Sagi Lotan
Papers
1
Total Citations
2
H-Index
1
About
Sagi Lotan is a researcher whose work bridges signal processing and data compression, focusing on uncovering latent structures in sensor data. His key research areas include pattern recognition in time-series signals, lossy compression techniques, and the analysis of autonomous system behaviors. Lotan’s major contribution lies in demonstrating how lossy text compression algorithms can be repurposed to detect repeating patterns in noisy sensor streams—such as GPS trajectories from smartphones or movement data from robotic vacuum cleaners and quadcopters. This approach offers a computationally efficient method for identifying periodic behaviors (e.g., “home, work, home, work”) without requiring domain-specific models. While his most-cited paper, “Finding Patterns in Signals Using Lossy Text Compression” (2019), has garnered 2 citations, its conceptual novelty has influenced discussions on lightweight pattern mining for resource-constrained devices. Lotan’s work is particularly relevant for researchers in IoT, autonomous navigation, and ubiquitous computing, as it provides a foundation for extracting meaningful insights from raw, high-volume sensor data. His contributions highlight the untapped potential of cross-domain algorithmic reuse in signal analysis.
Research Focus
Key Achievements
Top Papers
- 1Finding Patterns in Signals Using Lossy Text Compression2 citations · 2019