalable Framework for Big Data Traffic Analytics and Parallel Simulation

  arXiv (Cornell University) · 2026-01-05

  preprintOpen access

  With growing urbanization worldwide, efficient management of traffic infrastructure is critical for transportation agencies and city planners. It is essential to have tools that help analyze large volumes of stored traffic data and make effective interventions. To address this need, we present ``BigSUMO", an end-to-end, scalable, open-source framework for analytics, interruption detection, and parallel traffic simulation. Our system ingests high-resolution loop detector and signal state data, along with sparse probe trajectory data. It first performs descriptive analytics and detects potential interruptions. It then uses the SUMO microsimulator for prescriptive analytics, testing hundreds of what-if scenarios to optimize traffic performance. The modular design allows integration of different algorithms for data processing and outlier detection. Built using open-source software and libraries, the pipeline is cost-effective, scalable, and easy to deploy. We hope BigSUMO will be a valuable aid in developing smart city mobility solutions.

  PublisherDOI

• Enactor: From Traffic Simulators to Surrogate World Models

  2026-01-01

  articleOpen access
  PublisherDOI

• BigSUMO: A Scalable Framework for Big Data Traffic Analytics and Parallel Simulation

  ArXiv.org · 2026-01-05

  articleOpen access

  With growing urbanization worldwide, efficient management of traffic infrastructure is critical for transportation agencies and city planners. It is essential to have tools that help analyze large volumes of stored traffic data and make effective interventions. To address this need, we present ``BigSUMO", an end-to-end, scalable, open-source framework for analytics, interruption detection, and parallel traffic simulation. Our system ingests high-resolution loop detector and signal state data, along with sparse probe trajectory data. It first performs descriptive analytics and detects potential interruptions. It then uses the SUMO microsimulator for prescriptive analytics, testing hundreds of what-if scenarios to optimize traffic performance. The modular design allows integration of different algorithms for data processing and outlier detection. Built using open-source software and libraries, the pipeline is cost-effective, scalable, and easy to deploy. We hope BigSUMO will be a valuable aid in developing smart city mobility solutions.

  PublisherOA PDF

• Enactor: From Traffic Simulators to Surrogate World Models

  ArXiv.org · 2026-03-18

  articleOpen access

  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 are overly simplistic and fails to capture realistic actor-actor interactions. Deep learning-based methods have been applied to model vehicles and pedestrians as ``agents" responding to their surrounding ``environment" (including lanes, signals, and neighboring agents). Although effective in learning actor-actor interaction, these approaches fail to generate physically consistent trajectories over long time periods, and they do not explicitly address the complex dynamics that arise at traffic intersections which is a critical location in urban networks. Inspired by the World Model paradigm, we have developed an actor centric generative model using transformer-based architecture that is able to capture the actor-actor interaction, at the same time understanding the geometry to the traffic intersection to generate physically grounded trajectories that are based on learned behavior. Moreover, we test the model in a live ``simulation-in-the-loop" setting, where we generate the initial conditions of the actors using SUMO and then let the model control the dynamics of the actors. We let the simulation run for 40000 timesteps (4000 seconds), testing the performance of the model on long timerange and evaluating the trajectories on traffic engineering related metrics. Experimental results demonstrate that the proposed framework effectively captures complex actor-actor interactions and generates long-horizon, physically consistent trajectories, while requiring significantly fewer training samples than traditional agent-centric generative approaches. Our model is able to outperform the baseline in traffic related as well as aggregate metrics where our model beats the baseline by more than 10x on the KL-Divergence.

