Papers
1
Total Citations
25
H-Index
1
About
Ritam Raha is a rising researcher in formal methods and artificial intelligence, whose work bridges the gap between logical specification and machine learning. His primary research focuses on learning interpretable temporal logic formulas from data—a critical challenge in program verification, robotics motion planning, and process mining. Raha’s most cited paper, “Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic” (2022, 25 citations), introduces novel algorithms that efficiently infer fragments of Linear Temporal Logic (LTL) from finite traces. This work addresses a fundamental bottleneck: making logic learning scalable and practical for real-world systems. By developing anytime algorithms that can return high-quality results even when interrupted, Raha enables the automatic synthesis of human-readable specifications from observed behavior—a key step toward trustworthy AI. His contributions are particularly notable for their focus on the $$\mathbf{U}$$ (until) operator fragment, which captures essential temporal dependencies. With a growing citation impact, Raha is establishing himself as a leading voice in combining logical rigor with data-driven learning, offering powerful tools for verification, robotics, and beyond.
Research Focus
Key Achievements
Top Papers
- 1Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic25 citations · 2022