Herbert Wertheim College of Engineering
Ranka Lab
Led by Sanjay Ranka · Computer and Information Science and Engineering
What we study
The Ranka Lab studies how to use machine learning and high-performance computing to solve problems in transportation, healthcare, and scientific data. They develop algorithms and software for real-time traffic monitoring, smart cities, and data compression.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Projects
- 3
- Papers
- 5
Current projects
Machine Learning for Scientific Data Compression
Using machine learning to reduce the size of scientific data while keeping it accurate.
Machine Learning based Algorithms and Software for Transportation and Smart Cities
Developing real-time traffic monitoring systems using video cameras and sensor data.
Show 1 more projectsShow fewer
Data Science for Health Care Applications
Uses machine learning and high-performance computing to analyze large healthcare datasets, with the goal of improving health outcomes.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- Graph Attention Network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation
2025 · IEEE Transactions on Intelligent Transportation Systems
Builds a computationally efficient graph auto-encoder to simulate intersection traffic using lane-wise and topology-invariant data.
- A Comprehensive Survey on Multi-Layer Graph Embedding Methods
2025 · Vietnam Journal of Computer Science
Classifies methods for embedding multi-layer graphs into three groups: algorithmic, machine learning, and deep learning approaches.
Show 3 more papersShow fewer
- CAESAR: A Unified Framework for Foundation and Generative Models for Efficient Compression of Scientific Data
2025 · Applied Sciences
Creates a framework using autoencoders and diffusion models to compress scientific data efficiently while maintaining quality.
- Scalable Hybrid Learning Techniques for Scientific Data Compression
2025 · IEEE Transactions on Parallel and Distributed Systems
Builds a GPU-based tool that combines machine learning and standard compression to preserve important data features in scientific datasets.
- An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation
2024 · IEEE Transactions on Intelligent Transportation Systems
Develops a semi-automated scheme to annotate LiDAR data for infrastructure systems, addressing gaps in datasets and annotation costs.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Sanjay Ranka hasn't set up this page yet. You can also look them up in the UF directory (search “Sanjay Ranka”).