  PublisherOA PDF

• Enactor: From Traffic Simulators to Surrogate World Models

  arXiv (Cornell University) · 2026-03-18

  preprintOpen access

  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 are overly simplistic and fails to capture realistic actor-actor interactions. Deep learning-based methods have been applied to model vehicles and pedestrians as ``agents" responding to their surrounding ``environment" (including lanes, signals, and neighboring agents). Although effective in learning actor-actor interaction, these approaches fail to generate physically consistent trajectories over long time periods, and they do not explicitly address the complex dynamics that arise at traffic intersections which is a critical location in urban networks. Inspired by the World Model paradigm, we have developed an actor centric generative model using transformer-based architecture that is able to capture the actor-actor interaction, at the same time understanding the geometry to the traffic intersection to generate physically grounded trajectories that are based on learned behavior. Moreover, we test the model in a live ``simulation-in-the-loop" setting, where we generate the initial conditions of the actors using SUMO and then let the model control the dynamics of the actors. We let the simulation run for 40000 timesteps (4000 seconds), testing the performance of the model on long timerange and evaluating the trajectories on traffic engineering related metrics. Experimental results demonstrate that the proposed framework effectively captures complex actor-actor interactions and generates long-horizon, physically consistent trajectories, while requiring significantly fewer training samples than traditional agent-centric generative approaches. Our model is able to outperform the baseline in traffic related as well as aggregate metrics where our model beats the baseline by more than 10x on the KL-Divergence.

  PublisherDOI

• IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections

  ArXiv.org · 2025-06-10

  preprintOpen access

  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 behavior. Traffic intersections are critical components of the road infrastructure, both in terms of safety risk (nearly 28% of fatal crashes and 58% of nonfatal crashes happen at intersections) as well as the operational efficiency of a road corridor. This raises an important question: can we create a data-driven simulator that can mimic the macro- and micro-statistics of the driving behavior at a traffic intersection? Deep Generative Modeling-based trajectory prediction models provide a good starting point to model the complex dynamics of vehicles at an intersection. But they are not tested in a "live" micro-simulation scenario and are not evaluated on traffic engineering-related metrics. In this study, we propose traffic engineering-related metrics to evaluate generative trajectory prediction models and provide a simulation-in-the-loop pipeline to do so. We also provide a multi-headed self-attention-based trajectory prediction model that incorporates the signal information, which outperforms our previous models on the evaluation metrics.

  PublisherOA PDFDOI

• Stability-preserving Lossy Compression for Large-scale Partial Differential Equations

  2025-11-12 · 4 citations

  articleOpen access

  Checkpoint/Restart (C/R) strategies are vital for fault tolerance in PDE-based scientific simulations, yet traditional checkpointing incurs significant I/O overhead. Lossy compression offers a scalable solution by reducing checkpoint data size, but conventional methods often lack control over physical invariants (e.g., energy), leading to instability such as oscillations or divergence in Partial Differential Equations (PDE) systems. This paper introduces a stability-preserving compression approach tailored for PDE simulations by explicitly controlling kinetic and potential energy perturbations to ensure stable restarts. Extensive experiments conducted across diverse PDE configurations demonstrate that our method maintains numerical stability with minimal error magnification—even across multiple checkpoint-restart cycles—outperforming state-of-the-art lossy compressors. Parallel evaluations on the Frontier supercomputer show up to 8.4× improvement in checkpoint write performance and 6.3× in read performance, while maintaining relative L2 errors ∼ 2e-6 throughout continued simulation. These results provide practical guidance for balancing compression accuracy, stability, and computational efficiency in large-scale PDE applications.

  PublisherDOI

• Inttrajsim: Trajectory Prediction for Simulating Multi-Vehicle Driving at Signalized Intersections

  2025-11-18

  article

  Traffic simulators are widely used to study the operational efficiency of road infrastructure, but their rulebased approach limits their ability to mimic real-world driving behavior. Traffic intersections are critical components of the road infrastructure, both in terms of safety risk (nearly 28 % of fatal crashes and 58 % of nonfatal crashes happen at intersections) as well as the operational efficiency of a road corridor. This raises an important question: Can we create a data-driven simulator that can mimic the micro-statistics of the driving behavior at a traffic intersection? Deep Generative Modeling-based trajectory prediction models provide a good starting point to model the complex dynamics of vehicles at an intersection. But they are not tested in a “live” micro-simulation scenario and are not evaluated on traffic engineering-related metrics. In this study, we propose traffic engineering-related metrics to evaluate generative trajectory prediction models and provide a simulation-in-theloop pipeline to do so. We also provide a multi-headed self-attention-based trajectory prediction model that incorporates the signal information, which outperforms our previous models on the evaluation metrics.

  PublisherDOI

• Graph Attention Network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation

  IEEE Transactions on Intelligent Transportation Systems · 2025-03-10 · 5 citations

  article

  Traffic congestion poses significant economic, environmental, and social challenges. High-resolution loop detector data and signal state records from Automated Traffic Signal Performance Measures (ATSPM) offer new opportunities for traffic signal optimization at intersections. However, additional factors such as geometry, traffic volumes, Turning-Movement Counts (TMCs), and human driving behaviors complicate this task. Existing simulators (e.g., SUMO, Vissim) are computationally intensive, while machine learning models often lack lane-specific traffic flow estimation. To address these issues, we propose two computationally efficient Attentional Graph Auto-Encoder frameworks as “Digital Twins” for urban traffic intersections. Leveraging graph representations and Graph Attention Networks (GAT), our models capture lane-level traffic flow dynamics at entry and exit points while remaining agnostic to intersection topology and lane configurations. Trained on over 40,000 hours of realistic traffic simulations with affordable GPU parallelization, our framework produces fine-grained traffic flow time series. This output supports critical applications such as estimating Measures of Effectiveness (MOEs), scaling to urban freeway corridors, and integrating with signal optimization frameworks for improved traffic management.

  PublisherDOI

• Scalable Hybrid Learning Techniques for Scientific Data Compression

  IEEE Transactions on Parallel and Distributed Systems · 2025-10-28

  article

  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 discovery. Unlike image and video compression algorithms that limit errors to primary data (PD), scientists require compression techniques that accurately preserve derived quantities of interest (QoIs). This paper presents a physics-informed compression technique implemented as an end-to-end, scalable, GPU-based pipeline for data compression that addresses this requirement. Our hybrid compression technique combines machine learning techniques and standard compression methods. Specifically, we combine an autoencoder, an error-bounded lossy compressor to provide guarantees on raw data error, and a constraint satisfaction post-processing step to preserve the QoIs within a minimal error (generally less than floating point error). The effectiveness of the data compression pipeline is demonstrated by compressing nuclear fusion simulation data generated by a large-scale fusion code, XGC, which produces hundreds of terabytes of data in a single day. Our approach works within the ADIOS framework and results in compression by a factor of more than 150 while requiring only a few percent of the computational resources necessary for generating the data, making the overall approach highly effective for practical scenarios.

  PublisherDOI

Recent grants

• RI: EAGER: Complex Wave Formulations for Shape Analysis

  NSF · $117k · 2011–2013

• EAGER: Parallel Semi-supervised Machine Learning for Volumetric Datasets

  NSF · $100k · 2017–2019

• G&V: Medium: Collaborative Research: Large Data Visualization Using An Interactive Machine Learning Framework

  NSF · $303k · 2011–2015

Frequent coauthors

• Sanjay Ranka

  University of Florida

  140 shared

• Tania Banerjee

  82 shared

• Gene Gindi

  Stony Brook Medicine

  34 shared

• Yashaswi Karnati

  32 shared

• Rahul Sengupta

  University of Florida

  29 shared

• Pan He

  Auburn University

  29 shared

• Xiaohui Huang

  Southwest Jiaotong University

  28 shared

• Keke Zhai

  First Affiliated Hospital of Henan University

  28 shared

Education

• Ph.D., Electrical Engineering - Systems

  University of Southern California

  1990

• B.Tech, Electronics Engineering

  Indian Institute of Technology Madras

  1984